Delineation of management units across broad spatial scales can help to visualize population structuring and identify conservation opportunities. Geographical information system (GIS) approaches can be useful for developing broad‐scale management units, especially when paired with field data that can validate the GIS‐based delineations. Genetic data can be useful for evaluating whether management units accurately represent population structuring. The Eastern Brook Trout Joint Venture, a regionwide collaborative group, delineated patch‐based management units for Brook Trout Salvelinus fontinalis by using GIS approaches to inform conservation strategies across the eastern United States. The objectives of this research were to (1) evaluate how well the patches predicted Brook Trout genetic structuring in Connecticut, USA; (2) modify the patches as needed to represent contemporary genetic structuring; and (3) identify catchment‐ and patch‐scale riverscape characteristics that predict genetic diversity. Patches with dams and high levels of upstream impervious surfaces (>3%) had increased intrapatch genetic structuring, which we incorporated into our revised patch delineation algorithm. Patch area and catchment area were the best predictors of genetic diversity, suggesting the importance of maintaining connectivity and incorporating patch‐scale processes into conservation actions. The modified patch layer could be used as the basis for Brook Trout management units to help predict population structuring in the absence of watershed‐scale genetic data, allowing opportunities for Brook Trout conservation to be identified.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10260","usgsCitation":"Nathan, L., Kanno, Y., Letcher, B., Welsh, A.B., Whiteley, A.R., and Vokoun, J., 2020, Evaluation of genetic structuring within GIS‐derived Brook Trout management units: Transactions of the American Fisheries Society, v. 149, no. 6, p. 681-694, https://doi.org/10.1002/tafs.10260.","productDescription":"14 p.","startPage":"681","endPage":"694","ipdsId":"IP-117802","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":382550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"149","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nathan, Lucas","contributorId":236997,"corporation":false,"usgs":false,"family":"Nathan","given":"Lucas","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":794912,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kanno, Y.","contributorId":214290,"corporation":false,"usgs":false,"family":"Kanno","given":"Y.","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":794913,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Letcher, Benjamin 0000-0003-0191-5678 bletcher@usgs.gov","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":169305,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin","email":"bletcher@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":794914,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Welsh, Amy B.","contributorId":192239,"corporation":false,"usgs":false,"family":"Welsh","given":"Amy","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":794915,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whiteley, Andrew R.","contributorId":150155,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":794916,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vokoun, Jason C.","contributorId":236998,"corporation":false,"usgs":false,"family":"Vokoun","given":"Jason C.","affiliations":[{"id":47587,"text":"University of CT","active":true,"usgs":false}],"preferred":false,"id":794917,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70212923,"text":"70212923 - 2020 - Seismicity induced by massive wastewater injection near Puerto Gaitán, Colombia","interactions":[],"lastModifiedDate":"2020-09-02T13:31:05.404093","indexId":"70212923","displayToPublicDate":"2020-08-06T08:13:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Seismicity induced by massive wastewater injection near Puerto Gaitán, Colombia","docAbstract":"Seven years after the beginning of a massive wastewater injection project in eastern Colombia, local earthquake activity increased significantly. The field operator and the Colombian Geological Survey immediately reinforced the monitoring of the area. Our analysis of the temporal evolution of the seismic and injection data together with our knowledge of the geological parameters of the region indicate that the surge of seismicity is being induced by the re-injection of produced water into the same three producing reservoirs. Earthquake activity began on known faults once disposal rates had reached a threshold of ∼2 × 106 m3 of water per month. The average reservoir pressure had remained constant at 7.6 MPa after several years of production, sustained by a large, active aquifer. Surface injection pressures in the seismically active areas remain below 8.3 MPa, a value large enough to activate some of the faults. Since faults are mapped throughout the region and many do not have seismicity on them, we conclude that the existence of known faults is not the only control on whether earthquakes are generated. Stress conditions of these faults are open to future studies. Earthquakes are primarily found in four clusters, located near faults mapped by the operator. The hypocentres reveal vertical planes with orientations consistent with focal mechanisms of these events. Stress inversion of the focal mechanisms gives a maximum compression in the direction ENE-WSW, which is in agreement with borehole breakout measurements. Since the focal mechanisms of the earthquakes are consistent with the tectonic stress regime, we can conclude that the seismicity is resulting from the activation of critically stressed faults. Slip was progressive and seismic activity reached a peak before declining to few events per month. The decline in seismicity suggests that most of the stress has been relieved on the main faults. The magnitude of a large majority of the recorded earthquakes was lower than 4, as the pore pressure disturbance did not reach the mapped large faults whose activation might have resulted in larger magnitude earthquakes. Our study shows that a good knowledge of the local fault network and conditions of stress is of paramount importance when planning a massive water disposal program. These earthquakes indicate that while faults provide an opportunity to dispose produced water at an economically attractive volume–pressure ratio, the possibility of induced seismicity must also be considered.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/gji/ggaa326","usgsCitation":"Molina, I., Velasquez, J., Rubinstein, J., Garcia-Aristizabal, A., and Dionicio, V., 2020, Seismicity induced by massive wastewater injection near Puerto Gaitán, Colombia: Geophysical Journal International, v. 223, no. 2, p. 777-791, https://doi.org/10.1093/gji/ggaa326.","productDescription":"25 p.","startPage":"777","endPage":"791","ipdsId":"IP-094755","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":455745,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggaa326","text":"Publisher Index Page"},{"id":378094,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Colombia","city":"Puerto Gaitan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.2186279296875,\n 3.436658158559092\n ],\n [\n -71.1199951171875,\n 3.436658158559092\n ],\n [\n -71.1199951171875,\n 4.469641844538532\n ],\n [\n -72.2186279296875,\n 4.469641844538532\n ],\n [\n -72.2186279296875,\n 3.436658158559092\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"223","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Molina, I","contributorId":239740,"corporation":false,"usgs":false,"family":"Molina","given":"I","email":"","affiliations":[{"id":47998,"text":"Servicio Geologico Colombiano","active":true,"usgs":false}],"preferred":false,"id":797819,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Velasquez, J.S.","contributorId":239741,"corporation":false,"usgs":false,"family":"Velasquez","given":"J.S.","email":"","affiliations":[{"id":47998,"text":"Servicio Geologico Colombiano","active":true,"usgs":false}],"preferred":false,"id":797820,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rubinstein, Justin 0000-0003-1274-6785","orcid":"https://orcid.org/0000-0003-1274-6785","contributorId":215341,"corporation":false,"usgs":true,"family":"Rubinstein","given":"Justin","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797821,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garcia-Aristizabal, A","contributorId":239742,"corporation":false,"usgs":false,"family":"Garcia-Aristizabal","given":"A","email":"","affiliations":[{"id":39118,"text":"Istituto Nazionale di Geofisica e Vulcanologia","active":true,"usgs":false}],"preferred":false,"id":797822,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dionicio, V","contributorId":239744,"corporation":false,"usgs":false,"family":"Dionicio","given":"V","email":"","affiliations":[{"id":47998,"text":"Servicio Geologico Colombiano","active":true,"usgs":false}],"preferred":false,"id":797824,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211604,"text":"ds1129 - 2020 - Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2019","interactions":[],"lastModifiedDate":"2020-08-04T21:34:42.312972","indexId":"ds1129","displayToPublicDate":"2020-08-04T14:33:20","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1129","displayTitle":"Water-Level Data for the Albuquerque Basin and Adjacent Areas, Central New Mexico, Period of Record Through September 30, 2019","title":"Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2019","docAbstract":"The Albuquerque Basin, located in central New Mexico, is about 100 miles long and 25–40 miles wide. The basin is hydrologically defined as the extent of consolidated and unconsolidated deposits of Tertiary and Quaternary age that encompasses the structural Rio Grande Rift between San Acacia to the south and Cochiti Lake to the north. A 20-percent population increase in the basin from 1990 to 2000 and a 22-percent population increase from 2000 to 2010 resulted in an increased demand for water in areas within the basin. Drinking-water supplies throughout the basin were obtained solely from groundwater resources until December 2008, when the Albuquerque Bernalillo County Water Utility Authority (ABCWUA) began treatment and distribution of surface water from the Rio Grande through the San Juan-Chama Drinking Water Project.
An initial network of wells was established by the U.S. Geological Survey (USGS) in cooperation with the City of Albuquerque from April 1982 through September 1983 to monitor changes in groundwater levels throughout the Albuquerque Basin. In 1983, this network consisted of 6 wells with analog-to-digital recorders and 27 wells where water levels were measured monthly. As of 2019, the network consisted of 120 wells and piezometers. (A piezometer is a specialized well open to a specific depth in the aquifer, often of small diameter and nested with other piezometers screened at different depths.) The USGS, in cooperation with the ABCWUA, the New Mexico Office of the State Engineer, and Bernalillo County, measures water levels from the 120 wells and piezometers in the network; this report, prepared in cooperation with the ABCWUA, presents water-level data collected by USGS personnel at those 120 sites through water year 2019 (October 1, 2018, through September 30, 2019). Water levels that were collected from those discontinued wells in previous water years were published in previous USGS reports.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1129","collaboration":"Prepared in cooperation with the Albuquerque Bernalillo County Water Utility Authority","usgsCitation":"Beman, J.E., 2020, Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2019: U.S. Geological Survey Data Series 1129, 40 p., https://doi.org/10.3133/ds1129.","productDescription":"iii, 40 p.","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-120239","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":377000,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1129/coverthb.jpg"},{"id":377001,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1129/ds1129.pdf","text":"Report","size":"5.67 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1129"}],"country":"United States","state":"New Mexico","city":"Albuquerque","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.611083984375,\n 33.797408767572485\n ],\n [\n -105.941162109375,\n 33.797408767572485\n ],\n [\n -105.941162109375,\n 36.06686213257888\n ],\n [\n -107.611083984375,\n 36.06686213257888\n ],\n [\n -107.611083984375,\n 33.797408767572485\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
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Methane hydrate was synthesized from pure water ice and flash frozen seawater, with varying amounts of sand or silt added. Electrical conductivity was determined by impedance spectroscopy, using equivalent circuit modeling to separate the effects of electrodes and to gain insight into conduction mechanisms. Silt and sand increase the conductivity of pure hydrate, we infer by contaminant NaCl contributing to conduction in hydrate, to values in agreement with resistivities observed in well logs through hydrate The addition of silt and sand lowers the conductivity of hydrate synthesized from seawater by an amount consistent with Archie's Law. All samples were characterized using cryogenic scanning electron microscopy and energy dispersive spectroscopy, which show good connectivity of salt and brine phases. Electrical conductivity measurements of pure hydrate and hydrate mixed with silt during pressure‐induced dissociation supports previous conclusions that sediment increases dissociation rate.
There is a growing body of tools available for science support for determining the fate and behavior of industrial and agricultural chemicals that are rapidly injected (“spilled”) into aquatic environments. A 2-day roundtable-style workshop was held by the U.S. Geological Survey (USGS) in Middleton, Wisconsin, in December 2017 to describe and explore existing Federal science support for spill fate and behavior tools used for inland spills, ongoing and new fate and behavior studies, and science gaps in planning and response tools as part of the USGS Midcontinent Region’s efforts to include spill response as part of its strategic plans. A total of 28 attendees representing a variety of Federal, State, and regional entities presented on programs and tools used in various aspects of spill response. Most programs and tools discussed were for spills in riverine environments but tools and applications for spills in lakes, on land surfaces, in urban storm sewer networks, and groundwater also were discussed. A primary workshop focus was to facilitate communication and increase potential for future collaboration among agencies for inland spill science support. The role and need for more USGS science support within the inland spill community was discussed. Enhanced communication is needed within the USGS and the U.S. Department of the Interior science programs, as well as within and among other agencies that do emergency planning and response. A main conclusion of the workshop was that there are untapped resources of the USGS outlined in the agency’s science strategy that could strengthen science support for fate and behavior tools in inland areas, especially in the Upper Mississippi River, Ohio River, and Great Lakes Basins where large freshwater resources overlap with dense corridors of oil and hazardous substances, with transportation networks, and with large populations centers.
Fate and behavior tools are being developed quickly for inland spill response by multiple Federal agencies in partnership with local and regional entities. Applicability of these tools ranges from planning and preparedness, to the early stages of spill response for protection of human life and property, and to the application of monitoring and models to assess the long-term consequences of spills. Key findings from the workshop, with an emphasis on potential further development of USGS science support, include the following:
•The national and regional response to spills occurs within an established system that must be respected by all parties involved in spill response. The USGS’s role is to support spill responders who are physically working at a spill scene, deploying booms and using other efforts to contain and recover spilled materials.
•The USGS has tools that have been used throughout spill response operations, from early response to recovery and restoration. Developing a more formal role for the USGS to participate in science support for inland spills on a consistent basis is a desired outcome. This will require the USGS to improve internal and external communication and would be best accomplished by assigning one or more coordinator positions within the agency to plan and oversee USGS spill-response efforts. More involvement of the USGS on National and Regional Response Teams, especially in the realm of the Science and Technology Subcommittees, will gofar in increasing external communication and integration of fate and behavior tools.
•Rapid response to spills requires modeling and mapping of plumes and associated time-of-travel estimation for a range of stream sizes across the United States. Many existing models use USGS streamgage data and the USGS National Hydrography Dataset. Nearly all existing models would benefit from updated linkages to USGS StreamStats and its soon-to-be released time-of-travel estimates,real-time velocity, stream morphology, and slope data. Integrating USGS tools with those from other agencies could be done to better serve the larger spill response community.
• A problem is that existing models to rapidly predict plume extent, as well as more followup/longer-term fate and transport models, can be unknown or unavailable to spill responders. Thus, creating and strengthening linkages among USGS scientists skilled at using these tools is needed to support spill response with the on-scene responders.
• Research for inland spill fate and behavior done outside of an immediate spill response can assist with spill planning and preparedness by (1) revealing sites likely to experience spills in the future (high-risk sites) and (2) understanding how a spilled substance might behave under a range of environmental conditions. However, USGS research on this topic has been scarce and subject to funding availability. Examples include the 2010 Line 6B Spill release into the Kalamazoo River in Michigan, where the USGS provided science support for a variety of fate and behavior tools for stream and impoundment environments. A long-term research site in Bemidji, Minnesota, provides important insights into transformations and longevity of spilled oil in groundwater and groundwater-surface water interactions.
• Linking stream models to other components of this inland environment, including groundwater, overland flow, and karst, is needed. Stream network data can be linked to underground conduits such as storm sewers and karst groundwater systems. Stream models can also be linked with geospatial data such as that contained in U.S. Environmental Protection Agency’s
interactive mapping tools.
• The USGS is uniquely qualified to collect water-quality data during spills in the United States because of its many geographically dispersed water science centers, its knowledge and preparedness for flood measurement and documentation, and its cadre of skilled water-quality employees. Rapid-deployment gages, used for floods, could also be used for spills if they included spill-specific sensors. Coordinated expertise at USGS water and environmental science centers can be used for monitoring spill effects and for assessing risk to water quality and ecological communities.
• Scientists at the USGS have proven capable of providing science coordination and technical assistance within the Incident Command Structure at the request of the lead on-scene coordinator. This external coordination, as well as internal communication within USGS Water, Hazards, and Ecosystems Mission Areas, could be improved by establishing and naming a USGS spills coordinator. Scott Morlock, Jo Ellen Hinck, and Faith Fitzpatrick are currently (2017) serving in informal coordination roles in addition to their traditional duties.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201063","usgsCitation":"Sullivan, D.J., and Fitzpatrick, F.A., 2020, Fate and behavior tools related to inland spill response—Workshop on the U.S. Geological Survey’s role in Federal science support: U.S. Geological Survey Open-File Report 2020–1063, 22 p., https://doi.org/10.3133/ofr20201063.","productDescription":"v, 22 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-111089","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":376920,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1063/ofr20201063.pdf","text":"Report","size":"8.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1063"},{"id":376919,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1063/coverthb.jpg"}],"contact":"Director, Upper Midwest Water Science Center
U.S Geological Survey
8505 Research Way
Middleton, WI 53562
Information concerning the availability, use, and quality of water in Evangeline Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, about 282.66 million gallons per day (Mgal/d) of water were withdrawn in Evangeline Parish, including about 122.05 Mgal/d from groundwater sources and 160.61 Mgal/d from surface-water sources. Withdrawals for agricultural use, composed of aquaculture, general irrigation, livestock, and rice irrigation, accounted for 45 percent (126.86 Mgal/d) of the total water withdrawn. Withdrawals for power-generation use accounted for about 52 percent (146.33 Mgal/d) of the total water withdrawn. Other categories of use included public supply, industry, and rural domestic. Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 1980.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203020","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"Murphy, C.J., and White, V.E., 2020, Water resources of Evangeline Parish, Louisiana: U.S. Geological Survey Fact Sheet 2020–3020, 6 p., https://doi.org/10.3133/fs20203020.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-103346","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":376884,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 2014–2015"},{"id":376882,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3020/coverthb.jpg"},{"id":376883,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3020/fs20203020.pdf","text":"Report","size":"1.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3020"}],"country":"United States","state":"Louisiana","county":"Evangeline Parish","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-92.2809,30.9653],[-92.2811,30.9365],[-92.2381,30.8924],[-92.2377,30.8486],[-92.2132,30.8487],[-92.2127,30.7948],[-92.2079,30.7889],[-92.2073,30.7848],[-92.1977,30.7798],[-92.1918,30.7785],[-92.187,30.7758],[-92.1816,30.7694],[-92.1774,30.7685],[-92.1694,30.7677],[-92.1729,30.6758],[-92.175,30.6762],[-92.1797,30.6661],[-92.1861,30.667],[-92.1887,30.6647],[-92.1945,30.6596],[-92.2009,30.6564],[-92.2008,30.6477],[-92.205,30.639],[-92.2034,30.6372],[-92.2055,30.6353],[-92.2044,30.6331],[-92.206,30.6299],[-92.2038,30.6257],[-92.2064,30.6216],[-92.2107,30.6198],[-92.2117,30.6129],[-92.2113,30.569],[-92.2622,30.5682],[-92.263,30.5385],[-92.2795,30.5388],[-92.4148,30.5405],[-92.4227,30.5386],[-92.4285,30.5363],[-92.4397,30.5362],[-92.4508,30.532],[-92.4592,30.5246],[-92.4622,30.5163],[-92.4659,30.5108],[-92.4637,30.5008],[-92.4657,30.4967],[-92.471,30.4939],[-92.4805,30.4924],[-92.4874,30.4878],[-92.4942,30.4818],[-92.6304,30.4827],[-92.6305,30.4859],[-92.6284,30.4896],[-92.6237,30.4929],[-92.6232,30.4974],[-92.618,30.5021],[-92.6165,30.5067],[-92.6176,30.5135],[-92.6246,30.5185],[-92.6241,30.5208],[-92.6162,30.5259],[-92.6051,30.531],[-92.6,30.5434],[-92.5958,30.5457],[-92.5948,30.5517],[-92.5932,30.5554],[-92.588,30.5559],[-92.5859,30.5618],[-92.5844,30.5683],[-92.5871,30.5719],[-92.5903,30.5732],[-92.593,30.5796],[-92.5979,30.5832],[-92.5986,30.8726],[-92.5989,30.8945],[-92.5658,30.8948],[-92.5605,30.899],[-92.5537,30.9031],[-92.5484,30.9032],[-92.5451,30.9009],[-92.5366,30.8978],[-92.5275,30.8997],[-92.5253,30.8943],[-92.5142,30.8953],[-92.4961,30.9037],[-92.484,30.9138],[-92.4798,30.9226],[-92.4861,30.9536],[-92.484,30.9559],[-92.4728,30.9587],[-92.4664,30.9574],[-92.4568,30.9589],[-92.451,30.9626],[-92.4404,30.9686],[-92.4271,30.9733],[-92.4154,30.9788],[-92.4123,30.9853],[-92.405,30.994],[-92.3948,30.9968],[-92.3869,31.0033],[-92.3784,31.0029],[-92.3746,30.9974],[-92.3676,30.9916],[-92.3606,30.9925],[-92.3601,30.9898],[-92.3611,30.988],[-92.3579,30.9848],[-92.3419,30.9817],[-92.3408,30.9794],[-92.3418,30.9758],[-92.345,30.9739],[-92.3439,30.9703],[-92.3316,30.9736],[-92.3252,30.9704],[-92.331,30.9635],[-92.3171,30.9636],[-92.315,30.9655],[-92.2809,30.9653]]]},\"properties\":{\"name\":\"Evangeline\",\"state\":\"LA\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
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3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
Information concerning the availability, use, and quality of water in Avoyelles Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, about 70 million gallons per day (Mgal/d) of water were withdrawn in Avoyelles Parish, including about 59.27 Mgal/d from groundwater sources and 10.95 Mgal/d from surface-water sources. Withdrawals for agricultural use—composed of aquaculture, general irrigation, livestock, and rice irrigation—accounted for 93 percent (65.59 Mgal/d) of the total water withdrawn. Other categories of use included public supply and rural domestic. Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 2014.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203011","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"White, V.E., 2020, Water resources of Avoyelles Parish, Louisiana: U.S. Geological Survey Fact Sheet 2020–3011, 6 p., https://doi.org/10.3133/fs20203011.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-102165","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":376881,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 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Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
Natural resource managers are coping with rapid changes in both environmental conditions and ecosystems. Enabled by recent advances in data collection and assimilation, short-term ecological forecasting may be a powerful tool to help resource managers anticipate impending near-term changes in ecosystem conditions or dynamics. Managers may use the information in forecasts to minimize the adverse effects of ecological stressors and optimize the effectiveness of management actions. To explore the potential for ecological forecasting to enhance natural resource management, the U.S. Geological Survey (USGS) convened a workshop titled \"Building Capacity for Applied Short-Term Ecological Forecasting\" on May 29—31, 2019, with participants from several Federal agencies, including the Bureau of Land Management, the U.S. Fish and Wildlife Service, the National Park Service, and the National Oceanic and Atmospheric Administration as well as all mission areas within the USGS.
Participants broadly agreed that short-term ecological forecasting—on the order of days to years into the future—has tremendous potential to improve the quality and timeliness of information available to guide resource management decisions. Participants considered how ecological forecasting could directly affect their agency missions and specified numerous critical tools for addressing natural resource management concerns in the 21st century that could be enhanced by ecological forecasting. Given this breadth of possible applications for forecast products, participants developed a repeatable framework for evaluating potential value of a forecast product for enhancing resource management. Applying that process to a large list of forecast ideas that were developed in a brainstorming session, participants identified a small set of promising forecast products that illustrate the value of ecological forecasting for informing resource management. Workshop outcomes also include insights about important likely obstacles and next steps. In particular, reliable production and delivery of operational ecological forecasts will require a sustained commitment by research agencies, in partnership with resource management agencies, to maintain and improve forecasting tools and capabilities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201073","usgsCitation":"Bradford, J.B., Weltzin, J.F., McCormick, M., Baron, J., Bowen, Z., Bristol, S., Carlisle, D., Crimmins, T., Cross, P., DeVivo, J., Dietze, M., Freeman, M., Goldberg, J., Hooten, M., Hsu, L., Jenni, K., Keisman, J., Kennen, J., Lee, K., Lesmes, D., Loftin, K., Miller, B.W., Murdoch, P., Newman, J., Prentice, K.L., Rangwala, I., Read, J., Sieracki, J., Sofaer, H., Thur, S., Toevs, G., Werner, F., White, C.L., White, T., and Wiltermuth, M., 2020, Ecological forecasting—21st century science for 21st century management: U.S. Geological Survey Open-File Report 2020–1073, 54 p., https://doi.org/10.3133/ofr20201073.","productDescription":"vii, 54 p.","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-114740","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":433,"text":"National Phenology Network","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":376787,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1073/ofr20201073.pdf","text":"Report","size":"598 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1073"},{"id":376786,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1073/coverthb.jpg"}],"contact":"Director, Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
The accurate mapping of streams and their streamflow conditions in terms of presence or absence of surface water is important to both understanding physical, chemical, and biological processes in streams and to managing land, water, and ecological resources. This document describes a field form, FLOwPER (FLOw PERmanence), available within a mobile application (app), for standardized data collection of the presence or absence of surface flow in streams. The FLOwPER Database is a publicly available geodataset that can be used for research and management applications. This document provides instructions on how to (1) access and download the FLOwPER field form within the mobile app service, (2) use and complete a FLOwPER field form, and (3) view and download data from the FLOwPER Database.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201075","collaboration":"Prepared in cooperation with the United States Forest Service and the Bureau of Land Management","usgsCitation":"Jaeger, K.L., Burnett, J., Heaston, E.D., Wondzell, S.M., Chelgren, N., Dunham, J.B., Johnson, S., and Brown, M., 2020, FLOwPER user guide—For collection of FLOw PERmanence field observations: U.S. Geological Survey Open-File Report 2020–1075, 40 p., https://doi.org/10.3133/ofr20201075.","productDescription":"Report: vi, 40 p.; Appendix","onlineOnly":"Y","ipdsId":"IP-118616","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":436839,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13WFKYW","text":"USGS data release","linkHelpText":"FLOwPER Database: StreamFLOw PERmanence field observations, Jan 2021 - Dec 2021"},{"id":407336,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5edea67582ce7e579c6e5845","text":"USGS data release","description":"USGS data release","linkHelpText":"FLOwPER Database: StreamFLOw PERmanence Field Observations"},{"id":376985,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1075/coverthb.jpg"},{"id":377862,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1075/ofr20201075_appendix01.pdf","text":"Appendix 1","size":"507 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1075 Appendix 1"},{"id":376986,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1075/ofr20201075.pdf","text":"Report","size":"5.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1075"}],"contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Temporal and spatial distribution of niclosamide in the water column and sediment were evaluated after the application of granular Bayluscide in six lentic sea lamprey (Petromyzon marinus) larval assessment plots. Water and sediment were collected 0.25, 1, 3, 5, and 7 hours after application and were analyzed for niclosamide, the active ingredient in granular Bayluscide. Water samples were collected from five heights in the water column (1 cm, 13 cm, 26 cm, 1/2 water column, and water surface) at five locations inside and four locations 10 m outside of each assessment plot. Sediment was collected from 18 locations within each plot. Niclosamide water concentrations inside and outside of the plots did not vary by depth but did vary between plots and by time. Niclosamide water concentrations also varied by sampler location outside of the plots. Following granular Bayluscide applications the mean niclosamide concentration in water for all levels, within the plots, decreased from 0.12 mgL-1 (SD = 0.12 mgL-1) at 15 minutes to 0.061 mgL-1 (SD = 0.040 mgL-1) at hour 1. The mean niclosamide concentration in the top 4 cm of sediment was 2.9 mgkg-1 (SD = 2.4 mgkg-1) 15 minutes after application and was 1.3 mgkg-1 (SD = 1.8 mgkg-1) at hour 7. Concentrations in all sediment samples ranged from < 0.001 to 30.730 mgkg-1 and varied between the six plots. Niclosamide concentrations measured in sediment samples were more than 1 order of magnitude greater than in the water and varied spatially by over 4 orders of magnitude.
","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Bernardy, J., Kaye, C., Schloesser, N., and Schueller, J., 2020, Distribution of niclosamide following granular Bayer applications in lentic environments: Project Completion Report, 30 p.","productDescription":"30 p.","ipdsId":"IP-107424","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":399095,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":377968,"type":{"id":15,"text":"Index Page"},"url":"https://www.glfc.org/"}],"country":"United States","state":"Michigan, Wisconsin","county":"Mackinac County, Marinette County","otherGeospatial":"Hog Island Creek, Lake Michigan, Peshtigo Harbor","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.66952514648438,\n 44.96832008904543\n ],\n [\n -87.64514923095703,\n 44.96832008904543\n ],\n [\n -87.64514923095703,\n 44.98568481264677\n ],\n [\n -87.66952514648438,\n 44.98568481264677\n ],\n [\n -87.66952514648438,\n 44.96832008904543\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.28724074363708,\n 46.07139217240364\n ],\n [\n -85.28464436531067,\n 46.07139217240364\n ],\n [\n -85.28464436531067,\n 46.07282125858186\n ],\n [\n -85.28724074363708,\n 46.07282125858186\n ],\n [\n -85.28724074363708,\n 46.07139217240364\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bernardy, Jeffry 0000-0001-7443-1995","orcid":"https://orcid.org/0000-0001-7443-1995","contributorId":213528,"corporation":false,"usgs":true,"family":"Bernardy","given":"Jeffry","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":797469,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kaye, Cheryl","contributorId":167292,"corporation":false,"usgs":false,"family":"Kaye","given":"Cheryl","affiliations":[{"id":6599,"text":"U.S. Fish and Wildlife Service, Marquette Biological Station","active":true,"usgs":false}],"preferred":false,"id":797470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schloesser, Nicholas 0000-0002-3815-5302","orcid":"https://orcid.org/0000-0002-3815-5302","contributorId":237025,"corporation":false,"usgs":true,"family":"Schloesser","given":"Nicholas","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":797471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schueller, Justin R. 0000-0002-7102-3889","orcid":"https://orcid.org/0000-0002-7102-3889","contributorId":213527,"corporation":false,"usgs":true,"family":"Schueller","given":"Justin","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":797472,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228560,"text":"70228560 - 2020 - Spatiotemporal variation in occurrence and co-occurrence of pesticides, hormones, and other organic contaminants in rivers in the Chesapeake Bay Watershed, United States","interactions":[],"lastModifiedDate":"2022-02-15T12:22:36.066283","indexId":"70228560","displayToPublicDate":"2020-08-01T09:59:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal variation in occurrence and co-occurrence of pesticides, hormones, and other organic contaminants in rivers in the Chesapeake Bay Watershed, United States","docAbstract":"Investigating the spatiotemporal dynamics of contaminants in surface water is crucial to better understand how introduced chemicals are interacting with and potentially influencing aquatic organisms and environments. Within the Chesapeake Bay Watershed, USA, there are concerns about the potential role of contaminant exposure on fish health. Evidence suggests that exposure to contaminants in surface water is causing immunosuppression and intersex in freshwater fish species. Despite these concerns, there is a paucity of information regarding the complex dynamics of contaminant occurrence and co-occurrence in surface water across both space and time. To address these concerns, we applied a Bayesian hierarchical joint-contaminant model to describe the occurrence and co-occurrence patterns of 28 contaminants and total estrogenicity across six river sites and over three years. We found that seasonal occurrence patterns varied by contaminant, with the highest occurrence probabilities during the spring and summer months. Additionally, we found that the proportion of agricultural landcover in the immediate catchment, as well as stream discharge, did not have a significant effect on the occurrence probabilities of most compounds. Four pesticides (atrazine, metolachlor, fipronil and simazine) co-occurred across sites after accounting for environmental covariates. These results provide baseline information on the contaminant occurrence patterns of several classes of compounds within the Chesapeake Bay Watershed. Understanding the spatiotemporal dynamics of contaminants in surface water is the first step in investigating the effects of contaminant exposure on fisheries and aquatic environments.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.138765","usgsCitation":"McClure, C.M., Smalling, K., Blazer, V.S., Sperry, A., Schall, M.K., Kolpin, D., Phillips, P.J., Hladik, M.L., and Wagner, T., 2020, Spatiotemporal variation in occurrence and co-occurrence of pesticides, hormones, and other organic contaminants in rivers in the Chesapeake Bay Watershed, United States: Science of the Total Environment, v. 728, p. 1-13, https://doi.org/10.1016/j.scitotenv.2020.138765.","productDescription":"138765, 13 p.","startPage":"1","endPage":"13","ipdsId":"IP-117478","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":365,"text":"Leetown Science 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Center","active":true,"usgs":true}],"preferred":true,"id":834601,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":221229,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":834602,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834595,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70212896,"text":"70212896 - 2020 - Genomes reveal genetic diversity of Piscine orthoreovirus in farmed and free-ranging salmonids from Canada and USA","interactions":[],"lastModifiedDate":"2020-10-28T15:59:41.808411","indexId":"70212896","displayToPublicDate":"2020-07-31T18:44:38","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5051,"text":"Virus Evolution","onlineIssn":"2057-1577","active":true,"publicationSubtype":{"id":10}},"title":"Genomes reveal genetic diversity of Piscine orthoreovirus in farmed and free-ranging salmonids from Canada and USA","docAbstract":"Piscine orthoreovirus (PRV-1) is a segmented RNA virus which is commonly found in salmonids in the Atlantic and Pacific Oceans. PRV-1 causes the Heart and Skeletal Muscle Inflammation (HSMI) disease in Atlantic salmon and is associated with several other disease conditions. Previous phylogenetic studies of genome segment 1 (S1) identified four main genogroups of PRV-1 (S1 genogroups I – IV). The goal of the present study was to use Bayesian phylogenetic inference to expand our understanding of the spatial, temporal and host patterns of PRV-1 from the waters of the northeast Pacific. To that end, we determined the coding genome sequences of 14 PRV-1 samples that were selected to improve our knowledge of genetic diversity across a broader temporal, geographic and host range, including the first reported genome sequences from the northwest Atlantic (Eastern Canada). Nucleotide and amino acid sequences of the concatenated genomes and their individual segments revealed that established sequences from the northeast Pacific were monophyletic in all analyses. Bayesian inference phylogenetic trees of S1 sequences using BEAST and MrBayes also found that sequences from the northeast Pacific grouped separately from sequences from other areas. One PRV-1 sample (WCAN_BC17_AS_2017) from an escaped Atlantic salmon, collected in British Columbia but derived from Icelandic broodstock, grouped with other S1 sequences from Iceland. Our concatenated genome and S1 analysis demonstrated that PRV-1 from the northeast Pacific is genetically distinct but descended from PRV-1 from the North Atlantic. However, the analyses were inconclusive as to the timing and exact source of introduction into the northeast Pacific, either from eastern North America or European waters of the North Atlantic. There was no evidence that PRV-1 was evolving differently between free-ranging Pacific Salmon and farmed Atlantic Salmon. The northeast Pacific PRV-1 sequences fall within genogroup II based on the classification of Garseth et al. (2013), which also includes North Atlantic sequences from Eastern Canada, Iceland and Norway. The additional full genome sequences herein strengthen our understanding of phylogeographical patterns related to the northeast Pacific, but a more balanced representation of full PRV-1 genomes from across its range, as well additional sequencing of archived samples, are still needed to better understand global relationships including potential transmission links among regions.
","language":"English","publisher":"Oxford Academic Journals","doi":"10.1093/ve/veaa054","usgsCitation":"Siah, A., Breyta, B.R., Warheit, K.I., Gagne, N., Purcell, M.K., Morrison, D.B., Powell, J.F., and Johnson, S., 2020, Genomes reveal genetic diversity of Piscine orthoreovirus in farmed and free-ranging salmonids from Canada and USA: Virus Evolution, v. 6, no. 2, veaa054, 15 p., https://doi.org/10.1093/ve/veaa054.","productDescription":"veaa054, 15 p.","ipdsId":"IP-118186","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":455811,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ve/veaa054","text":"Publisher Index Page"},{"id":378077,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Chile, Norway, United States","otherGeospatial":"Faroe Islands","volume":"6","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-07-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Siah, Ahmed","contributorId":149983,"corporation":false,"usgs":false,"family":"Siah","given":"Ahmed","email":"","affiliations":[{"id":17874,"text":"British Columbia Centre for Aquatic Health Sciences, BC Canada","active":true,"usgs":false}],"preferred":false,"id":797785,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Breyta, B. R.","contributorId":239729,"corporation":false,"usgs":false,"family":"Breyta","given":"B.","email":"","middleInitial":"R.","affiliations":[{"id":47991,"text":"University of Washington, School of Aquatic Fisheries Sciences, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":797786,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Warheit, K. I.","contributorId":239730,"corporation":false,"usgs":false,"family":"Warheit","given":"K.","email":"","middleInitial":"I.","affiliations":[{"id":47993,"text":"Washington Department of Fish and Wildlife, Olympia WA, USA","active":true,"usgs":false}],"preferred":false,"id":797787,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gagne, N","contributorId":239731,"corporation":false,"usgs":false,"family":"Gagne","given":"N","email":"","affiliations":[{"id":47994,"text":"Fisheries & Oceans Canada, Gulf Fisheries Center, Moncton, NB, Canada","active":true,"usgs":false}],"preferred":false,"id":797788,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Purcell, Maureen K. 0000-0003-0154-8433 mpurcell@usgs.gov","orcid":"https://orcid.org/0000-0003-0154-8433","contributorId":168475,"corporation":false,"usgs":true,"family":"Purcell","given":"Maureen","email":"mpurcell@usgs.gov","middleInitial":"K.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":797789,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Morrison, Diane B.","contributorId":149984,"corporation":false,"usgs":false,"family":"Morrison","given":"Diane","email":"","middleInitial":"B.","affiliations":[{"id":17875,"text":"Marine Harvest Canada, Campbell River, BC, Canada","active":true,"usgs":false}],"preferred":false,"id":797790,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Powell, J. F. F.","contributorId":239732,"corporation":false,"usgs":false,"family":"Powell","given":"J.","email":"","middleInitial":"F. F.","affiliations":[{"id":47996,"text":"British Columbia Centre for Aquatic Health Sciences, Campbell River BC, Canada","active":true,"usgs":false}],"preferred":false,"id":797791,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, S. C.","contributorId":239733,"corporation":false,"usgs":false,"family":"Johnson","given":"S. C.","affiliations":[{"id":47997,"text":"Fisheries & Oceans Canada, Nanaimo, British Columbia, Canada","active":true,"usgs":false}],"preferred":false,"id":797792,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70211518,"text":"sir20205069 - 2020 - Incipient bed-movement and flood-frequency analysis using hydrophones to estimate flushing flows on the upper Colorado River, Colorado, 2019","interactions":[],"lastModifiedDate":"2020-08-05T18:38:22.157905","indexId":"sir20205069","displayToPublicDate":"2020-07-31T18:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5069","displayTitle":"Incipient Bed-Movement and Flood-Frequency Analysis using Hydrophones to Estimate Flushing Flows on the Upper Colorado River, Colorado, 2019","title":"Incipient bed-movement and flood-frequency analysis using hydrophones to estimate flushing flows on the upper Colorado River, Colorado, 2019","docAbstract":"In 2019, the U.S. Geological Survey, in cooperation with the Upper Colorado River Wild and Scenic Stakeholder Group, studied the magnitude and recurrence interval of streamflow (discharge) needed to initiate bed movement of gravel-sized and finer sediment in a segment of the Colorado River in Colorado to better understand sediment movement and its relation to flow regimes of the river. The study area extended from the confluence of the Blue and Colorado Rivers near Kremmling, Colorado, downstream to the confluence of the Eagle and Colorado Rivers near Dotsero, Colo. Bed movement occurred more frequently and at lower streamflows from State Bridge to Catamount Bridge compared to the study area upstream from State Bridge. As a result, the flushing flow was characterized in the study area using two definitions: the “upstream flushing flow” for locations above State Bridge and the “downstream flushing flow” for locations below State Bridge.
Acoustic data from stationary hydrophones continuously deployed in the spring and summer of 2019 and longitudinal hydrophone acoustic profiles manually collected in summer 2019 were used to identify the streamflow needed for incipient gravel-bed movement and establish flushing flows defined for this study. The upstream flushing flow was defined as 3,000 cubic feet per second (ft3/s) at streamgage 09058000 Colorado River near Kremmling, Colo. (the Kremmling streamgage) based on the underwater acoustic data from the downstream location at the Radium stationary site (2,950 ft3/s at the Kremmling streamgage which was rounded to 3,000 ft3/s). The downstream flushing flow was defined as 2,400 ft3/s at the Kremmling streamgage or 3,100 ft3/s at streamgage 09060799 Colorado River at Catamount Bridge, Colo. (the Catamount Bridge streamgage) based on the more conservative streamflow associated with the flushing flow defined using underwater acoustic data from the downstream location at the above Catamount Bridge stationary site (2,310 ft3/s at the Kremmling streamgage which was rounded to 2,400 ft3/s and 3,040 ft3/s at the Catamount Bridge streamgage which was rounded to 3,100 ft3/s).
The annual series of peak-streamflow data at the Kremmling streamgage were used to estimate annual exceedance probability (AEP) streamflows to compare to the flushing flow. Results from the Denver Water Platte and Colorado Simulation Model were used to generate daily peak-streamflows for a future conditions scenario provided for this report. The upstream flushing flow of approximately 3,000 ft3/s at the Kremmling streamgage has an AEP near 0.50 (2-year return period) depending on the period of historical record and an AEP near 0.43 (2.33-year return period) for the future period. The downstream flushing flow of approximately 2,400 ft3/s at the Kremmling streamgage has an AEP near 0.67 (1.5-year return period) depending on the period of historical record and an AEP near 0.67 (1.5-year return period) for the future period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205069","collaboration":"Prepared in cooperation with the Upper Colorado River Wild and Scenic Stakeholder Group and the Colorado River Water Conservation District","usgsCitation":"Kohn, M.S., Marineau, M.D., Hempel, L.A., and McDonald, R.R., 2020, Incipient bed-movement and flood-frequency analysis using hydrophones to estimate flushing flows on the upper Colorado River, Colorado, 2019: U.S. Geological Survey Scientific Investigations Report 2020–5069, 39 p., https://doi.org/10.3133/sir20205069.","productDescription":"Report: viii, 39 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-114386","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":376916,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J5L78O","text":"USGS data release","linkHelpText":"Acoustic, Spatial, and Sediment Size Data 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Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS 415
Denver, CO 80225
The capture and use of water are critically important in drylands, which collectively constitute Earth's largest biome. Drylands will likely experience lower and more unreliable rainfall as climatic conditions change over the next century. Dryland soils support a rich community of microphytic organisms (biocrusts), which are critically important because they regulate the delivery and retention of water. Yet despite their hydrological significance, a global synthesis of their effects on hydrology is lacking. We synthesized 2,997 observations from 109 publications to explore how biocrusts affected five hydrological processes (times to ponding and runoff, early [sorptivity] and final [infiltration] stages of water flow into soil, and the rate or volume of runoff) and two hydrological outcomes (moisture storage, sediment production). We found that increasing biocrust cover reduced the time for water to pond on the surface (−40%) and commence runoff (−33%), and reduced infiltration (−34%) and sediment production (−68%). Greater biocrust cover had no significant effect on sorptivity or runoff rate/amount, but increased moisture storage (+14%). Infiltration declined most (−56%) at fine scales, and moisture storage was greatest (+36%) at large scales. Effects of biocrust type (cyanobacteria, lichen, moss, mixed), soil texture (sand, loam, clay), and climatic zone (arid, semiarid, dry subhumid) were nuanced. Our synthesis provides novel insights into the magnitude, processes, and contexts of biocrust effects in drylands. This information is critical to improve our capacity to manage dwindling dryland water supplies as Earth becomes hotter and drier.
The seawaveQ R package provides functionality and help to fit a parametric regression model, SEAWAVE-Q, to pesticide concentration data from stream-water samples to assess trends. The model incorporates the strong seasonality and high degree of censoring common in pesticide data, and users can incorporate numerous ancillary variables such as streamflow anomalies. The model is fitted to pesticide data using maximum likelihood methods for censored data and is robust in terms of pesticide, stream location, and degree of censoring of the concentration data. This R package standardizes this methodology for trend analysis, documents the code, and provides help and tutorial information.
In previous investigations, the SEAWAVE-Q model assumed a linear trend across the period analyzed. For short trend periods, this assumption of a linear trend is adequate. However, as the period of record analyzed becomes longer, the assumption of linearity is problematic because of changes in pesticide regulation and use, some of which can be abrupt. In this update to the model, a restricted cubic spline option was added for long trend periods. This option allows for more flexibility in the time component of the model. Bootstrap functionality is included to determine statistical significance. Model results with the new restricted cubic spline option are compared to the linear trend option for two pesticide-site combinations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201082","collaboration":"National Water Quality Program","usgsCitation":"Ryberg, K.R., and York, B.C., 2020, seawaveQ—An R package providing a model and utilities for analyzing trends in chemical concentrations in streams with a seasonal wave (seawave) and adjustment for streamflow (Q) and other ancillary variables, version 2.0.0: U.S. Geological Survey Open-File Report 2020–1082, 25 p., https://doi.org/10.3133/ofr20201082.","productDescription":"Report: vi, 25; 3 Appendixes","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-101011","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":376796,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1082/ofr20201082_appendix_1.pdf","text":"Appendix 1.","size":"356 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1082 Appendix 1","linkHelpText":"— Vignette for seawaveQ—An R Package Providing a Model and Utilities for Analyzing Trends in Chemical Concentrations in Streams with a Seasonal Wave (seawave) and Adjustment for Streamflow (Q) and Other Ancillary Variables"},{"id":376797,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1082/ofr20201082_appendix_2.pdf","text":"Appendix 2.","size":"228 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1082 Appendix 2","linkHelpText":"— R Documentation"},{"id":376798,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1082/ofr20201082_appendix_4.pdf","text":"Appendix 4.","size":"1.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1082 Appendix 4","linkHelpText":"— Model Comparisons Using seawaveQ"},{"id":376794,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1082/coverthb.jpg"},{"id":376795,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1082/ofr20201082.pdf","text":"Report","size":"2.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1082"}],"contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD
Rivers commonly exhibit substantial variability in suspended‐sand concentration, even at constant water discharge. Here we derive an approach for evaluating how much of this variability arises from mean bed‐sand grain size. We apply this approach to the Colorado River in Grand Canyon, where discharge‐independent concentration of suspended sand varies by more than a factor of 23 (N = 1.4 × 106). Theory predicts that where concentration is controlled by bed‐sand grain size, concentration and grain size in suspension will be inversely correlated (i.e., coarsening of the bed causes suspended sand to become coarser in grain size and lower in concentration). Although the observed correlation is negative, riverbed grain size accounts for only 40% of the variability in concentration. The residuals vary by an order of magnitude; they arise from other processes, such as changes in topography or distribution of sand that cause shear stress to change at constant discharge, changes in the fine tail of bed‐sand grain sizes or changing bedforms. Both bed sand and the other factors influence concentration for durations from less than 1 day to several years. Predictions of concentration based on bed‐sand grain size (N = 4 × 104) are less accurate than predictions based on suspended‐sand grain size, probably because suspended sand is a natural integrator of sand‐transporting processes, giving more weight to those areas of the bed that exchange more sand with the flow. Although the causes of variability vary from one river to another, the approach illustrated here is applicable to any river in which concentration varies at constant water discharge.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JF005226","usgsCitation":"Rubin, D.M., Buscombe, D.D., Wright, S., Topping, D.J., Grams, P.E., Schmidt, J.C., Hazel, J., Kaplinski, M.A., and Tusso, R.B., 2020, Causes of variability in suspended‐sand concentration evaluated using measurements in the Colorado River in Grand Canyon: Journal of Geophysical Research, Earth Surface, v. 125, e2019JF005226, 23 p., https://doi.org/10.1029/2019JF005226.","productDescription":"e2019JF005226, 23 p.","ipdsId":"IP-107060","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":436851,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92Y65R8","text":"USGS data release","linkHelpText":"Measurements of bed grain size on the Colorado River in Grand Canyon National Park, Arizona - 2000 to 2014"},{"id":436850,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92Y65R8","text":"USGS data release","linkHelpText":"Measurements of bed grain size on the Colorado River in Grand Canyon National Park, Arizona - 2000 to 2014"},{"id":377940,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River, Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.01611328125,\n 35.68853320738875\n ],\n [\n -111.4068603515625,\n 35.68853320738875\n ],\n [\n -111.4068603515625,\n 36.97183825093165\n ],\n [\n -114.01611328125,\n 36.97183825093165\n ],\n [\n -114.01611328125,\n 35.68853320738875\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","noUsgsAuthors":false,"publicationDate":"2020-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Rubin, David M.","contributorId":206587,"corporation":false,"usgs":false,"family":"Rubin","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":32898,"text":"U.C. Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":797396,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buscombe, Daniel D. 0000-0001-6217-5584","orcid":"https://orcid.org/0000-0001-6217-5584","contributorId":198817,"corporation":false,"usgs":false,"family":"Buscombe","given":"Daniel","middleInitial":"D.","affiliations":[],"preferred":false,"id":797397,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wright, Scott 0000-0002-0387-5713 sawright@usgs.gov","orcid":"https://orcid.org/0000-0002-0387-5713","contributorId":1536,"corporation":false,"usgs":true,"family":"Wright","given":"Scott","email":"sawright@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":797398,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Topping, David J. 0000-0002-2104-4577 dtopping@usgs.gov","orcid":"https://orcid.org/0000-0002-2104-4577","contributorId":140985,"corporation":false,"usgs":true,"family":"Topping","given":"David","email":"dtopping@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":797399,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grams, Paul E. 0000-0002-0873-0708","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":216115,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":797400,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schmidt, John C.","contributorId":207751,"corporation":false,"usgs":false,"family":"Schmidt","given":"John","email":"","middleInitial":"C.","affiliations":[{"id":37627,"text":"Department of Watershed Sciences, Utah State University, Logan, UT, USA","active":true,"usgs":false}],"preferred":false,"id":797401,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hazel, J.E. Jr.","contributorId":65211,"corporation":false,"usgs":true,"family":"Hazel","given":"J.E.","suffix":"Jr.","email":"","affiliations":[],"preferred":false,"id":797402,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kaplinski, Matthew A.","contributorId":139210,"corporation":false,"usgs":false,"family":"Kaplinski","given":"Matthew","email":"","middleInitial":"A.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":797403,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tusso, Robert B. 0000-0001-7541-3713 rtusso@usgs.gov","orcid":"https://orcid.org/0000-0001-7541-3713","contributorId":4079,"corporation":false,"usgs":true,"family":"Tusso","given":"Robert","email":"rtusso@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":797404,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70218467,"text":"70218467 - 2020 - Nutrient removal and uptake by native planktonic and biofilm bacterial communities in an anaerobic aquifer","interactions":[],"lastModifiedDate":"2021-03-02T13:01:03.632408","indexId":"70218467","displayToPublicDate":"2020-07-29T10:42:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1702,"text":"Frontiers in Microbiology","onlineIssn":"1664-302X","active":true,"publicationSubtype":{"id":10}},"title":"Nutrient removal and uptake by native planktonic and biofilm bacterial communities in an anaerobic aquifer","docAbstract":"Managed aquifer recharge (MAR) offers a collection of water storage and storage options that have been used by resource managers to mitigate the reduced availability of fresh water. One of these technologies is aquifer storage and recovery (ASR), where surface water is treated then recharged into a storage zone within an existing aquifer for later recovery and discharge into a body of water. During the storage phase of ASR, nutrient concentrations in the recharge water have been shown to decrease due, presumably via the uptake by the native aquifer microbial community. In this study, the native microbial community in an anaerobic carbonate aquifer zone targeted for ASR storage was segregated into planktonic and biofilm communities then challenged with NO3-N, PO4-P, and acetate as dissolved organic carbon (DOC) to determine their respective removal and uptake rates. The planktonic community removed NO3-N at a rate of 0.059 mg L–1d–1, PO4-P at 5.73 × 10–8–1.03 × 10–7 mg L–1d–1 and DOC at 0.015–0.244 mg L–1d–1. The biofilm community was significantly more proficient, removing NO3-N at 0.116 mg L–1d–1 (1.6–9.0 μg m–2d–1), PO4-P at 4.20–5.91 × 10–5 mg L–1d–1 (2.47–9.88 ng m–2d–1) and DOC at 0.301–0.696 mg L–1d–1 (29.0–71.0 μg m–2d–1). Additionally, the PO4-P sorption rate onto the carbonate aquifer matrix ranged from 1.64 × 10–7 to 9.25 × 10–7 mg PO4-P m–2 day–1. These rates were applied to field data collected at an ASR facility in central Florida and from the same aquifer storage zone from which the biofilm communities were grown. With only 10% of the available surface area within the storage zone being colonized by biofilms, typical concentrations of NO3-N, PO4-P, and DOC in the recharged filtered surface waters would be reduced to below detection limits, and by 81.4 and 91.1%, respectively, during a 150 days storage period.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmicb.2020.01765","usgsCitation":"Lisle, J.T., 2020, Nutrient removal and uptake by native planktonic and biofilm bacterial communities in an anaerobic aquifer: Frontiers in Microbiology, v. 11, 1765, 13 p., https://doi.org/10.3389/fmicb.2020.01765.","productDescription":"1765, 13 p.","ipdsId":"IP-111177","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":455831,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmicb.2020.01765","text":"Publisher Index Page"},{"id":436853,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EOM5RC","text":"USGS data release","linkHelpText":"Microbial Nutrient Cycling in the Upper Floridan Aquifer"},{"id":436852,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EOM5RC","text":"USGS data release","linkHelpText":"Microbial Nutrient Cycling in the Upper Floridan Aquifer"},{"id":383695,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Kissimmee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.87735652923584,\n 27.15044802232913\n ],\n [\n -80.86731433868408,\n 27.15044802232913\n ],\n [\n -80.86731433868408,\n 27.15772232531679\n ],\n [\n -80.87735652923584,\n 27.15772232531679\n ],\n [\n -80.87735652923584,\n 27.15044802232913\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2020-07-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Lisle, John T. 0000-0002-5447-2092 jlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-5447-2092","contributorId":2944,"corporation":false,"usgs":true,"family":"Lisle","given":"John","email":"jlisle@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":811085,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70216379,"text":"70216379 - 2020 - Life at the frozen limit: Microbial carbon metabolism across a Late Pleistocene permafrost chronosequence","interactions":[],"lastModifiedDate":"2020-11-13T15:09:56.965257","indexId":"70216379","displayToPublicDate":"2020-07-29T09:00:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1702,"text":"Frontiers in Microbiology","onlineIssn":"1664-302X","active":true,"publicationSubtype":{"id":10}},"title":"Life at the frozen limit: Microbial carbon metabolism across a Late Pleistocene permafrost chronosequence","docAbstract":"Permafrost is an extreme habitat yet it hosts microbial populations that remain active over millennia. Using permafrost collected from a Pleistocene chronosequence (19 to 33 ka), we hypothesized that the functional genetic potential of microbial communities in permafrost would reflect microbial strategies to metabolize permafrost soluble organic matter (OM) in situ over geologic time. We also hypothesized that changes in the metagenome across the chronosequence would correlate with shifts in carbon chemistry, permafrost age, and paleoclimate at the time of permafrost formation. We combined high-resolution characterization of water-soluble OM by Fourier-transform ion-cyclotron-resonance mass spectrometry (FT-ICR MS), quantification of organic anions in permafrost water extracts, and metagenomic sequencing to better understand the relationships between the molecular-level composition of potentially bioavailable OM, the microbial community, and permafrost age. Both age and paleoclimate had marked effects on both the molecular composition of dissolved OM and the microbial community. The relative abundance of genes associated with hydrogenotrophic methanogenesis, carbohydrate active enzyme families, nominal oxidation state of carbon (NOSC), and number of identifiable molecular formulae significantly decreased with increasing age. In contrast, genes associated with fermentation of short chain fatty acids (SCFAs), the concentration of SCFAs and ammonium all significantly increased with age. We present a conceptual model of microbial metabolism in permafrost based on fermentation of OM and the buildup of organic acids that helps to explain the unique chemistry of ancient permafrost soils. These findings imply long-term in situ microbial turnover of ancient permafrost OM and that this pooled biolabile OM could prime ancient permafrost soils for a larger and more rapid microbial response to thaw compared to younger permafrost soils.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmicb.2020.01753","usgsCitation":"Leewis, M., Berlemont, R., Podgorski, D.C., Srinivas, A., Zito, P., Spencer, R.G., McFarland, J., Douglas, T.A., Conaway, C., Waldrop, M., and Mackelprang, R., 2020, Life at the frozen limit: Microbial carbon metabolism across a Late Pleistocene permafrost chronosequence: Frontiers in Microbiology, v. 11, 1753, 15 p., https://doi.org/10.3389/fmicb.2020.01753.","productDescription":"1753, 15 p.","ipdsId":"IP-114701","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":455834,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmicb.2020.01753","text":"Publisher Index Page"},{"id":436855,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P933APLH","text":"USGS data release","linkHelpText":"Microbial Carbon and Nitrogen Metabolism Across a Late Pleistocene Permafrost Chronosequence"},{"id":436854,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P933APLH","text":"USGS data release","linkHelpText":"Microbial Carbon and Nitrogen Metabolism Across a Late Pleistocene Permafrost Chronosequence"},{"id":380506,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Fairbanks","otherGeospatial":"Cold Regions Research and Engineering Laboratory","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -147.8814697265625,\n 64.82616499475317\n ],\n [\n -147.5148010253906,\n 64.82616499475317\n ],\n [\n -147.5148010253906,\n 64.98807019388211\n ],\n [\n -147.8814697265625,\n 64.98807019388211\n ],\n [\n -147.8814697265625,\n 64.82616499475317\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2020-07-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Leewis, Mary-Cathrine 0000-0001-6496-8094","orcid":"https://orcid.org/0000-0001-6496-8094","contributorId":244858,"corporation":false,"usgs":true,"family":"Leewis","given":"Mary-Cathrine","email":"","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":804829,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berlemont, Renaud 0000-0001-9243-5092","orcid":"https://orcid.org/0000-0001-9243-5092","contributorId":244872,"corporation":false,"usgs":false,"family":"Berlemont","given":"Renaud","email":"","affiliations":[{"id":34411,"text":"California State University Long Beach","active":true,"usgs":false}],"preferred":false,"id":804830,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Podgorski, David C.","contributorId":178153,"corporation":false,"usgs":false,"family":"Podgorski","given":"David","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":804831,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Srinivas, Archana","contributorId":244875,"corporation":false,"usgs":false,"family":"Srinivas","given":"Archana","email":"","affiliations":[{"id":49006,"text":"Department of Biology, California State University Northridge, Northridge, CA, USA","active":true,"usgs":false}],"preferred":false,"id":804832,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zito, Phoebe","contributorId":206101,"corporation":false,"usgs":false,"family":"Zito","given":"Phoebe","email":"","affiliations":[{"id":37245,"text":"University of New Orleans","active":true,"usgs":false}],"preferred":false,"id":804833,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Spencer, Robert G. M. 0000-0003-0777-0748","orcid":"https://orcid.org/0000-0003-0777-0748","contributorId":238028,"corporation":false,"usgs":false,"family":"Spencer","given":"Robert","email":"","middleInitial":"G. M.","affiliations":[{"id":47686,"text":"Department of Earth, Ocean and Atmospheric Science, Florida State University","active":true,"usgs":false}],"preferred":false,"id":804834,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McFarland, Jack 0000-0001-9672-8597","orcid":"https://orcid.org/0000-0001-9672-8597","contributorId":214819,"corporation":false,"usgs":true,"family":"McFarland","given":"Jack","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":804835,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Douglas, Thomas A. 0000-0003-1314-1905","orcid":"https://orcid.org/0000-0003-1314-1905","contributorId":64553,"corporation":false,"usgs":false,"family":"Douglas","given":"Thomas","email":"","middleInitial":"A.","affiliations":[{"id":33087,"text":"Cold Regions Research and Engineering Laboratory","active":true,"usgs":false}],"preferred":true,"id":804836,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Conaway, Christopher H. 0000-0002-0991-033X","orcid":"https://orcid.org/0000-0002-0991-033X","contributorId":201932,"corporation":false,"usgs":true,"family":"Conaway","given":"Christopher H.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":804837,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Waldrop, Mark 0000-0003-1829-7140","orcid":"https://orcid.org/0000-0003-1829-7140","contributorId":216758,"corporation":false,"usgs":true,"family":"Waldrop","given":"Mark","affiliations":[],"preferred":true,"id":804838,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mackelprang, Rachel","contributorId":200882,"corporation":false,"usgs":false,"family":"Mackelprang","given":"Rachel","email":"","affiliations":[{"id":7080,"text":"California State University, Northridge","active":true,"usgs":false}],"preferred":false,"id":804839,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70211634,"text":"70211634 - 2020 - Legacy effects of hydrologic alteration in playa wetland responses to droughts","interactions":[],"lastModifiedDate":"2020-12-29T21:21:31.795804","indexId":"70211634","displayToPublicDate":"2020-07-28T15:46:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Legacy effects of hydrologic alteration in playa wetland responses to droughts","docAbstract":"Wetland conservation increasingly must account for climate change and legacies of previous land-use practices. Playa wetlands provide critical wildlife habitat, but may be impacted by intensifying droughts and previous hydrologic modifications. To inform playa restoration planning, we asked: (1) what are the trends in playa inundation? (2) what are the factors influencing inundation? (3) how is playa inundation affected by increasingly severe drought? (4) do certain playas provide hydrologic refugia during droughts, and (5) if so, how are refugia patterns related to historical modifications? Using remotely sensed surface-water data, we evaluated a 30-year time series (1985–2015) of inundation for 153 playas of the Great Basin, USA. Inundation likelihood and duration increased with wetter weather conditions and were greater in modified playas. Inundation probability was projected to decrease from 22% under average conditions to 11% under extreme drought, with respective annual inundation decreasing from 1.7 to 0.9 months. Only 4% of playas were inundated for at least 2 months in each of the 5 driest years, suggesting their potential as drought refugia. Refugial playas were larger and more likely to have been modified, possibly because previous land managers selected refugial playas for modification. These inundation patterns can inform efforts to restore wetland functions and to conserve playa habitats as climate conditions change.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-020-01334-0","usgsCitation":"Russell, M.T., Cartwright, J.M., Collins, G.H., Long, R.A., and Eitel, J., 2020, Legacy effects of hydrologic alteration in playa wetland responses to droughts: Wetlands, v. 40, p. 2011-2024, https://doi.org/10.1007/s13157-020-01334-0.","productDescription":"14 p.","startPage":"2011","endPage":"2024","ipdsId":"IP-111867","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":455846,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s13157-020-01334-0","text":"Publisher Index Page"},{"id":377103,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada, Oregon","otherGeospatial":"Sheldon-Hart Mountain National Wildlife Refuge Complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.96246337890624,\n 41.52091689636249\n ],\n [\n -118.69354248046875,\n 41.52091689636249\n ],\n [\n -118.69354248046875,\n 42.84777884235988\n ],\n [\n -119.96246337890624,\n 42.84777884235988\n ],\n [\n -119.96246337890624,\n 41.52091689636249\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","noUsgsAuthors":false,"publicationDate":"2020-07-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Russell, Micah T.","contributorId":236988,"corporation":false,"usgs":false,"family":"Russell","given":"Micah","email":"","middleInitial":"T.","affiliations":[{"id":38118,"text":"Western Colorado University","active":true,"usgs":false}],"preferred":false,"id":794877,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cartwright, Jennifer M. 0000-0003-0851-8456 jmcart@usgs.gov","orcid":"https://orcid.org/0000-0003-0851-8456","contributorId":5386,"corporation":false,"usgs":true,"family":"Cartwright","given":"Jennifer","email":"jmcart@usgs.gov","middleInitial":"M.","affiliations":[{"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":794878,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Gail H.","contributorId":59170,"corporation":false,"usgs":false,"family":"Collins","given":"Gail","email":"","middleInitial":"H.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":794879,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Long, Ryan A.","contributorId":236989,"corporation":false,"usgs":false,"family":"Long","given":"Ryan","email":"","middleInitial":"A.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":794880,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eitel, Jan H.","contributorId":236991,"corporation":false,"usgs":false,"family":"Eitel","given":"Jan H.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":794881,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227774,"text":"70227774 - 2020 - Vegetation sampling and management","interactions":[],"lastModifiedDate":"2022-01-31T17:47:50.412067","indexId":"70227774","displayToPublicDate":"2020-07-28T11:39:15","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"19","title":"Vegetation sampling and management","docAbstract":"What is the utility of vegetation measurements for wildlife managers? In the prairie, savanna, tundra, forest, steppe, and wetland regions of the world, mixtures of plant species provide wildlife with food, cover and, in some circumstances, water; the 3 essential habitat elements necessary to sustain viable wildlife populations. We define habitat in reference to use of a vegetation type by an animal (e.g., deer habitat) and vegetation type when referring to differences in vegetation stands (e.g., marsh vegetation type versus tall grass prairie vegetation type; Hall et al. 1997). In strict definition, the variety of wildlife using plants ranges from snails and voles (Microtus spp.) to bison (Bison bison) and elephants (Loxodonta spp.) in uplands and from mosquitoes and ducks to muskrats (Ondatra zibethicus) and manatees (Trichechus manatus) in wetlands. Through evolutionary processes, some wildlife species are totally dependent on vegetation for all annual life requirements, whereas other species use vegetation only for cover or food. Regardless of the role of vegetation in the sustenance of wildlife, any management or research project that requires evaluation of wildlife and vegetation type relationships on a unit of land will necessitate some form of vegetation measurement.\nThe term vegetation can refer to a single plant or species on a specific site or a community in the landscape. Vegetation may occur naturally or be introduced, and may be live or dead. Uses of vegetation measurements are many: (1) evaluation of vegetation response to management practices, (2) estimation of carrying capacity and/or forage production, (3) characterization of cover and habitat components for an endangered species, or (4) long-term monitoring of the general trend of plant vigor or vegetation type condition.\nSurveying and measuring quantity and quality of vegetation within habitats are basic to wildlife research and management. Grassland, shrubland, and woodland vegetation types are comprised of populations in which individual plants are usually too numerous to inventory completely. Consequently, wildlife biologists usually use sampling techniques to make inferences about the total plant population within a given vegetation type.\nVegetation sampling methodologies have evolved within several ecological disciplines (e.g., plant ecology, forestry, rangeland science) and for a variety of management or research objectives (e.g., estimating forage for ungulates, describing habitat use by passerine birds). Description of every method that has been used to sample vegetation is beyond the scope of this chapter. We describe how to measure vegetation structure, which Dansereau (1957) defined as the spatial organization (distribution) of individuals that form a stand. We have organized this chapter into a description of basic methods of vegetation sampling with examples of how those methods have been applied or modified in wildlife research and management. We assume the investigator/reader has adequate knowledge of the concepts of wildlife ecology, primary habitat requirements of wildlife species under study, and ability to systematically identify the species of wildlife and vascular plants within the geographical area of investigation.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The wildlife techniques manual. Volume 1: Research. Volume 2: Management.","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Johns Hopkins University Press","collaboration":"The Wildlife Society","usgsCitation":"Higgins, K., Jenkins, K., Uresk, D.W., Perkins, L.B., Jensen, K., Norland, J., Klaver, R.W., and Naugle, D.E., 2020, Vegetation sampling and management, chap. 19 of The wildlife techniques manual. Volume 1: Research. Volume 2: Management., p. 409-438.","productDescription":"30 p.","startPage":"409","endPage":"438","ipdsId":"IP-092410","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"links":[{"id":395165,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Higgins, Kenneth F.","contributorId":272584,"corporation":false,"usgs":false,"family":"Higgins","given":"Kenneth F.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":832179,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jenkins, Kurt 0000-0003-1415-6607","orcid":"https://orcid.org/0000-0003-1415-6607","contributorId":221472,"corporation":false,"usgs":true,"family":"Jenkins","given":"Kurt","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":832183,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Uresk, Daniel W.","contributorId":272585,"corporation":false,"usgs":false,"family":"Uresk","given":"Daniel","email":"","middleInitial":"W.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":832181,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perkins, Lora B.","contributorId":272586,"corporation":false,"usgs":false,"family":"Perkins","given":"Lora","email":"","middleInitial":"B.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":832182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jensen, Kent C.","contributorId":272587,"corporation":false,"usgs":false,"family":"Jensen","given":"Kent C.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":832184,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Norland, Jack E.","contributorId":272588,"corporation":false,"usgs":false,"family":"Norland","given":"Jack E.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":832185,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":832180,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Naugle, David E.","contributorId":272589,"corporation":false,"usgs":false,"family":"Naugle","given":"David","email":"","middleInitial":"E.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":832186,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70211356,"text":"sir20205075 - 2020 - Estimates of groundwater discharge by evapotranspiration, Stump Spring and Hiko Springs, Clark County, southern Nevada, 2016–18","interactions":[],"lastModifiedDate":"2025-05-14T18:35:24.827452","indexId":"sir20205075","displayToPublicDate":"2020-07-28T09:24:17","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5075","displayTitle":"Estimates of Groundwater Discharge by Evapotranspiration, Stump Spring and Hiko Springs, Clark County, Southern Nevada, 2016–18","title":"Estimates of groundwater discharge by evapotranspiration, Stump Spring and Hiko Springs, Clark County, southern Nevada, 2016–18","docAbstract":"This report documents methodology and results of a study that estimated groundwater discharge by evapotranspiration (GWET) from phreatophytic vegetation in two desert riparian areas with ephemeral spring discharge in Clark County, southern Nevada. The phreatophytes consisted primarily of western honey mesquite [Prosopis glandulosa var. torreyana (L.D. Benson) M.C. Johnst.] at Stump Spring and mixed shrubs at Hiko Springs. An eddy-covariance station and precipitation gage were established to concurrently measure actual evapotranspiration (AET) and precipitation. Site-scale GWET rates—computed by subtracting measured precipitation from AET—were 239 ±45 millimeters per year (mm/yr) based on measurements over one growing season at Stump Spring and 109 ±27 mm/yr averaged over two growing seasons at Hiko Springs.
The volume of GWET for each groundwater discharge area (GDA) was estimated by developing relations between site-scale computed GWET rates and phreatophytic vegetation represented by a Normalized Difference Vegetation Index (NDVI). A GDA was delineated for the natural drainage in each area by mapping the extent of phreatophytes using high-resolution imagery. A second GDA was delineated at Stump Spring by mapping the extent of phreatophytes in the Area of Critical Environmental Concern (ACEC). Site-scale GWET rates were scaled up by applying the site-based GWET -NDVI relations to NDVI distributions in each GDA. The areas of phreatophytic vegetation within each GDA, area-weighted mean GWET rates, and GWET volumes were as follows: (1) Stump Spring—59 hectares (ha), 126 mm/yr, 7.4 ± 1.4 ×104 cubic meters per year (m3/yr) (60 ± 11 acre-feet/yr); Stump Spring ACEC—49 ha, 98 mm/yr, 4.9 ± 0.9 × 104 m3/yr (39 ± 7 acre-feet/yr); and (2) Hiko Springs—7.2 ha, 112 mm/yr, 0.8 ±0.2 × 104 m3/yr (6.6 ±1.6 acre-feet/yr). The GWET rate computed at Stump Spring compared favorably with published GWET rates for mesquite.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205075","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Moreo, M.T., Buto, S.G., Smith, D.W., and Nelson, N.C., 2020, Estimates of groundwater discharge by evapotranspiration, Stump Spring and Hiko Springs, Clark County, southern Nevada, 2016–18: U.S. Geological Survey Scientific Investigations Report 2020–5075, 39 p., https://doi.org/ 10.3133/ sir20205075.","productDescription":"Report: vii, 39 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-098487","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":376763,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5075/sir20205075.pdf","text":"Report","size":"6.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5075"},{"id":376762,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5075/coverthb.jpg"},{"id":376765,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KD11GX","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Supplemental evapotranspiration gap-filled datasets from Stump Spring and Hiko Springs, Clark County, southern Nevada, 2016–18"},{"id":376764,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BG3VKP","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial data to support estimates of annual groundwater discharge by evapotranspiration from areas of spring-fed riparian vegetation, Stump Spring and Hiko Springs, Clark County, southern Nevada"}],"country":"United States","state":"Nevada","county":"Clark 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Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701
Understanding groundwater quality in transboundary aquifers like the Hueco Bolson is important for the 2.7 million people along the United States and Mexico border living in and near the combined metropolitan areas of Ciudad Juárez, Mexico, and El Paso, Texas, who rely on groundwater for water supply. To better understand water-quality conditions in the Mexico–New Mexico–Texas transboundary area, 23 water-supply wells were sampled in the Hueco Bolson within the United States near El Paso, Tex., during August–September 2016 and May–June 2017. Groundwater samples were analyzed for physical properties, major ions, dissolved solids, nutrients, trace elements, organic compounds, and selected isotopes such as strontium, hydrogen, oxygen, tritium, and carbon-14.
Most of the water samples from the Hueco Bolson water-supply wells were classified as a sodium-chloride type water. Only four wells sampled in the study area had dissolved-solids concentrations greater than 1,000 milligrams per liter (mg/L), with three of those wells closest to the Rio Grande/Río Bravo del Norte (hereinafter referred to as the Rio Grande).
Nitrate concentrations in the groundwater samples collected in the study area ranged from below the long-term method detection level of 0.04 to 6.2 mg/L. Arsenic was the only trace element detected in the wells sampled that had concentrations exceeding the designated drinking-water standard of 10 micrograms per liter (μg/L). Four of the 23 wells had arsenic concentrations greater than 10 μg/L, and these wells were all located near the Rio Grande. Three of the wells with the highest uranium concentrations (greater than 10 μg/L) were also located near the Rio Grande, and two of those wells were the same wells that had arsenic concentrations greater than 10 μg/L. Groundwater samples were analyzed for 83 organic compounds, but only 6 were detected—simazine, prometryn, prometon, atrazine, deethylatrazine, and dichloroaniline. All concentrations for the organic compounds detected were less than 0.03 μg/L, and the detections were only in five groundwater wells, three of which were located near the Rio Grande.
Strontium, hydrogen, and oxygen isotopic values indicate that recharge water to the central and northern sections of the study area originates from near the Franklin Mountains, whereas groundwater in the southern section of the study area is likely from the Rio Grande valley. Tritium and carbon-14 values indicate that most of the wells that were sampled contained water that is considered premodern, which means that it is more than several hundred years old. Three wells with modern groundwater (approximately less than 70 years old) are located near the Rio Grande and are the same wells that had elevated arsenic or uranium concentrations and organic compound detections. Most of the results of the geochemical analyses indicate that groundwater near the Rio Grande has higher dissolved-solids concentrations, higher concentrations of several trace elements, and slightly more organic compound detections than the groundwater farther away from the Rio Grande; therefore, the groundwater may be affected by the Rio Grande and surrounding land-use activities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205056","usgsCitation":"Ging, P.B., Humberson, D.G., and Ikard, S.J., 2020, Geochemical assessment of the Hueco Bolson, New Mexico and Texas, 2016–17: U.S. Geological Survey Scientific Investigations Report 2020–5056, 30 p., https://doi.org/10.3133/sir20205056.","productDescription":"Report: viii, 30 p.; Data Release","numberOfPages":"42","onlineOnly":"N","ipdsId":"IP-112001","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":376690,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5056/sir20205056.pdf","text":"Report","size":"5.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 22020–5056"},{"id":376689,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5056/coverthb.jpg"},{"id":376691,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F73T9GHR","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Transboundary Aquifer Assessment Program—Water-quality data from groundwater wells in the Hueco Bolson near El Paso, Texas, August 2016–June 2017"}],"country":"United States","state":"New Mexico, Texas","otherGeospatial":"Hueco Bolson","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.4462890625,\n 31.12819929911196\n ],\n [\n -105.53466796874999,\n 31.12819929911196\n ],\n [\n -105.53466796874999,\n 32.491230287947594\n ],\n [\n -107.4462890625,\n 32.491230287947594\n ],\n [\n -107.4462890625,\n 31.12819929911196\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
The National Water-Quality Assessment (NAWQA) Project of the U.S. Geological Survey (USGS) submitted nearly 1,900 samples collected from groundwater sites across the United States in 2013–18 for analysis of 225 pesticide compounds (pesticides and pesticide degradates, hereafter referred to as “pesticides”) by USGS National Water Quality Laboratory schedule 2437 (S2437). For the associated NAWQA study of pesticide occurrence and concentration in groundwater, and for other studies using pesticide results determined by S2437, it is necessary to assess the ability of reported results to meet data-quality requirements that will allow study objectives to be achieved. This assessment of the quality of S2437 results reported in 2013–18 examined data from field and laboratory quality-control samples, along with third-party performance assessment samples, to estimate bias and variability and to identify their potential sources, with an emphasis on implications for the interpretation of pesticide data for groundwater. Results indicate that measurements produced by the S2437 method for most pesticides have bias and variability that would be considered acceptable for many interpretative studies, which could therefore use the results without qualification or censoring. However, the reported data for a subset of pesticides have the potential for unacceptable contamination bias, high or low recovery bias, or high variability as a consequence of method performance and (or) nonlaboratory factors that could preclude their use for certain common objectives or could necessitate adjustment or qualification to meet those objectives.
Based on data for laboratory blanks, censoring of some detections for a subset of pesticides reported by the laboratory in environmental samples might be necessary or desirable to avoid an unacceptably high likelihood of a false-positive result caused by laboratory contamination. The 90-percent upper confidence limit for the 95th percentile of laboratory blank concentration equals or exceeds the minimum reported groundwater concentration in at least 1 water year for 28 pesticides. During at least 1 water year, this upper confidence limit exceeds the maximum laboratory detection limit for 17 pesticides and exceeds the maximum laboratory reporting limit for 3 pesticides (ametryn, atrazine, and diazinon). The level of contamination indicated by this upper confidence limit should not substantially affect the suitability of reported environmental concentrations for any compound for comparison with corresponding human-health benchmarks.
Despite being subjected to the same laboratory processes as laboratory blanks, field blanks indicated little evidence of contamination bias. This observation could largely be the consequence of data-reporting practices, which utilize detections in laboratory blanks to censor results in associated field samples (including blanks and environmental samples) when relative concentrations indicate that a result could have a substantial contribution from laboratory contamination. Laboratory censoring appears likely to reduce the risk of false-positive results in environmental samples below the level that laboratory blank results alone would imply.
Whereas data available for third-party blind blank samples analyzed in 2018 indicate that only propoxur had any false-positive results, data for pesticides that were not spiked into blind spike samples analyzed in 2013–18 indicate that the false-positive rates for 31 pesticides exceeded 1 percent when considering only detections reported at concentrations greater than the maximum detection limit. Although about half of these pesticides lack substantial supporting evidence of contamination bias based on laboratory blank or field blank detections, indicating that spiking issues or degradation of parent compounds within the spiked samples might be a contributing factor to some false-positive results, these results indicate the need to closely examine detections reported for some pesticides in environmental samples analyzed during a similar period for possible contributions from contamination bias. Data for blind spike samples that were spiked at concentrations above the maximum reporting limit indicate that false-negative rates for eight pesticides exceed 10 percent; substantial low bias could affect results reported for these pesticides in environmental samples analyzed during a similar period.
Data for laboratory reagent spikes, which measure recovery of pesticides in blank water, show little evidence for unacceptable recovery bias for S2437 pesticides. However, field matrix spikes, which measure recovery of pesticides in environmental matrices, indicate that degradation and (or) matrix effects could result in moderate to substantial low bias for groundwater results for several pesticides. Low bias could cause some reported concentrations to be categorized as being below a benchmark when the actual concentration in groundwater is greater than the benchmark. Occurrence and concentrations in groundwater could be substantially underrepresented for six pesticides with benchmarks (1H-1,2,4-triazole, asulam, bifenthrin, cis-permethrin, fenbutatin oxide, and naled) that have median recoveries between zero and 50 percent in field matrix spikes. Two compounds (didealkylatrazine and 2-hydroxy-6-ethylamino-4-amino-s-triazine) have median recoveries near or greater than 150 percent in field matrix spikes, indicating a substantial high bias. Plots of data for all spike types show clear changes in the typical recovery with time for some pesticides, which would require further examination for evaluation of temporal trends in environmental concentrations.
Data for laboratory reagent spikes indicate that nearly all S2437 pesticides have acceptable variability resulting from random measurement error. Only two compounds (fenbutatin oxide and naled) have F-pseudosigma values greater than 30 percent for recovery, which implies the potential for relatively high variability in reported concentrations and could affect comparison of concentrations to benchmarks and determination of whether concentrations for samples collected at separate locations or times are truly different with a specified level of confidence. Data for third-party blind spike samples show relatively high variability for a greater number of pesticides, although these results likely reflect the influence of degradation and (or) differences in the magnitude and variability of concentrations used for blind spikes relative to laboratory reagent spikes. Detailed analysis of variability using field replicate data is possible for only 12 pesticides on S2437; low variability in analyte detection and concentration is indicated for most of these pesticides in groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205072","collaboration":"National Water-Quality Assessment Project","usgsCitation":"Bexfield, L.M., Belitz, K., Sandstrom, M.W., Beaty, D., Medalie, L., Lindsey, B.D., and Nowell, L.H., 2020, Quality of pesticide data for groundwater analyzed for the National Water-Quality Assessment Project, 2013–18: U.S. Geological Survey Scientific Investigations Report 2020–5072, 35 p., https://doi.org/10.3133/sir20205072.","productDescription":"Report: vi, 35 p.; 11 Tables; Data Release","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-111029","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes 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U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
The U.S. Geological Survey, in cooperation with the University of Minnesota-Duluth Natural Resources Research Institute, completed an assessment of regional water quality in areas of potential base-metal mining in Minnesota. Bedrock, soil, streambed sediment, and surface-water samples were collected in three watersheds that cross the basal part of the Duluth Complex with different mineral-deposit settings: (1) copper-nickel-platinum group element mineralization (Filson Creek), (2) iron-titanium-oxide mineralization (headwaters of the St. Louis River), and (3) no identified mineralization (Keeley Creek). At least 10 bedrock, 30 soil (2 each from 15 sites), and as many as 13 streambed sediment samples were collected in each watershed and analyzed for 44 major and trace elements, total and inorganic carbon, and 10 loosely bound metals (when possible). Surface-water samples were collected at four to nine locations in each watershed three to four times per year for 2 years (total of 141 environmental samples). Surface-water samples were analyzed for 10 trace metals (total and dissolved concentrations), 8 trace elements, 8 major ions (dissolved concentrations), alkalinity, and total and dissolved organic carbon.
Metal and element concentrations in solid media varied by watershed, representing local geology. Copper-nickel sulfide mineralization in the Filson Creek watershed was evidenced in bedrock, soil, and streambed sediments. In the Keeley Creek watershed, silicate mineralogy of underlying bedrock contributed metals to streambed sediments. Thick glacial cover masked potential bedrock contributions to solid media in the St. Louis River watershed. Water-quality data indicate that waters in all three watersheds are dilute. Water quality is more similar between the Filson and Keeley Creek watersheds, compared to the St. Louis River watershed, because of the difference in glacial cover. Metal concentrations (copper and nickel, in particular) in surface-water samples follow similar patterns of concentrations in solid media, indicating the influence of bedrock on water quality in Filson and Keeley Creeks. Data from this study provide a baseline of metal concentrations and general water quality within an area of active mineral exploration.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205039","collaboration":"Prepared in cooperation with the University of Minnesota-Duluth Natural Resources Research Institute","usgsCitation":"Elliott, S.M., Jones, P.M., Woodruff, L.G., Jennings, C.E., Krall, A.L., and Morel, D.L., 2020, Assessing the influence of natural copper-nickel-bearing bedrocks of the Duluth Complex on water quality in Minnesota, 2013–15: U.S. Geological Survey Scientific Investigations Report 2020–5039, 51 p., https://doi.org/10.3133/sir20205039.","productDescription":"Report: x, 51 p.; 1 Table; 2 Appendices; Data Release","numberOfPages":"66","onlineOnly":"Y","ipdsId":"IP-110010","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":376695,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5039/sir20205039_appendix_tables.xlsx","text":"Appendix Tables 1.1 and 1.2","size":"207 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5039 Appendix Tables 1.1–1.2"},{"id":376694,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5039/sir20205039_tables5to7.xlsx","text":"Tables 5 to 7","size":"42.2 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5039 Tables 5–7"},{"id":376696,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VO251H","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geochemical characterization of solid media from three watersheds that transect the basal contact of the Duluth Complex, northeastern Minnesota"},{"id":376692,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5039/coverthb.jpg"},{"id":376693,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5039/sir20205039.pdf","text":"Report","size":"28.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5039"}],"country":"United States","state":"Minnesota","otherGeospatial":"Duluth Complex, Filson Creek, Keeley Creek, headwaters of the St. Louis River watersheds","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.6806640625,\n 47.1075227853425\n ],\n [\n -92.076416015625,\n 47.12995075666307\n ],\n [\n -91.38427734374999,\n 47.1075227853425\n ],\n [\n -91.373291015625,\n 47.98256841921405\n ],\n [\n -92.691650390625,\n 47.98256841921405\n ],\n [\n -92.6806640625,\n 47.1075227853425\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112