Aquatic systems are critical to food, security, and society. But, water data are collected by hundreds of research groups and organizations, many of which use nonstandard or inconsistent data descriptions and dissemination, and disparities across different types of water observation systems represent a major challenge for freshwater research. To address this issue, the Water Quality Portal (WQP) was developed by the U.S. Environmental Protection Agency, the U.S. Geological Survey, and the National Water Quality Monitoring Council to be a single point of access for water quality data dating back more than a century. The WQP is the largest standardized water quality data set available at the time of this writing, with more than 290 million records from more than 2.7 million sites in groundwater, inland, and coastal waters. The number of data contributors, data consumers, and third-party application developers making use of the WQP is growing rapidly. Here we introduce the WQP, including an overview of data, the standardized data model, and data access and services; and we describe challenges and opportunities associated with using WQP data. We also demonstrate through an example the value of the WQP data by characterizing seasonal variation in lake water clarity for regions of the continental U.S. The code used to access, download, analyze, and display these WQP data as shown in the figures is included as supporting information.
","language":"English","publisher":"Wiley","doi":"10.1002/2016WR019993","usgsCitation":"Read, E.K., Carr, L., DeCicco, L.A., Dugan, H., Hanson, P.C., Hart, J.A., Kreft, J., Read, J.S., and Winslow, L., 2017, Water quality data for national-scale aquatic research: The Water Quality Portal: Water Resources Research, v. 53, no. 2, p. 1735-1745, https://doi.org/10.1002/2016WR019993.","productDescription":"11 p.","startPage":"1735","endPage":"1745","ipdsId":"IP-082664","costCenters":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"links":[{"id":470070,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016wr019993","text":"Publisher Index Page"},{"id":335451,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-12","publicationStatus":"PW","scienceBaseUri":"58a576bae4b057081a24ed19","contributors":{"authors":[{"text":"Read, Emily K. 0000-0002-9617-9433 eread@usgs.gov","orcid":"https://orcid.org/0000-0002-9617-9433","contributorId":5815,"corporation":false,"usgs":true,"family":"Read","given":"Emily","email":"eread@usgs.gov","middleInitial":"K.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"preferred":false,"id":669232,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carr, Lindsay 0000-0002-5799-6297 lcarr@usgs.gov","orcid":"https://orcid.org/0000-0002-5799-6297","contributorId":181732,"corporation":false,"usgs":true,"family":"Carr","given":"Lindsay","email":"lcarr@usgs.gov","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":669233,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeCicco, Laura A. 0000-0002-3915-9487 ldecicco@usgs.gov","orcid":"https://orcid.org/0000-0002-3915-9487","contributorId":174716,"corporation":false,"usgs":true,"family":"DeCicco","given":"Laura","email":"ldecicco@usgs.gov","middleInitial":"A.","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":669234,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dugan, Hilary","contributorId":150191,"corporation":false,"usgs":false,"family":"Dugan","given":"Hilary","affiliations":[{"id":17938,"text":"Center for Limnology University of Wisconsin, Madison, WI 53706, US","active":true,"usgs":false}],"preferred":false,"id":669235,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hanson, Paul C.","contributorId":35634,"corporation":false,"usgs":false,"family":"Hanson","given":"Paul","email":"","middleInitial":"C.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":669236,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hart, Julia A. 0000-0002-0183-8070","orcid":"https://orcid.org/0000-0002-0183-8070","contributorId":181733,"corporation":false,"usgs":false,"family":"Hart","given":"Julia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":669237,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kreft, James 0000-0001-8088-7788 jkreft@usgs.gov","orcid":"https://orcid.org/0000-0001-8088-7788","contributorId":181734,"corporation":false,"usgs":true,"family":"Kreft","given":"James","email":"jkreft@usgs.gov","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":669238,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Read, Jordan S. 0000-0002-3888-6631 jread@usgs.gov","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":4453,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","email":"jread@usgs.gov","middleInitial":"S.","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":669239,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Winslow, Luke 0000-0002-8602-5510 lwinslow@usgs.gov","orcid":"https://orcid.org/0000-0002-8602-5510","contributorId":168947,"corporation":false,"usgs":true,"family":"Winslow","given":"Luke","email":"lwinslow@usgs.gov","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":669240,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70182056,"text":"70182056 - 2017 - Fire and the distribution and uncertainty of carbon sequestered as above-ground tree biomass in Yosemite and Sequoia & Kings Canyon National Parks","interactions":[],"lastModifiedDate":"2017-02-15T15:12:59","indexId":"70182056","displayToPublicDate":"2017-02-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2596,"text":"Land","active":true,"publicationSubtype":{"id":10}},"title":"Fire and the distribution and uncertainty of carbon sequestered as above-ground tree biomass in Yosemite and Sequoia & Kings Canyon National Parks","docAbstract":"Fire is one of the principal agents changing forest carbon stocks and landscape level distributions of carbon, but few studies have addressed how accurate carbon accounting of fire-killed trees is or can be. We used a large number of forested plots (1646), detailed selection of species-specific and location-specific allometric equations, vegetation type maps with high levels of accuracy, and Monte Carlo simulation to model the amount and uncertainty of aboveground tree carbon present in tree species (hereafter, carbon) within Yosemite and Sequoia & Kings Canyon National Parks. We estimated aboveground carbon in trees within Yosemite National Park to be 25 Tg of carbon (C) (confidence interval (CI): 23–27 Tg C), and in Sequoia & Kings Canyon National Park to be 20 Tg C (CI: 18–21 Tg C). Low-severity and moderate-severity fire had little or no effect on the amount of carbon sequestered in trees at the landscape scale, and high-severity fire did not immediately consume much carbon. Although many of our data inputs were more accurate than those used in similar studies in other locations, the total uncertainty of carbon estimates was still greater than ±10%, mostly due to potential uncertainties in landscape-scale vegetation type mismatches and trees larger than the ranges of existing allometric equations. If carbon inventories are to be meaningfully used in policy, there is an urgent need for more accurate landscape classification methods, improvement in allometric equations for tree species, and better understanding of the uncertainties inherent in existing carbon accounting methods.","language":"English","publisher":"MDPI","doi":"10.3390/land6010010","usgsCitation":"Lutz, J.A., Matchett, J.R., Tarnay, L.W., Smith, D., Becker, K.M., Furniss, T.J., and Brooks, M.L., 2017, Fire and the distribution and uncertainty of carbon sequestered as above-ground tree biomass in Yosemite and Sequoia & Kings Canyon National Parks: Land, v. 6, no. 1, Article 10; 24 p., https://doi.org/10.3390/land6010010.","productDescription":"Article 10; 24 p.","ipdsId":"IP-066486","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":461737,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/land6010010","text":"Publisher Index Page"},{"id":335622,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Kings Canyon National Park, Sequoia National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.827880859375,\n 35.40248356426937\n ],\n [\n -117.61962890624999,\n 35.40248356426937\n ],\n [\n -117.61962890624999,\n 37.18657859524883\n ],\n [\n -119.827880859375,\n 37.18657859524883\n ],\n [\n -119.827880859375,\n 35.40248356426937\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-27","publicationStatus":"PW","scienceBaseUri":"58a576b8e4b057081a24ed0e","contributors":{"authors":[{"text":"Lutz, James A.","contributorId":139178,"corporation":false,"usgs":false,"family":"Lutz","given":"James","email":"","middleInitial":"A.","affiliations":[{"id":12682,"text":"Utah State University, Logan, UT","active":true,"usgs":false}],"preferred":false,"id":669414,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Matchett, John R. 0000-0002-2905-6468 jmatchett@usgs.gov","orcid":"https://orcid.org/0000-0002-2905-6468","contributorId":1669,"corporation":false,"usgs":true,"family":"Matchett","given":"John","email":"jmatchett@usgs.gov","middleInitial":"R.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":669415,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tarnay, Leland W.","contributorId":139179,"corporation":false,"usgs":false,"family":"Tarnay","given":"Leland","email":"","middleInitial":"W.","affiliations":[{"id":12683,"text":"National Park Service, Yosemite National Park, El Portal, CA","active":true,"usgs":false}],"preferred":false,"id":669416,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Douglas F.","contributorId":181753,"corporation":false,"usgs":false,"family":"Smith","given":"Douglas F.","affiliations":[],"preferred":false,"id":669417,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Becker, Kendall M.L.","contributorId":139180,"corporation":false,"usgs":false,"family":"Becker","given":"Kendall","email":"","middleInitial":"M.L.","affiliations":[{"id":12682,"text":"Utah State University, Logan, UT","active":true,"usgs":false}],"preferred":false,"id":669418,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Furniss, Tucker J.","contributorId":181754,"corporation":false,"usgs":false,"family":"Furniss","given":"Tucker","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":669419,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brooks, Matthew L. 0000-0002-3518-6787 mlbrooks@usgs.gov","orcid":"https://orcid.org/0000-0002-3518-6787","contributorId":393,"corporation":false,"usgs":true,"family":"Brooks","given":"Matthew","email":"mlbrooks@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":669413,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70175400,"text":"70175400 - 2017 - Source modeling of the 2015 Mw 7.8 Nepal (Gorkha) earthquake sequence: Implications for geodynamics and earthquake hazards","interactions":[],"lastModifiedDate":"2017-10-08T11:26:20","indexId":"70175400","displayToPublicDate":"2017-02-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3525,"text":"Tectonophysics","active":true,"publicationSubtype":{"id":10}},"title":"Source modeling of the 2015 Mw 7.8 Nepal (Gorkha) earthquake sequence: Implications for geodynamics and earthquake hazards","docAbstract":"The Gorkha earthquake on April 25th, 2015 was a long anticipated, low-angle thrust-faulting event on the shallow décollement between the India and Eurasia plates. We present a detailed multiple-event hypocenter relocation analysis of the Mw 7.8 Gorkha Nepal earthquake sequence, constrained by local seismic stations, and a geodetic rupture model based on InSAR and GPS data. We integrate these observations to place the Gorkha earthquake sequence into a seismotectonic context and evaluate potential earthquake hazard.
Major results from this study include (1) a comprehensive catalog of calibrated hypocenters for the Gorkha earthquake sequence; (2) the Gorkha earthquake ruptured a ~ 150 × 60 km patch of the Main Himalayan Thrust (MHT), the décollement defining the plate boundary at depth, over an area surrounding but predominantly north of the capital city of Kathmandu (3) the distribution of aftershock seismicity surrounds the mainshock maximum slip patch; (4) aftershocks occur at or below the mainshock rupture plane with depths generally increasing to the north beneath the higher Himalaya, possibly outlining a 10–15 km thick subduction channel between the overriding Eurasian and subducting Indian plates; (5) the largest Mw 7.3 aftershock and the highest concentration of aftershocks occurred to the southeast the mainshock rupture, on a segment of the MHT décollement that was positively stressed towards failure; (6) the near surface portion of the MHT south of Kathmandu shows no aftershocks or slip during the mainshock. Results from this study characterize the details of the Gorkha earthquake sequence and provide constraints on where earthquake hazard remains high, and thus where future, damaging earthquakes may occur in this densely populated region. Up-dip segments of the MHT should be considered to be high hazard for future damaging earthquakes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.tecto.2016.08.004","usgsCitation":"McNamara, D.E., Yeck, W.L., Barnhart, W.D., Schulte-Pelkum, V., Bergman, E., Adhikari, L.B., Dixit, A., Hough, S., Benz, H.M., and Earle, P.S., 2017, Source modeling of the 2015 Mw 7.8 Nepal (Gorkha) earthquake sequence: Implications for geodynamics and earthquake hazards: Tectonophysics, v. 714-715, p. 21-30, https://doi.org/10.1016/j.tecto.2016.08.004.","productDescription":"10 p.","startPage":"21","endPage":"30","ipdsId":"IP-078438","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":470069,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.tecto.2016.08.004","text":"Publisher Index Page"},{"id":335585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Nepal","otherGeospatial":"Gorkha","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 85,\n 26\n ],\n [\n 88,\n 26\n ],\n [\n 88,\n 30\n ],\n [\n 85,\n 30\n ],\n [\n 85,\n 26\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"714-715","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58a576bfe4b057081a24ed33","contributors":{"authors":[{"text":"McNamara, Daniel E. 0000-0001-6860-0350 mcnamara@usgs.gov","orcid":"https://orcid.org/0000-0001-6860-0350","contributorId":402,"corporation":false,"usgs":true,"family":"McNamara","given":"Daniel","email":"mcnamara@usgs.gov","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":645060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":645061,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnhart, William D. wbarnhart@usgs.gov","contributorId":5299,"corporation":false,"usgs":true,"family":"Barnhart","given":"William","email":"wbarnhart@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":645062,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schulte-Pelkum, V.","contributorId":173550,"corporation":false,"usgs":false,"family":"Schulte-Pelkum","given":"V.","affiliations":[{"id":6709,"text":"University of Colorado, Denver","active":true,"usgs":false}],"preferred":false,"id":669379,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bergman, E.","contributorId":84289,"corporation":false,"usgs":true,"family":"Bergman","given":"E.","affiliations":[],"preferred":false,"id":645064,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Adhikari, L. B.","contributorId":147569,"corporation":false,"usgs":false,"family":"Adhikari","given":"L.","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":669380,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dixit, Amod","contributorId":150708,"corporation":false,"usgs":false,"family":"Dixit","given":"Amod","email":"","affiliations":[{"id":18073,"text":"National Society for Earthquake Technology","active":true,"usgs":false}],"preferred":false,"id":669381,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hough, S. E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":7316,"corporation":false,"usgs":true,"family":"Hough","given":"S. E.","affiliations":[],"preferred":false,"id":669382,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Benz, Harley M. 0000-0002-6860-2134 benz@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-2134","contributorId":794,"corporation":false,"usgs":true,"family":"Benz","given":"Harley","email":"benz@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":645065,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Earle, Paul S. 0000-0002-3500-017X pearle@usgs.gov","orcid":"https://orcid.org/0000-0002-3500-017X","contributorId":173551,"corporation":false,"usgs":true,"family":"Earle","given":"Paul","email":"pearle@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":645066,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70191586,"text":"70191586 - 2017 - Lithospheric density structure beneath the Tarim basin and surroundings, northwestern China, from the joint inversion of gravity and topography","interactions":[],"lastModifiedDate":"2017-10-18T10:28:04","indexId":"70191586","displayToPublicDate":"2017-02-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Lithospheric density structure beneath the Tarim basin and surroundings, northwestern China, from the joint inversion of gravity and topography","docAbstract":"Intraplate strain generally focuses in discrete zones, but despite the profound impact of this partitioning on global tectonics, geodynamics, and seismic hazard, the processes by which deformation becomes localized are not well understood. Such heterogeneous intraplate strain is exemplified in central Asia, where the Indo-Eurasian collision has caused widespread deformation while the Tarim block has experienced minimal Cenozoic shortening. The apparent stability of Tarim may arise either because strain is dominantly accommodated by pre-existing faults in the continental suture zones that bound it—essentially discretizing Eurasia into microplates—or because the lithospheric-scale strength (i.e., viscosity) of the Tarim block is greater than its surroundings. Here, we jointly analyze seismic velocity, gravity, topography, and temperature to develop a 3-D density model of the crust and upper mantle in this region. The Tarim crust is characterized by high density, vs, vp, and vp/vs, consistent with a dominantly mafic composition and with the presence of an oceanic plateau beneath Tarim. Low-density but high-velocity mantle lithosphere beneath southern (southwestern) Tarim underlies a suite of Permian plume-related mafic intrusions and A-type granites sourced in previously depleted mantle lithosphere; we posit that this region was further depleted, dehydrated, and strengthened by Permian plume magmatism. The actively deforming western and southern margins of Tarim—the Tien Shan, Kunlun Shan, and Altyn Tagh fault—are underlain by buoyant upper mantle with low velocity; we hypothesize that this material has been hydrated by mantle-derived fluids that have preferentially migrated along Paleozoic continental sutures. Such hydrous material should be weak, and herein strain focuses there because of lithospheric-scale variations in rheology rather than the pre-existence of faults in the brittle crust. Thus this world-class example of strain partitioning arises not simply from the pre-existence of brittle faults but from the thermo-chemical and therefore rheological variations inherited from prior tectonism.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2016.10.051","usgsCitation":"Deng, Y., Levandowski, W.B., and Kusky, T., 2017, Lithospheric density structure beneath the Tarim basin and surroundings, northwestern China, from the joint inversion of gravity and topography: Earth and Planetary Science Letters, v. 460, p. 244-254, https://doi.org/10.1016/j.epsl.2016.10.051.","productDescription":"11 p.","startPage":"244","endPage":"254","ipdsId":"IP-081717","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":461733,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2016.10.051","text":"Publisher Index Page"},{"id":346829,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 76,\n 36\n ],\n [\n 92,\n 36\n ],\n [\n 92,\n 44\n ],\n [\n 76,\n 44\n ],\n [\n 76,\n 36\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"460","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59e86837e4b05fe04cd4d20a","contributors":{"authors":[{"text":"Deng, Yangfan","contributorId":197188,"corporation":false,"usgs":false,"family":"Deng","given":"Yangfan","email":"","affiliations":[],"preferred":false,"id":712816,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Levandowski, William Brower 0000-0003-4903-5012 wlevandowski@usgs.gov","orcid":"https://orcid.org/0000-0003-4903-5012","contributorId":5729,"corporation":false,"usgs":true,"family":"Levandowski","given":"William","email":"wlevandowski@usgs.gov","middleInitial":"Brower","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":712818,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kusky, Tim","contributorId":197189,"corporation":false,"usgs":false,"family":"Kusky","given":"Tim","email":"","affiliations":[],"preferred":false,"id":712817,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70181804,"text":"70181804 - 2017 - Book review: Physics of tsunamis","interactions":[],"lastModifiedDate":"2017-02-24T10:35:52","indexId":"70181804","displayToPublicDate":"2017-02-14T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3208,"text":"Pure and Applied Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Book review: Physics of tsunamis","docAbstract":"“Physics of Tsunamis”, second edition, provides a comprehensive analytical treatment of the hydrodynamics associated with the tsunami generation process. The book consists of seven chapters covering 388 pages. Because the subject matter within each chapter is distinct, an abstract appears at the beginning and references appear at the end of each chapter, rather than at the end of the book. Various topics of tsunami physics are examined largely from a theoretical perspective, although there is little information on how the physical descriptions are applied in numerical models.
“Physics of Tsunamis”, by B. W. Levin and M. A. Nosov, Second Edition, Springer, 2016; ISBN-10: 33-1933106X, ISBN-13: 978-331933-1065
","language":"English","publisher":"Springer","doi":"10.1007/s00024-017-1488-z","usgsCitation":"Geist, E.L., 2017, Book review: Physics of tsunamis: Pure and Applied Geophysics, v. 174, no. 3, p. 1521-1521, https://doi.org/10.1007/s00024-017-1488-z.","productDescription":"1 p.","startPage":"1521","endPage":"1521","ipdsId":"IP-082921","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":461745,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00024-017-1488-z","text":"Publisher Index Page"},{"id":335394,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"174","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-13","publicationStatus":"PW","scienceBaseUri":"58a42528e4b0c825128ad3de","contributors":{"authors":[{"text":"Geist, Eric L. 0000-0003-0611-1150 egeist@usgs.gov","orcid":"https://orcid.org/0000-0003-0611-1150","contributorId":1956,"corporation":false,"usgs":true,"family":"Geist","given":"Eric","email":"egeist@usgs.gov","middleInitial":"L.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":668644,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70179693,"text":"cir1426 - 2017 - Restoration handbook for sagebrush steppe ecosystems with emphasis on greater sage-grouse habitat—Part 3. Site level restoration decisions","interactions":[],"lastModifiedDate":"2018-03-08T09:38:31","indexId":"cir1426","displayToPublicDate":"2017-02-14T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1426","title":"Restoration handbook for sagebrush steppe ecosystems with emphasis on greater sage-grouse habitat—Part 3. Site level restoration decisions","docAbstract":"Sagebrush steppe ecosystems in the United States currently (2016) occur on only about one-half of their historical land area because of changes in land use, urban growth, and degradation of land, including invasions of non-native plants. The existence of many animal species depends on the existence of sagebrush steppe habitat. The greater sage-grouse (Centrocercus urophasianus) depends on large landscapes of intact habitat of sagebrush and perennial grasses for their existence. In addition, other sagebrush-obligate animals have similar requirements and restoration of landscapes for greater sage-grouse also will benefit these animals. Once sagebrush lands are degraded, they may require restoration actions to make those lands viable habitat for supporting sagebrush-obligate animals, livestock, and wild horses, and to provide ecosystem services for humans now and for future generations.
When a decision is made on where restoration treatments should be applied, there are a number of site-specific decisions managers face before selecting the appropriate type of restoration. This site-level decision tool for restoration of sagebrush steppe ecosystems is organized in nine steps.
Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
http://fresc.usgs.gov
Fishery independent gill net surveys provide valuable demographic information for population assessment and resource management, but relative to net construction, the effects of ancillary species, and environmental variables on focal species catch rates are poorly understood. In response, we conducted comparative deployments with three unique, inter-agency, survey gill nets used to assess walleye Sander vitreus in Lake Erie. We used an information-theoretic approach with Akaike’s second-order information criterion (AICc) to evaluate linear mixed models of walleye catch as a function of net type (multifilament and two types of monofilament netting), mesh size (categorical), Secchi depth, temperature, water depth, catch of ancillary species, and interactions among selected variables. The model with the greatest weight of evidence showed that walleye catches were positively associated with potential prey and intra-guild predators and negatively associated with water depth and temperature. In addition, the multifilament net had higher average walleye catches than either of the two monofilament nets. Results from this study both help inform decisions about proposed gear changes to stock assessment surveys in Lake Erie, and advance our understanding of how multispecies associations explain variation in gill net catches. Of broader interest to fishery-independent gill net studies, effects of abiotic variables and ancillary species on focal specie’s catch rates were small in comparison with net characteristics of mesh size or twine type.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2016.12.004","usgsCitation":"Kraus, R.T., Vandergoot, C., Kocovsky, P.M., Rogers, M.W., Cook, H., and Brenden, T.O., 2017, Reconciling catch differences from multiple fishery independent gill net surveys: Fisheries Research, v. 188, p. 17-22, https://doi.org/10.1016/j.fishres.2016.12.004.","productDescription":"6 p.","startPage":"17","endPage":"22","ipdsId":"IP-069874","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":470072,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.fishres.2016.12.004","text":"Publisher Index Page"},{"id":438437,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75D8Q1G","text":"USGS data release","linkHelpText":"Gill net catch data in Lake Erie, 2010-2013"},{"id":335324,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.5078125,\n 42.581399679665054\n ],\n [\n -80.71105957031249,\n 42.66628070564928\n ],\n [\n -80.947265625,\n 42.69051116998238\n ],\n [\n -81.3262939453125,\n 42.67839711889055\n ],\n [\n -81.6558837890625,\n 42.53689200787315\n ],\n [\n -82.37548828125,\n 42.17561739661684\n ],\n [\n -82.84790039062499,\n 42.020732852644294\n ],\n [\n -83.133544921875,\n 42.13082130188811\n ],\n [\n -83.4027099609375,\n 41.92680320648791\n ],\n [\n -83.5235595703125,\n 41.68932225997044\n ],\n [\n -83.045654296875,\n 41.43449030894922\n ],\n [\n -82.529296875,\n 41.36444153054222\n ],\n [\n -81.749267578125,\n 41.46742831254425\n ],\n [\n -80.5352783203125,\n 41.97174336327968\n ],\n [\n -80.5078125,\n 42.581399679665054\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"188","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58a4252be4b0c825128ad3ea","contributors":{"authors":[{"text":"Kraus, Richard T. 0000-0003-4494-1841 rkraus@usgs.gov","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":2609,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","email":"rkraus@usgs.gov","middleInitial":"T.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":668497,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vandergoot, Christopher 0000-0003-4128-3329 cvandergoot@usgs.gov","orcid":"https://orcid.org/0000-0003-4128-3329","contributorId":178356,"corporation":false,"usgs":true,"family":"Vandergoot","given":"Christopher","email":"cvandergoot@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":668498,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kocovsky, Patrick M. 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":3429,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","middleInitial":"M.","affiliations":[{"id":251,"text":"Ecosystems Mission Area","active":false,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":668499,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rogers, Mark W. 0000-0001-7205-5623 mwrogers@usgs.gov","orcid":"https://orcid.org/0000-0001-7205-5623","contributorId":4590,"corporation":false,"usgs":true,"family":"Rogers","given":"Mark","email":"mwrogers@usgs.gov","middleInitial":"W.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":668500,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cook, H. Andrew","contributorId":181530,"corporation":false,"usgs":false,"family":"Cook","given":"H. Andrew","affiliations":[{"id":16762,"text":"Ontario Ministry of Natural Resources and Forestry","active":true,"usgs":false}],"preferred":false,"id":668501,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brenden, Travis O.","contributorId":126759,"corporation":false,"usgs":false,"family":"Brenden","given":"Travis","email":"","middleInitial":"O.","affiliations":[{"id":6596,"text":"Quantitative Fisheries Center, Department of Fisheries and Wildlife Michigan State University","active":true,"usgs":false}],"preferred":false,"id":668502,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70181777,"text":"70181777 - 2017 - Asynchrony in the inter-annual recruitment of lake whitefish Coregonus clupeaformis in the Great Lakes region","interactions":[],"lastModifiedDate":"2017-03-22T14:49:10","indexId":"70181777","displayToPublicDate":"2017-02-14T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Asynchrony in the inter-annual recruitment of lake whitefish Coregonus clupeaformis in the Great Lakes region","docAbstract":"Spatially separated fish populations may display synchrony in annual recruitment if the factors that drive recruitment success, particularly abiotic factors such as temperature, are synchronised across broad spatial scales. We examined inter-annual variation in recruitment among lake whitefish (Coregonus clupeaformis) populations in lakes Huron, Michigan and Superior using fishery-dependent and -independent data from 1971 to 2014. Relative year-class strength (RYCS) was calculated from catch-curve residuals for each year class across multiple sampling years. Pairwise comparison of RYCS among datasets revealed no significant associations either within or between lakes, suggesting that recruitment of lake whitefish is spatially asynchronous. There was no consistent correlation between pairwise agreement and the distance between datasets, and models to estimate the spatial scale of recruitment synchrony did not fit well to these data. This suggests that inter-annual recruitment variation of lake whitefish is asynchronous across broad spatial scales in the Great Lakes. While our method primarily evaluated year-to-year recruitment variation, it is plausible that recruitment of lake whitefish varies at coarser temporal scales (e.g. decadal). Nonetheless, our findings differ from research on some other Coregonus species and suggest that local biotic or density-dependent factors may contribute strongly to lake whitefish recruitment rather than inter-annual variability in broad-scale abiotic factors.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2017.01.007","usgsCitation":"Zischke, M.T., Bunnell, D., Troy, C.D., Berglund, E.K., Caroffino, D.C., Ebener, M.P., He, J.X., Sitar, S.P., and Hook, T.O., 2017, Asynchrony in the inter-annual recruitment of lake whitefish Coregonus clupeaformis in the Great Lakes region: Journal of Great Lakes Research, v. 43, no. 2, p. 359-369, https://doi.org/10.1016/j.jglr.2017.01.007.","productDescription":"11 p.","startPage":"359","endPage":"369","ipdsId":"IP-070793","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":470073,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2017.01.007","text":"Publisher Index Page"},{"id":335328,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Great Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.82421875,\n 41.705728515237524\n ],\n [\n -79.6728515625,\n 41.705728515237524\n ],\n [\n -79.6728515625,\n 48.951366470947725\n ],\n [\n -89.82421875,\n 48.951366470947725\n ],\n [\n -89.82421875,\n 41.705728515237524\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58a4252be4b0c825128ad3ec","contributors":{"authors":[{"text":"Zischke, Mitchell T.","contributorId":181525,"corporation":false,"usgs":false,"family":"Zischke","given":"Mitchell","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":668489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bunnell, David B. 0000-0003-3521-7747 dbunnell@usgs.gov","orcid":"https://orcid.org/0000-0003-3521-7747","contributorId":169859,"corporation":false,"usgs":true,"family":"Bunnell","given":"David B.","email":"dbunnell@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":668488,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Troy, Cary D.","contributorId":169861,"corporation":false,"usgs":false,"family":"Troy","given":"Cary","email":"","middleInitial":"D.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":668490,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Berglund, Eric K.","contributorId":115926,"corporation":false,"usgs":false,"family":"Berglund","given":"Eric","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":668491,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Caroffino, David C.","contributorId":181527,"corporation":false,"usgs":false,"family":"Caroffino","given":"David","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":668492,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ebener, Mark P.","contributorId":25099,"corporation":false,"usgs":false,"family":"Ebener","given":"Mark","email":"","middleInitial":"P.","affiliations":[{"id":12957,"text":"Chippewa Ottawa Resource Authority","active":true,"usgs":false}],"preferred":false,"id":668493,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"He, Ji X.","contributorId":181528,"corporation":false,"usgs":false,"family":"He","given":"Ji","email":"","middleInitial":"X.","affiliations":[],"preferred":false,"id":668494,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sitar, Shawn P.","contributorId":181529,"corporation":false,"usgs":false,"family":"Sitar","given":"Shawn","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":668495,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hook, Tomas O.","contributorId":150480,"corporation":false,"usgs":false,"family":"Hook","given":"Tomas","email":"","middleInitial":"O.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":668496,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70179367,"text":"sir20165180 - 2017 - Hydrogeology and simulation of groundwater flow and analysis of projected water use for the Canadian River alluvial aquifer, western and central Oklahoma","interactions":[],"lastModifiedDate":"2017-03-27T13:31:09","indexId":"sir20165180","displayToPublicDate":"2017-02-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5180","title":"Hydrogeology and simulation of groundwater flow and analysis of projected water use for the Canadian River alluvial aquifer, western and central Oklahoma","docAbstract":"This report describes a study of the hydrogeology and simulation of groundwater flow for the Canadian River alluvial aquifer in western and central Oklahoma conducted by the U.S. Geological Survey in cooperation with the Oklahoma Water Resources Board. The report (1) quantifies the groundwater resources of the Canadian River alluvial aquifer by developing a conceptual model, (2) summarizes the general water quality of the Canadian River alluvial aquifer groundwater by using data collected during August and September 2013, (3) evaluates the effects of estimated equal proportionate share (EPS) on aquifer storage and streamflow for time periods of 20, 40, and 50 years into the future by using numerical groundwater-flow models, and (4) evaluates the effects of present-day groundwater pumping over a 50-year period and sustained hypothetical drought conditions over a 10-year period on stream base flow and groundwater in storage by using numerical flow models. The Canadian River alluvial aquifer is a Quaternary-age alluvial and terrace unit consisting of beds of clay, silt, sand, and fine gravel sediments unconformably overlying Tertiary-, Permian-, and Pennsylvanian-age sedimentary rocks. For groundwater-flow modeling purposes, the Canadian River was divided into Reach I, extending from the Texas border to the Canadian River at the Bridgeport, Okla., streamgage (07228500), and Reach II, extending downstream from the Canadian River at the Bridgeport, Okla., streamgage (07228500), to the confluence of the river with Eufaula Lake. The Canadian River alluvial aquifer spans multiple climate divisions, ranging from semiarid in the west to humid subtropical in the east. The average annual precipitation in the study area from 1896 to 2014 was 34.4 inches per year (in/yr).
A hydrogeologic framework of the Canadian River alluvial aquifer was developed that includes the areal and vertical extent of the aquifer and the distribution, texture variability, and hydraulic properties of aquifer materials. The aquifer areal extent ranged from less than 0.2 to
8.5 miles wide. The maximum aquifer thickness was 120 feet (ft), and the average aquifer thickness was 50 ft. Average horizontal hydraulic conductivity for the Canadian River alluvial aquifer was calculated to be 39 feet per day, and the maximum horizontal hydraulic conductivity was calculated to be 100 feet per day.
Recharge rates to the Canadian River alluvial aquifer were estimated by using a soil-water-balance code to estimate the spatial distribution of groundwater recharge and a water-table fluctuation method to estimate localized recharge rates. By using daily precipitation and temperature data from 39 climate stations, recharge was estimated to average 3.4 in/yr, which corresponds to 8.7 percent of precipitation as recharge for the Canadian River alluvial aquifer from 1981 to 2013. The water-table fluctuation method was used at one site where continuous water-level observation data were available to estimate the percentage of precipitation that becomes groundwater recharge. Estimated annual recharge at that site was 9.7 in/yr during 2014.
Groundwater flow in the Canadian River alluvial aquifer was identified and quantified by a conceptual flow model for the period 1981–2013. Inflows to the Canadian River alluvial aquifer include recharge to the water table from precipitation, lateral flow from the surrounding bedrock, and flow from the Canadian River, whereas outflows include flow to the Canadian River (base-flow gain), evapotranspiration, and groundwater use. Total annual recharge inflows estimated by the soil-water-balance code were multiplied by the area of each reach and then averaged over the simulated period to produce an annual average of 28,919 acre-feet per year (acre-ft/yr) for Reach I and 82,006 acre-ft/yr for Reach II. Stream base flow to the Canadian River was estimated to be the largest outflow of groundwater from the aquifer, measured at four streamgages, along with evapotranspiration and groundwater use, which were relatively minor discharge components.
Objectives for the numerical groundwater-flow models included simulating groundwater flow in the Canadian River alluvial aquifer from 1981 to 2013 to address groundwater use and drought scenarios, including calculation of the EPS pumping rates. The EPS for the alluvial and terrace aquifers is defined by the Oklahoma Water Resources Board as the amount of fresh water that each landowner is allowed per year per acre of owned land to maintain a saturated thickness of at least 5 ft in at least 50 percent of the overlying land of the groundwater basin for a minimum of 20 years.
The groundwater-flow models were calibrated to water-table altitude observations, streamgage base flows, and base-flow gain to the Canadian River. The Reach I water-table altitude observation root-mean-square error was 6.1 ft, and 75 percent of residuals were within ±6.7 ft of observed measurements. The average simulated stream base-flow residual at the Bridgeport streamgage (07228500) was 8.8 cubic feet per second (ft3/s), and 75 percent of residuals were within ±30 ft3/s of observed measurements. Simulated base-flow gain in Reach I was 8.8 ft3/s lower than estimated base-flow gain. The Reach II water-table altitude observation root-mean-square error was 4 ft, and 75 percent of residuals were within ±4.3 ft of the observations. The average simulated stream base-flow residual in Reach II was between 35 and 132 ft3/s. The average simulated base-flow gain residual in Reach II was between 11.3 and 61.1 ft3/s.
Several future predictive scenarios were run, including estimating the EPS pumping rate for 20-, 40-, and 50-year life of basin scenarios, determining the effects of current groundwater use over a 50-year period into the future, and evaluating the effects of a sustained drought on water availability for both reaches. The EPS pumping rate was determined to be 1.35 acre-feet per acre per year ([acre-ft/acre]/yr) in Reach I and 3.08 (acre-ft/acre)/yr in Reach II for a 20-year period. For the 40- and 50-year periods, the EPS rate was determined to be
1.34 (acre-ft/acre)/yr in Reach I and 3.08 (acre-ft/acre)/yr in Reach II. Storage changes decreased in tandem with simulated groundwater pumping and were minimal after the first 15 simulated years for Reach I and the first 8 simulated years for Reach II.
Groundwater pumping at year 2013 rates for a period of 50 years resulted in a 0.2-percent decrease in groundwater-storage volumes in Reach I and a 0.6-percent decrease in the groundwater-storage volumes in Reach II. The small changes in storage are due to groundwater use by pumping, which composes a small percentage of the total groundwater-flow model budgets for Reaches I and II.
A sustained drought scenario was used to evaluate the effects of a hypothetical 10-year drought on water availability. A 10-year period was chosen where the effects of drought conditions would be simulated by decreasing recharge by 75 percent. In Reach I, average simulated stream base flow at the Bridgeport streamgage (07228500) decreased by 58 percent during the hypothetical 10-year drought compared to average simulated stream base flow during the nondrought period. In Reach II, average simulated stream base flows at the Purcell streamgage (07229200) and Calvin streamgage (07231500) decreased by 64 percent and 54 percent, respectively. In Reach I, the groundwater-storage drought scenario resulted in a storage decline of 30 thousand acre-feet, or an average decline in the water table of
1.2 ft. In Reach II, the groundwater-storage drought scenario resulted in a storage decline of 71 thousand acre-feet, or an average decline in the water table of 2.0 ft.
Director, Oklahoma Water Science Center
U.S. Geological Survey
202 NW 66th, Bldg 7
Oklahoma City, OK 73116
Wildfire area is predicted to increase with global warming. Empirical statistical models and process-based simulations agree almost universally. The key relationship for this unanimity, observed at multiple spatial and temporal scales, is between drought and fire. Predictive models often focus on ecosystems in which this relationship appears to be particularly strong, such as mesic and arid forests and shrublands with substantial biomass such as chaparral. We examine the drought–fire relationship, specifically the correlations between water-balance deficit and annual area burned, across the full gradient of deficit in the western USA, from temperate rainforest to desert. In the middle of this gradient, conditional on vegetation (fuels), correlations are strong, but outside this range the equivalence hotter and drier equals more fire either breaks down or is contingent on other factors such as previous-year climate. This suggests that the regional drought–fire dynamic will not be stationary in future climate, nor will other more complex contingencies associated with the variation in fire extent. Predictions of future wildfire area therefore need to consider not only vegetation changes, as some dynamic vegetation models now do, but also potential changes in the drought–fire dynamic that will ensue in a warming climate.
","language":"English","publisher":"Ecological Society of America","publisherLocation":"Washington, D.C.","doi":"10.1002/eap.1420","usgsCitation":"McKenzie, D., and Littell, J.S., 2017, Climate change and the eco-hydrology of fire: Will area burned increase in a warming western USA?: Ecological Applications, v. 27, no. 1, p. 26-36, https://doi.org/10.1002/eap.1420.","productDescription":"11 p.","startPage":"26","endPage":"36","ipdsId":"IP-073768","costCenters":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"links":[{"id":335295,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Western United 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,{"id":70181754,"text":"70181754 - 2017 - Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models","interactions":[],"lastModifiedDate":"2017-02-13T13:10:20","indexId":"70181754","displayToPublicDate":"2017-02-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3464,"text":"Spectrochimica Acta Part B: Atomic Spectroscopy","active":true,"publicationSubtype":{"id":10}},"title":"Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models","docAbstract":"Accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response of an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “sub-model” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. The sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.sab.2016.12.002","usgsCitation":"Anderson, R.B., Clegg, S.M., Frydenvang, J., Wiens, R.C., McLennan, S.M., Morris, R., Ehlmann, B.L., and Dyar, M., 2017, Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models: Spectrochimica Acta Part B: Atomic Spectroscopy, v. 129, p. 49-57, https://doi.org/10.1016/j.sab.2016.12.002.","productDescription":"9 p.","startPage":"49","endPage":"57","ipdsId":"IP-070366","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":470074,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1693629","text":"Publisher Index Page"},{"id":335233,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"129","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58a2d3afe4b0c825128699ed","contributors":{"authors":[{"text":"Anderson, Ryan B. 0000-0003-4465-2871 rbanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-4465-2871","contributorId":170054,"corporation":false,"usgs":true,"family":"Anderson","given":"Ryan","email":"rbanderson@usgs.gov","middleInitial":"B.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":668374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clegg, Samuel M.","contributorId":23460,"corporation":false,"usgs":false,"family":"Clegg","given":"Samuel","email":"","middleInitial":"M.","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":668413,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frydenvang, Jens","contributorId":173225,"corporation":false,"usgs":false,"family":"Frydenvang","given":"Jens","email":"","affiliations":[{"id":27196,"text":"LANL","active":true,"usgs":false}],"preferred":false,"id":668414,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wiens, Roger C.","contributorId":140330,"corporation":false,"usgs":false,"family":"Wiens","given":"Roger","email":"","middleInitial":"C.","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":668415,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McLennan, Scott M.","contributorId":95388,"corporation":false,"usgs":true,"family":"McLennan","given":"Scott","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":668416,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Morris, Richard V.","contributorId":167513,"corporation":false,"usgs":false,"family":"Morris","given":"Richard V.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":668417,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ehlmann, Bethany L. 0000-0002-2745-3240","orcid":"https://orcid.org/0000-0002-2745-3240","contributorId":147154,"corporation":false,"usgs":false,"family":"Ehlmann","given":"Bethany","email":"","middleInitial":"L.","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":668418,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dyar, M. Darby","contributorId":14314,"corporation":false,"usgs":true,"family":"Dyar","given":"M. Darby","affiliations":[],"preferred":false,"id":668419,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70181755,"text":"70181755 - 2017 - Chemistry of diagenetic features analyzed by ChemCam at Pahrump Hills, Gale crater, Mars","interactions":[],"lastModifiedDate":"2017-02-13T12:32:38","indexId":"70181755","displayToPublicDate":"2017-02-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1963,"text":"Icarus","active":true,"publicationSubtype":{"id":10}},"title":"Chemistry of diagenetic features analyzed by ChemCam at Pahrump Hills, Gale crater, Mars","docAbstract":"The Curiosity rover's campaign at Pahrump Hills provides the first analyses of lower Mount Sharp strata. Here we report ChemCam elemental composition of a diverse assemblage of post-depositional features embedded in, or cross-cutting, the host rock. ChemCam results demonstrate their compositional diversity, especially compared to the surrounding host rock: (i) Dendritic aggregates and relief enhanced features, characterized by a magnesium enhancement and sulfur detection, and interpreted as Mg-sulfates; (ii) A localized observation that displays iron enrichment associated with sulfur, interpreted as Fe-sulfate; (iii) Dark raised ridges with varying Mg- and Ca-enriched compositions compared to host rock; (iv) Several dark-toned veins with calcium enhancement associated with fluorine detection, interpreted as fluorite veins. (v) Light-toned veins with enhanced calcium associated with sulfur detection, and interpreted as Ca-sulfates. The diversity of the Pahrump Hills diagenetic assemblage suggests a complex post-depositional history for fine-grained sediments for which the origin has been interpreted as fluvial and lacustrine. Assessment of the spatial and relative temporal distribution of these features shows that the Mg-sulfate features are predominant in the lower part of the section, suggesting local modification of the sediments by early diagenetic fluids. In contrast, light-toned Ca-sulfate veins occur in the whole section and cross-cut all other features. A relatively late stage shift in geochemical conditions could explain this observation. The Pahrump Hills diagenetic features have no equivalent compared to targets analyzed in other locations at Gale crater. Only the light-toned Ca-sulfate veins are present elsewhere, along Curiosity's path, suggesting they formed through a common late-stage process that occurred at over a broad area.
","language":"English","publisher":"American Astronomical Society. Division for Planetary Sciences","publisherLocation":"San Diego","doi":"10.1016/j.icarus.2016.08.026","usgsCitation":"Nachon, M., Mangold, N., Forni, O., Kah, L.C., Cousin, A., Wiens, R.C., Anderson, R.B., Blaney, D.L., Blank, J.G., Calef, F.J., Clegg, S.M., Fabre, C., Fisk, M.R., Gasnault, O., Grotzinger, J.P., Kronyak, R., Lanza, N.L., Lasue, J., Le Deit, L., Le Mouelic, S., Maurice, S., Meslin, P., Oehler, D.Z., Payre, V., Rapin, W., Schroder, S., Stack, K.M., and Sumner, D., 2017, Chemistry of diagenetic features analyzed by ChemCam at Pahrump Hills, Gale crater, Mars: Icarus, v. 281, p. 121-136, https://doi.org/10.1016/j.icarus.2016.08.026.","productDescription":"16 p.","startPage":"121","endPage":"136","ipdsId":"IP-070351","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":461753,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1412864","text":"External 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This assessment was based on a geologic model that incorporates the Upper Jurassic-Cretaceous-Tertiary Composite Total Petroleum System (TPS) of the Gulf of Mexico basin; the TPS was defined previously by the USGS assessment team in the assessment of undiscovered hydrocarbon resources in Tertiary strata of the Gulf Coast region in 2007. One conventional assessment unit (AU), which extends from south Texas to the Florida panhandle, was defined: the Fredericksburg-Buda Carbonate Platform-Reef Gas and Oil AU. The assessed stratigraphic interval includes the Edwards Limestone of the Fredericksburg Group and the Georgetown and Buda Limestones of the Washita Group. The following factors were evaluated to define the AU and estimate oil and gas resources: potential source rocks, hydrocarbon migration, reservoir porosity and permeability, traps and seals, structural features, paleoenvironments (back-reef lagoon, reef, and fore-reef environments), and the potential for water washing of hydrocarbons near outcrop areas.
In Texas and Louisiana, the downdip boundary of the AU was defined as a line that extends 10 miles downdip of the Lower Cretaceous shelf margin to include potential reef-talus hydrocarbon reservoirs. In Mississippi, Alabama, and the panhandle area of Florida, where the Lower Cretaceous shelf margin extends offshore, the downdip boundary was defined by the offshore boundary of State Waters. Updip boundaries of the AU were drawn based on the updip extent of carbonate rocks within the assessed interval, the presence of basin-margin fault zones, and the presence of producing wells. Other factors evaluated were the middle Cenomanian sea-level fall and erosion that removed large portions of platform and platform-margin carbonate sediments in the Washita Group of central Louisiana. The production history of discovered reservoirs and well data within the AU were examined to estimate the number and size of undiscovered oil and gas reservoirs within the AU. Using the USGS National Oil and Gas Assessment resource assessment methodology, mean volumes of 40 million barrels of oil, 622 billion cubic feet of gas, and 14 million barrels of natural gas liquids are the estimated technically recoverable undiscovered resources for the Fredericksburg-Buda Carbonate Platform-Reef Gas and Oil AU.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161199","usgsCitation":"Swanson, S.M., Enomoto, C.B., Dennen, K.O., Valentine, B.J., and Cahan, S.M., 2017, Geologic assessment of undiscovered oil and gas resources—Lower Cretaceous Albian to Upper Cretaceous Cenomanian carbonate rocks of the Fredericksburg and Washita Groups, United States Gulf of Mexico Coastal Plain and State Waters: U.S. Geological Survey Open-File Report 2016–1199, 69 p., https://doi.org/10.3133/ofr20161199.","productDescription":"Report: vii, 68 p.; Appendix 1: 2 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-064618","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":335075,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1199/ofr20161199.pdf","text":"Report","size":"10.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1199"},{"id":335074,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1199/coverthb.jpg"},{"id":335076,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1199/ofr20161199_appendix1.pdf","text":"Appendix 1 - ","size":"446 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"Input data form for the Fredericksburg-Buda Carbonate Platform-Reef Gas and Oil Assessment Unit (50490127)"}],"country":"United States","state":"Alabama, Arkansas, Florida, Louisiana, Mississippi, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.20898437499999,\n 37.996162679728116\n ],\n [\n -90.3515625,\n 37.75334401310656\n ],\n [\n -90.9228515625,\n 36.491973470593685\n ],\n [\n -92.021484375,\n 35.746512259918504\n ],\n [\n -93.1201171875,\n 35.209721645221386\n ],\n [\n -94.74609375,\n 34.994003757575776\n ],\n [\n -96.6357421875,\n 34.125447565116126\n ],\n [\n -97.55859375,\n 33.90689555128866\n ],\n [\n -98.9208984375,\n 33.687781758439364\n ],\n [\n -100.1513671875,\n 33.211116472416855\n ],\n [\n -101.4697265625,\n 32.24997445586331\n ],\n [\n -102.1728515625,\n 31.27855085894653\n ],\n [\n -101.953125,\n 30.221101852485987\n ],\n [\n -101.42578124999999,\n 29.611670115197377\n ],\n [\n -100.5908203125,\n 28.729130483430154\n ],\n [\n -99.84374999999999,\n 27.605670826465445\n ],\n [\n -99.404296875,\n 26.941659545381516\n ],\n [\n -99.00878906249999,\n 26.31311263768267\n ],\n [\n -98.7451171875,\n 26.194876675795218\n ],\n [\n -97.822265625,\n 25.878994400196202\n ],\n [\n -97.119140625,\n 25.878994400196202\n ],\n [\n -97.03125,\n 26.27371402440643\n ],\n [\n -97.03125,\n 27.0982539061379\n ],\n [\n -96.6796875,\n 27.916766641249065\n ],\n [\n -95.49316406249999,\n 28.459033019728043\n ],\n [\n -94.306640625,\n 29.075375179558346\n ],\n [\n -92.98828125,\n 29.11377539511439\n ],\n [\n -91.7138671875,\n 29.267232865200878\n ],\n [\n -90.87890625,\n 28.92163128242129\n ],\n [\n -89.8681640625,\n 28.806173508854776\n ],\n [\n -89.07714843749999,\n 28.806173508854776\n ],\n [\n -88.8134765625,\n 29.19053283229458\n ],\n [\n -88.9453125,\n 29.80251790576445\n ],\n [\n -88.5498046875,\n 30.14512718337613\n ],\n [\n -87.6708984375,\n 30.031055426540206\n ],\n [\n -86.8798828125,\n 30.031055426540206\n ],\n [\n -85.7373046875,\n 29.76437737516313\n ],\n [\n -85.1220703125,\n 29.38217507514529\n ],\n [\n -84.55078125,\n 29.57345707301757\n ],\n [\n -84.111328125,\n 29.57345707301757\n ],\n [\n -83.6279296875,\n 29.305561325527698\n ],\n [\n -83.27636718749999,\n 29.036960648558267\n ],\n [\n -82.8369140625,\n 29.38217507514529\n ],\n [\n -82.6171875,\n 30.44867367928756\n ],\n [\n -82.705078125,\n 31.690781806136822\n ],\n [\n -83.8037109375,\n 32.287132632616384\n ],\n [\n -85.1220703125,\n 33.211116472416855\n ],\n [\n -86.0009765625,\n 34.415973384481866\n ],\n [\n -87.099609375,\n 35.60371874069731\n ],\n [\n -87.6708984375,\n 37.26530995561875\n ],\n [\n -88.72558593749999,\n 37.78808138412046\n ],\n [\n -89.20898437499999,\n 37.996162679728116\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Energy Resources Science Center
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Reston, VA 20192
http://energy.usgs.gov/
The Water, Energy, and Biogeochemical Model (WEBMOD) uses the framework of the U.S. Geological Survey (USGS) Modular Modeling System to simulate fluxes of water and solutes through watersheds. WEBMOD divides watersheds into model response units (MRU) where fluxes and reactions are simulated for the following eight hillslope reservoir types: canopy; snowpack; ponding on impervious surfaces; O-horizon; two reservoirs in the unsaturated zone, which represent preferential flow and matrix flow; and two reservoirs in the saturated zone, which also represent preferential flow and matrix flow. The reservoir representing ponding on impervious surfaces, currently not functional (2016), will be implemented once the model is applied to urban areas. MRUs discharge to one or more stream reservoirs that flow to the outlet of the watershed. Hydrologic fluxes in the watershed are simulated by modules derived from the USGS Precipitation Runoff Modeling System; the National Weather Service Hydro-17 snow model; and a topography-driven hydrologic model (TOPMODEL). Modifications to the standard TOPMODEL include the addition of heterogeneous vertical infiltration rates; irrigation; lateral and vertical preferential flows through the unsaturated zone; pipe flow draining the saturated zone; gains and losses to regional aquifer systems; and the option to simulate baseflow discharge by using an exponential, parabolic, or linear decrease in transmissivity. PHREEQC, an aqueous geochemical model, is incorporated to simulate chemical reactions as waters evaporate, mix, and react within the various reservoirs of the model. The reactions that can be specified for a reservoir include equilibrium reactions among water; minerals; surfaces; exchangers; and kinetic reactions such as kinetic mineral dissolution or precipitation, biologically mediated reactions, and radioactive decay. WEBMOD also simulates variations in the concentrations of the stable isotopes deuterium and oxygen-18 as a result of varying inputs, mixing, and evaporation. This manual describes the WEBMOD input and output files, along with the algorithms and procedures used to simulate the hydrology and water quality in a watershed. Examples are presented that demonstrate hydrologic processes, weathering reactions, and isotopic evolution in an alpine watershed and the effect of irrigation on water flows and salinity in an intensively farmed agricultural area.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: Surface Water in Book 6: Modeling Techniques","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6B35","issn":"2328-7055","usgsCitation":"Webb, R.M.T., and Parkhurst, D.L., 2017, Water, Energy, and Biogeochemical Model (WEBMOD), user’s manual, version 1: U.S. Geological Survey Techniques and Methods, book 6, chap. B35, 171 p., https://doi.org/10.3133/tm6B35.","productDescription":"xiv, 171 p.","numberOfPages":"190","onlineOnly":"Y","costCenters":[{"id":144,"text":"Branch of Regional Research","active":false,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":438440,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P26W9K","text":"USGS data release","linkHelpText":"Water, Energy, and Biogeochemical Model"},{"id":334918,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/b35/tm6b35.pdf","text":"Report","size":"8.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 6-B35"},{"id":334917,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/b35/coverthb.jpg"},{"id":334980,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://doi.org/10.5066/F7P26W9K","text":"Water, Energy, and Biogeochemical Model (WEBMOD)"}],"publicComments":"This report is Chapter 35 of Section B: Surface Water in Book 6 Modeling Techniques","contact":"Chief, National Research Program, Central Branch
U.S. Geological Survey
Box 25585, Mail Stop 418
Denver, CO 80225-0585
Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
https://ca.water.usgs.gov
Demographic characteristics of bats are often insufficiently described for modeling populations. In data poor situations, experts are often relied upon for characterizing ecological systems. In concert with the development of a matrix model describing Indiana bat (Myotis sodalis) demography, we elicited estimates for parameterizing this model from 12 experts. We conducted this elicitation in two stages, requesting expert values for 12 demographic rates. These rates were adult and juvenile seasonal (winter, summer, fall) survival rates, pup survival in fall, and propensity and success at breeding. Experts were most in agreement about adult fall survival (3% Coefficient of Variation) and least in agreement about propensity of juveniles to breed (37% CV). The experts showed greater concordance for adult ( mean CV, adult = 6.2%) than for juvenile parameters ( mean CV, juvenile = 16.4%), and slightly more agreement for survival (mean CV, survival = 9.8%) compared to reproductive rates ( mean CV, reproduction = 15.1%). However, survival and reproduction were negatively and positively biased, respectively, relative to a stationary dynamic. Despite the species exhibiting near stationary dynamics for two decades prior to the onset of a potential extinction-causing agent, white-nose syndrome, expert estimates indicated a population decline of -11% per year (95% CI = -2%, -20%); quasi-extinction was predicted within a century ( mean = 61 years to QE, range = 32, 97) by 10 of the 12 experts. Were we to use these expert estimates in our modeling efforts, we would have errantly trained our models to a rapidly declining demography asymptomatic of recent demographic behavior. While experts are sometimes the only source of information, a clear understanding of the temporal and spatial context of the information being elicited is necessary to guard against wayward predictions.
","language":"English","publisher":"PeerJ","doi":"10.7287/peerj.preprints.2790v1","usgsCitation":"Thogmartin, W.E., Sanders-Reed, C., Szymanski, J., Pruitt, L., and Runge, M.C., 2017, Experts correctly describe demography associated with historical decline of the endangered Indiana bat, but not recent period of stationarity: PeerJ, v. 5, e2790v1, https://doi.org/10.7287/peerj.preprints.2790v1.","productDescription":"e2790v1","ipdsId":"IP-029961","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":461755,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.7287/peerj.preprints.2790v1","text":"External Repository"},{"id":338812,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58de194de4b02ff32c699c93","contributors":{"authors":[{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":687426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sanders-Reed, Carol A.","contributorId":86441,"corporation":false,"usgs":true,"family":"Sanders-Reed","given":"Carol A.","affiliations":[],"preferred":false,"id":687708,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Szymanski, Jennifer","contributorId":15123,"corporation":false,"usgs":false,"family":"Szymanski","given":"Jennifer","affiliations":[{"id":6969,"text":"U.S. Fish and Wildlife Service, Division of Endangered Species","active":true,"usgs":false}],"preferred":false,"id":687709,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pruitt, Lori","contributorId":17468,"corporation":false,"usgs":true,"family":"Pruitt","given":"Lori","email":"","affiliations":[],"preferred":false,"id":687710,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":687711,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70189688,"text":"70189688 - 2017 - Comparing stream-specific to generalized temperature models to guide salmonid management in a changing climate","interactions":[],"lastModifiedDate":"2018-04-24T13:34:59","indexId":"70189688","displayToPublicDate":"2017-02-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3278,"text":"Reviews in Fish Biology and Fisheries","active":true,"publicationSubtype":{"id":10}},"title":"Comparing stream-specific to generalized temperature models to guide salmonid management in a changing climate","docAbstract":"Global climate change is predicted to increase air and stream temperatures and alter thermal habitat suitability for growth and survival of coldwater fishes, including brook charr (Salvelinus fontinalis), brown trout (Salmo trutta), and rainbow trout (Oncorhynchus mykiss). In a changing climate, accurate stream temperature modeling is increasingly important for sustainable salmonid management throughout the world. However, finite resource availability (e.g. funding, personnel) drives a tradeoff between thermal model accuracy and efficiency (i.e. cost-effective applicability at management-relevant spatial extents). Using different projected climate change scenarios, we compared the accuracy and efficiency of stream-specific and generalized (i.e. region-specific) temperature models for coldwater salmonids within and outside the State of Michigan, USA, a region with long-term stream temperature data and productive coldwater fisheries. Projected stream temperature warming between 2016 and 2056 ranged from 0.1 to 3.8 °C in groundwater-dominated streams and 0.2–6.8 °C in surface-runoff dominated systems in the State of Michigan. Despite their generally lower accuracy in predicting exact stream temperatures, generalized models accurately projected salmonid thermal habitat suitability in 82% of groundwater-dominated streams, including those with brook charr (80% accuracy), brown trout (89% accuracy), and rainbow trout (75% accuracy). In contrast, generalized models predicted thermal habitat suitability in runoff-dominated streams with much lower accuracy (54%). These results suggest that, amidst climate change and constraints in resource availability, generalized models are appropriate to forecast thermal conditions in groundwater-dominated streams within and outside Michigan and inform regional-level salmonid management strategies that are practical for coldwater fisheries managers, policy makers, and the public. We recommend fisheries professionals reserve resource-intensive stream-specific models for runoff-dominated systems containing high-priority fisheries resources (e.g. trophy individuals, endangered species) that will be directly impacted by projected stream warming.","language":"English","publisher":"SpringerLink","doi":"10.1007/s11160-017-9467-0","usgsCitation":"Andrew K. Carlson, Taylor, W.W., Hartikainen, K.M., Dana M. Infante, Beard, and Lynch, A., 2017, Comparing stream-specific to generalized temperature models to guide salmonid management in a changing climate: Reviews in Fish Biology and Fisheries, v. 2, no. 27, p. 443-462, https://doi.org/10.1007/s11160-017-9467-0.","productDescription":"19 p. 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\"}}]}","volume":"2","issue":"27","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-07","publicationStatus":"PW","scienceBaseUri":"5971c1c2e4b0ec1a4885dacb","contributors":{"authors":[{"text":"Andrew K. Carlson","contributorId":194927,"corporation":false,"usgs":false,"family":"Andrew K. Carlson","affiliations":[],"preferred":false,"id":705820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taylor, William W.","contributorId":194928,"corporation":false,"usgs":false,"family":"Taylor","given":"William","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":705822,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hartikainen, Kelsey M.","contributorId":194935,"corporation":false,"usgs":false,"family":"Hartikainen","given":"Kelsey","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":705834,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dana M. Infante","contributorId":131166,"corporation":false,"usgs":false,"family":"Dana M. Infante","affiliations":[{"id":7266,"text":"Michigan State University, Department of Fisheries and Wildlife","active":true,"usgs":false}],"preferred":false,"id":705824,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Beard, Jr. 0000-0003-2632-2350 dbeard@usgs.gov","orcid":"https://orcid.org/0000-0003-2632-2350","contributorId":169459,"corporation":false,"usgs":true,"family":"Beard","suffix":"Jr.","email":"dbeard@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":705819,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lynch, Abigail 0000-0001-8449-8392 ajlynch@usgs.gov","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":169460,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","email":"ajlynch@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":705821,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70180013,"text":"sir20175005 - 2017 - Characterization of peak streamflows and flood inundation of selected areas in Louisiana from the August 2016 flood","interactions":[],"lastModifiedDate":"2017-02-08T12:22:30","indexId":"sir20175005","displayToPublicDate":"2017-02-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5005","title":"Characterization of peak streamflows and flood inundation of selected areas in Louisiana from the August 2016 flood","docAbstract":"Heavy rainfall occurred across Louisiana and southwestern Mississippi in August 2016 as a result of a slow-moving area of low pressure and a high amount of atmospheric moisture. The storm caused major flooding in the southern portions of Louisiana including areas surrounding Baton Rouge and Lafayette. Flooding occurred along the rivers such as the Amite, Comite, Tangipahoa, Tickfaw, Vermilion, and Mermentau Rivers. Over 31 inches of rain was reported in the city of Watson, 20 miles northeast of Baton Rouge, La., over the duration of the event. Streamflow-gaging stations operated by the U.S. Geological Survey (USGS) recorded peak streamflows of record at 10 locations, and 7 other locations experienced peak streamflows ranking in the top five for the duration of the period of record. In August 2016, USGS hydrographers made 50 discharge measurements at 21 locations on streams in Louisiana. Many of those discharge measurements were made for the purpose of verifying the accuracy of stage-streamflow relations at gaging stations operated by the USGS. Following the storm event, USGS hydrographers recovered and documented 590 high-water marks, noting location and height of the water above land surface. Many of these high-water marks were used to create 12 flood-inundation maps for selected communities of Louisiana that experienced flooding in August 2016. Digital datasets of the inundation area, modeling boundary, water depth rasters, and final map products are available online.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175005","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Watson, K.M., Storm, J.B., Breaker, B.K., and Rose, C.E., 2017, Characterization of peak streamflows and flood inundation of selected areas in Louisiana from the August 2016 flood: U.S. Geological Survey Scientific Investigations Report 2017–5005, 26 p., https://doi.org/10.3133/sir20175005.","productDescription":"Report: v, 26 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-081535","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":334119,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5005/sir20175005.pdf","text":"Report","size":"7.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5005"},{"id":334118,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5005/coverthb2.jpg"},{"id":334120,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79K48C1","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Flood Inundation Extent and Depth in Selected Areas of Louisiana in August 2016"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.3123779296875,\n 28.859107573773\n ],\n [\n -93.3123779296875,\n 31.475524020001806\n ],\n [\n -89.879150390625,\n 31.475524020001806\n ],\n [\n -89.879150390625,\n 28.859107573773\n ],\n [\n -93.3123779296875,\n 28.859107573773\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
401 Hardin Road
Little Rock, AR 72211
For this study, a methodology was developed for assessing impacts of wind energy generation on populations of birds and bats at regional to national scales. The approach combines existing methods in applied ecology for prioritizing species in terms of their potential risk from wind energy facilities and estimating impacts of fatalities on population status and trend caused by collisions with wind energy infrastructure. Methods include a qualitative prioritization approach, demographic models, and potential biological removal. The approach can be used to prioritize species in need of more thorough study as well as to identify species with minimal risk. However, the components of this methodology require simplifying assumptions and the data required may be unavailable or of poor quality for some species. These issues should be carefully considered before using the methodology. The approach will increase in value as more data become available and will broaden the understanding of anthropogenic sources of mortality on bird and bat populations.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Wind energy and wildlife interactions","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer International Publishing","doi":"10.1007/978-3-319-51272-3_4","usgsCitation":"Diffendorfer, J., Beston, J.A., Merrill, M., Stanton, J.C., Corum, M., Loss, S., Thogmartin, W.E., Johnson, D.H., Erickson, R.A., and Heist, K.W., 2017, A method to assess the population-level consequences of wind energy facilities on bird and bat species, chap. of Wind energy and wildlife interactions, p. 65-76, https://doi.org/10.1007/978-3-319-51272-3_4.","productDescription":"12 p.","startPage":"65","endPage":"76","ipdsId":"IP-073352","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":339790,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-03","publicationStatus":"PW","scienceBaseUri":"58f5d43de4b0f2e20545e405","contributors":{"authors":[{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":649358,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beston, Julie A. jbeston@usgs.gov","contributorId":5673,"corporation":false,"usgs":true,"family":"Beston","given":"Julie","email":"jbeston@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":649359,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Merrill, Matthew D. 0000-0003-3766-847X mmerrill@usgs.gov","orcid":"https://orcid.org/0000-0003-3766-847X","contributorId":145534,"corporation":false,"usgs":true,"family":"Merrill","given":"Matthew D.","email":"mmerrill@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":649360,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stanton, Jessica C. 0000-0002-6225-3703 jcstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-6225-3703","contributorId":5634,"corporation":false,"usgs":true,"family":"Stanton","given":"Jessica","email":"jcstanton@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":649361,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Corum, M.D. 0000-0002-9038-3935 mcorum@usgs.gov","orcid":"https://orcid.org/0000-0002-9038-3935","contributorId":2249,"corporation":false,"usgs":true,"family":"Corum","given":"M.D.","email":"mcorum@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":649362,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Loss, Scott R.","contributorId":140471,"corporation":false,"usgs":false,"family":"Loss","given":"Scott R.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":649363,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":649364,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, Douglas H. 0000-0002-7778-6641 douglas_h_johnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7778-6641","contributorId":1387,"corporation":false,"usgs":true,"family":"Johnson","given":"Douglas","email":"douglas_h_johnson@usgs.gov","middleInitial":"H.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":649365,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":649366,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Heist, Kevin W.","contributorId":83040,"corporation":false,"usgs":false,"family":"Heist","given":"Kevin","email":"","middleInitial":"W.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":649367,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70189233,"text":"70189233 - 2017 - Nonlethal laparoscopic detection of intersex (testicular oocytes) in largemouthbass (Micropterus salmoides) and smallmouth bass (Micropterus dolomieu)","interactions":[],"lastModifiedDate":"2017-07-06T13:13:48","indexId":"70189233","displayToPublicDate":"2017-02-03T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Nonlethal laparoscopic detection of intersex (testicular oocytes) in largemouthbass (Micropterus salmoides) and smallmouth bass (Micropterus dolomieu)","docAbstract":"Intersex in wild fish populations has received considerable attention in the scientific literature and public media. Conventional detection of testicular oocytes (TO), the presence of immature oocytes within testis of male fish, employs transverse sectioning of excised testis and is lethal. This present study used a non-lethal laparoscopic technique to collect biopsies of testis from black bass, entering the body cavity via the genital pore. Detection of TO was compared between biopsy and conventional methods using 79 smallmouth bass (SMB) Micropterus dolomieu from 8 sites and 68 largemouth bass (LMB) M. salmoides from 4 sites. Both methods performed similarly at sites where TO severity was moderate or high (6 of 8 SMB sites) while transverse sectioning resulted in superior TO detection at sites where severity was low (2 of 8 SMB sites and all 4 LMB sites). In SMB, TO prevalence by transverse and biopsy methods was strongly correlated across sites (r2 = 0.81) and severity reported by enumeration of TO was moderately correlated across sites (r2 = 0.59). Survival of a subset of LMB (n = 20) to 28-d after laparoscopic surgery was 90%. This research indicates that laparoscopy may be useful for monitoring the prevalence and severity of TO in Micropterus species, particularly when lethal sampling is precluded.","language":"English","publisher":"Wiley","doi":"10.1002/etc.3716","usgsCitation":"Blazer, V., Macleod, A.H., Matsche, M.A., and Yonkos, L.T., 2017, Nonlethal laparoscopic detection of intersex (testicular oocytes) in largemouthbass (Micropterus salmoides) and smallmouth bass (Micropterus dolomieu): Environmental Toxicology and Chemistry, v. 36, no. 7, p. 1924-1933, https://doi.org/10.1002/etc.3716.","productDescription":"10 p.","startPage":"1924","endPage":"1933","ipdsId":"IP-080410","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":343417,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"7","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-20","publicationStatus":"PW","scienceBaseUri":"595f4c3ce4b0d1f9f057e338","contributors":{"authors":[{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":703637,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Macleod, Alexander H","contributorId":194274,"corporation":false,"usgs":false,"family":"Macleod","given":"Alexander","email":"","middleInitial":"H","affiliations":[],"preferred":false,"id":703638,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Matsche, Mark A","contributorId":194275,"corporation":false,"usgs":false,"family":"Matsche","given":"Mark","email":"","middleInitial":"A","affiliations":[],"preferred":false,"id":703639,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yonkos, Lance T","contributorId":194276,"corporation":false,"usgs":false,"family":"Yonkos","given":"Lance","email":"","middleInitial":"T","affiliations":[],"preferred":false,"id":703640,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70200023,"text":"70200023 - 2017 - In situ assessment of lampricide toxicity to age-0 lake sturgeon","interactions":[],"lastModifiedDate":"2018-10-11T11:01:41","indexId":"70200023","displayToPublicDate":"2017-02-01T11:01:35","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"In situ assessment of lampricide toxicity to age-0 lake sturgeon","title":"In situ assessment of lampricide toxicity to age-0 lake sturgeon","docAbstract":"The lampricides 3-trifluoromethyl-4-nitrophenol (TFM) and 2′, 5-dichloro-4′-nitrosalicylanilide (niclosamide) are used to control sea lamprey (Petromyzon marinus), an invasive species in the Great Lakes. Age-0 lake sturgeon (Acipenser fulvescens), a species of conservationconcern, share similar stream habitats with larval sea lampreys and these streams can be targeted for lampricide applications on a 3- to 5-year cycle. Previous laboratory researchfound that lake sturgeon smaller than 100 mm could be susceptible to lampricide treatments. We conducted stream-side toxicity (bioassay) and in situ studies in conjunction with 10 lampricide applications in nine Great Lakes tributaries to determine whether sea lamprey treatments could result in in situ age-0 lake sturgeon mortality, and developed a logistic model to help predict lake sturgeon survival during future treatments. In the bioassays the observed concentrations where no lake sturgeon mortality occurred (no observable effect concentration, NOEC) were at or greater than the observed sea lamprey minimum lethal concentration (MLC or LC99) in 7 of 10 tests. We found that the mean in situ survival of age-0 lake sturgeon during 10 lampricide applications was 80%, with a range of 45–100% survival within streams. Modeling indicated that in age-0 lake sturgeon survival was negatively correlated with absolute TFM concentration and stream alkalinity, and positively correlated with stream pH and temperature. Overall survival was higher than expected based on previous research, and we expect that these data will help managers with decisions on the trade-offs between sea lamprey control and the effect on stream-specific populations of age-0 lake sturgeon.
We present a new methodology to combine spatially dense high-resolution airborne electromagnetic (AEM) data and sparse borehole information to construct multiple plausible geological structures using a stochastic approach. The method developed allows for quantification of the performance of groundwater models built from different geological realizations of structure. Multiple structural realizations are generated using geostatistical Monte Carlo simulations that treat sparse borehole lithological observations as hard data and dense geophysically derived structural probabilities as soft data. Each structural model is used to define 3-D hydrostratigraphical zones of a groundwater model, and the hydraulic parameter values of the zones are estimated by using nonlinear regression to fit hydrological data (hydraulic head and river discharge measurements). Use of the methodology is demonstrated for a synthetic domain having structures of categorical deposits consisting of sand, silt, or clay. It is shown that using dense AEM data with the methodology can significantly improve the estimated accuracy of the sediment distribution as compared to when borehole data are used alone. It is also shown that this use of AEM data can improve the predictive capability of a calibrated groundwater model that uses the geological structures as zones. However, such structural models will always contain errors because even with dense AEM data it is not possible to perfectly resolve the structures of a groundwater system. It is shown that when using such erroneous structures in a groundwater model, they can lead to biased parameter estimates and biased model predictions, therefore impairing the model's predictive capability.
","language":"English","publisher":"AGU","doi":"10.1002/2016WR019141","usgsCitation":"Christensen, N.K., Minsley, B.J., and Christensen, S., 2017, Generation of 3-D hydrostratigraphic zones from dense airborne electromagnetic data to assess groundwater model prediction error: Water Resources Research, v. 53, no. 2, p. 1019-1038, https://doi.org/10.1002/2016WR019141.","productDescription":"20 p.","startPage":"1019","endPage":"1038","ipdsId":"IP-081403","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":488731,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://pure.au.dk/portal/en/publications/dcdb9b5e-bf3c-4826-83aa-0fb5cd606845","text":"External Repository"},{"id":348146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59fc2ea5e4b0531197b27f85","contributors":{"authors":[{"text":"Christensen, Nikolaj K","contributorId":199736,"corporation":false,"usgs":false,"family":"Christensen","given":"Nikolaj","email":"","middleInitial":"K","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false}],"preferred":false,"id":719889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":719888,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Christensen, Steen","contributorId":199737,"corporation":false,"usgs":false,"family":"Christensen","given":"Steen","email":"","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false}],"preferred":false,"id":719890,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70193472,"text":"70193472 - 2017 - Factors influencing detection of the federally endangered Diamond Darter Crystallaria cincotta: Implications for long-term monitoring strategies","interactions":[],"lastModifiedDate":"2017-11-10T18:32:18","indexId":"70193472","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5153,"text":"The American Midland Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Factors influencing detection of the federally endangered Diamond Darter Crystallaria cincotta: Implications for long-term monitoring strategies","docAbstract":"Population monitoring is an essential component of endangered species recovery programs. The federally endangered Diamond Darter Crystallaria cincotta is in need of an effective monitoring design to improve our understanding of its distribution and track population trends. Because of their small size, cryptic coloration, and nocturnal behavior, along with limitations associated with current sampling methods, individuals are difficult to detect at known occupied sites. Therefore, research is needed to determine if survey efforts can be improved by increasing probability of individual detection. The primary objective of this study was to determine if there are seasonal and diel patterns in Diamond Darter detectability during population surveys. In addition to temporal factors, we also assessed five habitat variables that might influence individual detection. We used N-mixture models to estimate site abundances and relationships between covariates and individual detectability and ranked models using Akaike's information criteria. During 2015 three known occupied sites were sampled 15 times each between May and Oct. The best supported model included water temperature as a quadratic function influencing individual detectability, with temperatures around 22 C resulting in the highest detection probability. Detection probability when surveying at the optimal temperature was approximately 6% and 7.5% greater than when surveying at 16 C and 29 C, respectively. Time of Night and day of year were not strong predictors of Diamond Darter detectability. The results of this study will allow researchers and agencies to maximize detection probability when surveying populations, resulting in greater monitoring efficiency and likely more precise abundance estimates.
","language":"English","doi":"10.1674/0003-0031-178.1.123","usgsCitation":"Rizzo, A.A., Brown, D., Welsh, S., and Thompson, P., 2017, Factors influencing detection of the federally endangered Diamond Darter Crystallaria cincotta: Implications for long-term monitoring strategies: The American Midland Naturalist, v. 178, no. 1, p. 123-131, https://doi.org/10.1674/0003-0031-178.1.123.","productDescription":"9 p.","startPage":"123","endPage":"131","ipdsId":"IP-079169","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":348594,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"178","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a06c8d1e4b09af898c86146","contributors":{"authors":[{"text":"Rizzo, Austin A.","contributorId":191439,"corporation":false,"usgs":false,"family":"Rizzo","given":"Austin","email":"","middleInitial":"A.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":721636,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Donald J.","contributorId":191568,"corporation":false,"usgs":false,"family":"Brown","given":"Donald J.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":721637,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Welsh, Stuart A. 0000-0003-0362-054X swelsh@usgs.gov","orcid":"https://orcid.org/0000-0003-0362-054X","contributorId":152088,"corporation":false,"usgs":true,"family":"Welsh","given":"Stuart A.","email":"swelsh@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":721638,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Patricia A. pathompson@usgs.gov","contributorId":5249,"corporation":false,"usgs":true,"family":"Thompson","given":"Patricia A.","email":"pathompson@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":721639,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70195839,"text":"70195839 - 2017 - Modeled ecohydrological responses to climate change at seven small watersheds in the northeastern United States","interactions":[],"lastModifiedDate":"2018-03-06T11:11:17","indexId":"70195839","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Modeled ecohydrological responses to climate change at seven small watersheds in the northeastern United States","docAbstract":"A cross-site analysis was conducted on seven diverse, forested watersheds in the northeastern United States to evaluate hydrological responses (evapotranspiration, soil moisture, seasonal and annual streamflow, and water stress) to projections of future climate. We used output from four atmosphere–ocean general circulation models (AOGCMs; CCSM4, HadGEM2-CC, MIROC5, and MRI-CGCM3) included in Phase 5 of the Coupled Model Intercomparison Project, coupled with two Representative Concentration Pathways (RCP 8.5 and 4.5). The coarse resolution AOGCMs outputs were statistically downscaled using an asynchronous regional regression model to provide finer resolution future climate projections as inputs to the deterministic dynamic ecosystem model PnET-BGC. Simulation results indicated that projected warmer temperatures and longer growing seasons in the northeastern United States are anticipated to increase evapotranspiration across all sites, although invoking CO2 effects on vegetation (growth enhancement and increases in water use efficiency (WUE)) diminish this response. The model showed enhanced evapotranspiration resulted in drier growing season conditions across all sites and all scenarios in the future. Spruce-fir conifer forests have a lower optimum temperature for photosynthesis, making them more susceptible to temperature stress than more tolerant hardwood species, potentially giving hardwoods a competitive advantage in the future. However, some hardwood forests are projected to experience seasonal water stress, despite anticipated increases in precipitation, due to the higher temperatures, earlier loss of snow packs, longer growing seasons, and associated water deficits. Considering future CO2effects on WUE in the model alleviated water stress across all sites. Modeled streamflow responses were highly variable, with some sites showing significant increases in annual water yield, while others showed decreases. This variability in streamflow responses poses a challenge to water resource management in the northeastern United States. Our analyses suggest that dominant vegetation type and soil type are important attributes in determining future hydrological responses to climate change.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13444","usgsCitation":"Pourmokhtarian, A., Driscoll, C.T., Campbell, J.L., Hayhoe, K., Stoner, A., Adams, M.B., Burns, D., Fernandez, I., Mitchell, M.J., and Shanley, J.B., 2017, Modeled ecohydrological responses to climate change at seven small watersheds in the northeastern United States: Global Change Biology, v. 23, no. 2, p. 840-856, https://doi.org/10.1111/gcb.13444.","productDescription":"17 p.","startPage":"840","endPage":"856","ipdsId":"IP-077080","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":352254,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"2","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-25","publicationStatus":"PW","scienceBaseUri":"5afee8d3e4b0da30c1bfc4ba","contributors":{"authors":[{"text":"Pourmokhtarian, Afshin","contributorId":202944,"corporation":false,"usgs":false,"family":"Pourmokhtarian","given":"Afshin","email":"","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":730243,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Driscoll, Charles T.","contributorId":167460,"corporation":false,"usgs":false,"family":"Driscoll","given":"Charles","email":"","middleInitial":"T.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":730244,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Campbell, John L.","contributorId":178410,"corporation":false,"usgs":false,"family":"Campbell","given":"John","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":730245,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hayhoe, Katharine","contributorId":149192,"corporation":false,"usgs":false,"family":"Hayhoe","given":"Katharine","email":"","affiliations":[{"id":17667,"text":"Climate Science Center, Texas Tech University, Lubbock, Texas, United States","active":true,"usgs":false}],"preferred":false,"id":730246,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stoner, Anne M. K.","contributorId":202945,"corporation":false,"usgs":false,"family":"Stoner","given":"Anne M. K.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":730247,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Adams, Mary Beth","contributorId":150354,"corporation":false,"usgs":false,"family":"Adams","given":"Mary","email":"","middleInitial":"Beth","affiliations":[],"preferred":false,"id":730248,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Burns, Douglas A. 0000-0001-6516-2869","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":202943,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas A.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":730242,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fernandez, Ivan","contributorId":178215,"corporation":false,"usgs":false,"family":"Fernandez","given":"Ivan","affiliations":[],"preferred":false,"id":730249,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mitchell, Myron J.","contributorId":73734,"corporation":false,"usgs":true,"family":"Mitchell","given":"Myron","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":730250,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Shanley, James B. 0000-0002-4234-3437 jshanley@usgs.gov","orcid":"https://orcid.org/0000-0002-4234-3437","contributorId":1953,"corporation":false,"usgs":true,"family":"Shanley","given":"James","email":"jshanley@usgs.gov","middleInitial":"B.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":730241,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ]}