Most studies of the ecological effects of climate change consider only a limited number of weather drivers that could affect populations, though we know that multiple weather drivers can simultaneously affect population growth rate. Multiple drivers could simultaneously increase/decrease one vital rate, or one may increase a vital rate while another decreases the same vital rate. Considering the impact of multiple weather drivers on vital rates is particularly important in a changing climate, in which correlations among drivers may not be preserved in the future. We used a long-term dataset on the endangered red-cockaded woodpecker (Dryobates borealis) to understand how multiple weather drivers jointly affect survival and reproductive vital rates and then assessed the contributions of individual weather drivers to historical trends in vital rates over time. We found that vital rates were often influenced by more than one weather driver and that weather drivers most commonly exerted opposing effects. For instance, some weather drivers increased vital rates over time, while others acted in the opposite direction, decreasing vital rates over time. Importantly, the historical correlations among weather drivers are almost always projected to change in the future climate, such that future trends in vital rates may not match historical trends. For example, we do not find historical trends in adult survival, but changing correlations among weather drivers could generate future trends in this vital rate. Our work provides an example of how multiple weather drivers can control a variety of vital rates and also illustrates how changes in the correlation structure of weather drivers through time might substantially affect future trends in individual and population performance.
In regions where water resources are scarce and in high demand, it is important to safeguard against contamination of groundwater aquifers by oil-field fluids (water, gas, oil). In this context, the geochemical characterisation of these fluids is critical so that anthropogenic contaminants can be readily identified. The first step is characterising pre-development geochemical fluid signatures (i.e., those unmodified by hydrocarbon resource development) and understanding how these signatures may have been perturbed by resource production, particularly in the context of enhanced oil recovery (EOR) techniques. Here, we present noble gas isotope data in fluids produced from oil wells in several water-stressed regions in California, USA, where EOR is prevalent. In oil-field systems, only casing gases are typically collected and measured for their noble gas compositions, even when oil and/or water phases are present, due to the relative ease of gas analyses. However, this approach relies on a number of assumptions (e.g., equilibrium between phases, water-to-oil ratio (WOR) and gas-to-oil ratio (GOR) in order to reconstruct the multiphase subsurface compositions. Here, we adopt a novel, more rigorous approach, and measure noble gases in both casing gas and produced fluid (oil-water-gas mixtures) samples from the Lost Hills, Fruitvale, North and South Belridge (San Joaquin Basin, SJB) and Orcutt (Santa Maria Basin) Oil Fields. Using this method, we are able to fully characterise the distribution of noble gases within a multiphase hydrocarbon system. We find that measured concentrations in the casing gases agree with those in the gas phase in the produced fluids and thus the two sample types can be used essentially interchangeably.
EOR signatures can readily be identified by their distinct air-derived noble gas elemental ratios (e.g., 20Ne/36Ar), which are elevated compared to pre-development oil-field fluids, and conspicuously trend towards air values with respect to elemental ratios and overall concentrations. We reconstruct reservoir 20Ne/36Ar values using both casing gas and produced fluids and show that noble gas ratios in the reservoir are strongly correlated (r2 = 0.88–0.98) to the amount of water injected within ~500 m of a well. We suggest that the 20Ne/36Ar increase resulting from injection is sensitive to the volume of fluid interacting with the injectate, the effective water-to-oil ratio, and the composition of the injectate. Defining both the pre-development and injection-modified hydrocarbon reservoir compositions are crucial for distinguishing the sources of hydrocarbons observed in proximal groundwaters, and for quantifying the transport mechanisms controlling this occurrence.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemgeo.2021.120540","usgsCitation":"Tyne, R.L., Barry, P.H., Karolytė, R., Bryne, D.J., Kulongoski, J.T., Hillegonds, D., and Ballentine, C.J., 2021, Investigating the effect of enhanced oil recovery on the noble gas signature of casing gases and produced waters from selected California oil fields: Chemical Geology, v. 584, 120540, 10 p., https://doi.org/10.1016/j.chemgeo.2021.120540.","productDescription":"120540, 10 p.","ipdsId":"IP-126638","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":450655,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.chemgeo.2021.120540","text":"Publisher Index Page"},{"id":393752,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Kern County","otherGeospatial":"Fruitvale, Lost Hills and North and South Belridge Oil Fields","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.73150634765625,\n 34.4069096565206\n ],\n [\n -119.24011230468749,\n 34.4069096565206\n ],\n [\n -119.24011230468749,\n 35.85566574217861\n ],\n [\n -120.73150634765625,\n 35.85566574217861\n ],\n [\n -120.73150634765625,\n 34.4069096565206\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"584","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tyne, R. L.","contributorId":205891,"corporation":false,"usgs":false,"family":"Tyne","given":"R.","email":"","middleInitial":"L.","affiliations":[{"id":37187,"text":"Department of Earth Sciences, University of Oxford, Oxford, UK","active":true,"usgs":false}],"preferred":false,"id":829842,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barry, P. H.","contributorId":270728,"corporation":false,"usgs":false,"family":"Barry","given":"P.","email":"","middleInitial":"H.","affiliations":[{"id":56200,"text":"Dept. of Marine Chem. and Geochem., Woods Hole Oceanographic Institution, Woods Hole, MA, USA","active":true,"usgs":false}],"preferred":false,"id":829843,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karolytė, R.","contributorId":270729,"corporation":false,"usgs":false,"family":"Karolytė","given":"R.","affiliations":[{"id":56201,"text":"Dept. of Earth Sci., University of Oxford, Oxford, UK","active":true,"usgs":false}],"preferred":false,"id":829844,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bryne, D. J.","contributorId":270730,"corporation":false,"usgs":false,"family":"Bryne","given":"D.","email":"","middleInitial":"J.","affiliations":[{"id":56201,"text":"Dept. of Earth Sci., University of Oxford, Oxford, UK","active":true,"usgs":false}],"preferred":false,"id":829845,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154 kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":173457,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin","email":"kulongos@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829846,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hillegonds, D.J.","contributorId":205892,"corporation":false,"usgs":false,"family":"Hillegonds","given":"D.J.","email":"","affiliations":[{"id":37187,"text":"Department of Earth Sciences, University of Oxford, Oxford, UK","active":true,"usgs":false}],"preferred":false,"id":829847,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ballentine, C. J.","contributorId":224737,"corporation":false,"usgs":false,"family":"Ballentine","given":"C.","email":"","middleInitial":"J.","affiliations":[{"id":40928,"text":"Oxford University","active":true,"usgs":false}],"preferred":false,"id":829848,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70224530,"text":"70224530 - 2021 - A simplified method for rapid estimation of emergency water supply needs after earthquakes","interactions":[],"lastModifiedDate":"2022-12-23T17:20:52.03908","indexId":"70224530","displayToPublicDate":"2021-09-25T09:53:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"A simplified method for rapid estimation of emergency water supply needs after earthquakes","docAbstract":"Researchers are investigating the problem of estimating households with potable water service outages soon after an earthquake. Most of these modeling approaches are computationally intensive, have large proprietary data collection requirements or lack precision, making them unfeasible for rapid assessment, prioritization, and allocation of emergency water resources in large, complex disasters. This study proposes a new simplified analytical method—performed without proprietary water pipeline data—to estimate water supply needs after earthquakes, and a case study of its application in the HayWired earthquake scenario. In the HayWired scenario—a moment magnitude (Mw) 7.0 Hayward Fault earthquake in the San Francisco Bay Area, California (USA)—an analysis of potable water supply in two water utility districts was performed using the University of Colorado Water Network (CUWNet) model. In the case study, application of the simplified method extends these estimates of household water service outage to the nine counties adjacent to the San Francisco Bay, aggregated by a ~250 m2 (nine-arcsecond) grid. The study estimates about 1.38 million households (3.7 million residents) out of 7.6 million residents (2017, ambient, nighttime population) with potable water service outage soon after the earthquake—about an 8% increase from the HayWired scenario estimates.
","language":"English","publisher":"MDPI","doi":"10.3390/w13192635","usgsCitation":"Toland, J.C., and Wein, A., 2021, A simplified method for rapid estimation of emergency water supply needs after earthquakes: Water, v. 13, 2635, 27 p., https://doi.org/10.3390/w13192635.","productDescription":"2635, 27 p.","ipdsId":"IP-132813","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":450658,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w13192635","text":"Publisher Index Page"},{"id":389813,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.79968261718749,\n 37.24782120155428\n ],\n [\n -121.55273437499999,\n 37.24782120155428\n ],\n [\n -121.55273437499999,\n 38.324420427006544\n ],\n [\n -122.79968261718749,\n 38.324420427006544\n ],\n [\n -122.79968261718749,\n 37.24782120155428\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationDate":"2021-09-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Toland, Joseph Charles 0000-0002-0092-0320","orcid":"https://orcid.org/0000-0002-0092-0320","contributorId":265976,"corporation":false,"usgs":true,"family":"Toland","given":"Joseph","email":"","middleInitial":"Charles","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":823911,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wein, Anne 0000-0002-5516-3697 awein@usgs.gov","orcid":"https://orcid.org/0000-0002-5516-3697","contributorId":589,"corporation":false,"usgs":true,"family":"Wein","given":"Anne","email":"awein@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":823912,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70224317,"text":"fs20213044 - 2021 - Managing water resources on Long Island, New York, with integrated, multidisciplinary science","interactions":[],"lastModifiedDate":"2021-09-27T12:11:24.513816","indexId":"fs20213044","displayToPublicDate":"2021-09-24T14:10:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-3044","displayTitle":"Managing Water Resources on Long Island, New York, with Integrated, Multidisciplinary Science","title":"Managing water resources on Long Island, New York, with integrated, multidisciplinary science","docAbstract":"Nutrients, harmful algal blooms, and synthetic chemicals like per- and polyfluoroalkyl substances (PFAS) and 1,4-dioxane threaten Long Island’s water resources by affecting the quality of drinking water and ecologically sensitive habitats that support the diverse wildlife throughout the island. Understanding the occurrence, fate, and transport of these potentially harmful chemicals is critical to protect these vital resources. The U.S. Geological Survey (USGS) is collecting and analyzing data to support informed water-resource management decisions. This fact sheet introduces ongoing efforts and future areas of study aimed to help water professionals develop a comprehensive science strategy to address contamination of the Long Island aquifer system, the sole source of drinking water for nearly 3 million people. These studies include surface and groundwater collection and groundwater flow modeling. Funding for the data collection has been provided by the USGS, New York State Department of Environmental Conservation, New York City Department of Environmental Protection, Suffolk County Water Authority, Nassau County Department of Public Works, State and local agencies, and Tribal and Federal partners. Without the foresight and long-term commitment of these funding partners, evaluating sustainability and planning for future water needs would not be possible.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213044","usgsCitation":"Breault, R.F., Masterson, J.P., Schubert, C.E., and Herdman, L.M., 2021, Managing water resources on Long Island, New York, with integrated, multidisciplinary science: U.S. Geological Survey Fact Sheet 2021–3044, 4 p., https://doi.org/10.3133/fs20213044.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-131602","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":389579,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3044/fs20213044.pdf","text":"Report","size":"14.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021-3044"},{"id":389578,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3044/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.0478515625,\n 40.538851525354666\n ],\n [\n -73.7677001953125,\n 40.538851525354666\n ],\n [\n -73.1304931640625,\n 40.60561205826018\n ],\n [\n -72.5537109375,\n 40.76806170936614\n ],\n [\n -71.9549560546875,\n 40.97575093157534\n ],\n [\n -71.83959960937499,\n 41.10832999732831\n ],\n [\n -72.1417236328125,\n 41.24064190269475\n ],\n [\n -72.8338623046875,\n 41.10005163093046\n ],\n [\n -73.5205078125,\n 40.96330795307353\n ],\n [\n -73.883056640625,\n 40.83043687764923\n ],\n [\n -74.06982421875,\n 40.713955826286046\n ],\n [\n -74.0863037109375,\n 40.61812224225511\n ],\n [\n -74.0478515625,\n 40.538851525354666\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
This field trip highlights the results of recent U.S. Geological Survey (USGS) bedrock geologic mapping in four 7.5 min quadrangles in the western Connecticut highlands near Southbury, Connecticut, USA. The rocks are broadly within what Rodgers (1985) called the Hartland and Gneiss Dome belts of the Connecticut Valley Synclinorium (Rodgers, 1985; Fig. 1), the latter of which is now known as the Connecticut Valley–Gaspe Trough (Hibbard et al., 2006). The mapping occurred over two intervals: 2003–2005 and 2016–present. In the first, the goal was a detailed map of the early Mesozoic Pomperaug basin, which overlaps the four quadrangles. Portions of the basin had been separately mapped during the statewide 7.5 min quadrangle mapping campaign spanning the 1950s–1970s, resulting in an inaccurate depiction of the basin on the 1985 state geologic map (Rodgers, 1985). A new map of the basin was proposed in part to benefit an ongoing project by the USGS Connecticut Water Science Center to determine the contributions of natural and artificial contaminants to a public water-supply well in Woodbury, Connecticut, under the National Water-Quality Assessment (NAWQA) Program (Starn and Brown, 2007). The NAWQA project was partially funded by the Pomperaug River Watershed Coalition, which also provided logistical support to the geologic mapping project. Mapping of the basin was also a high priority for the Connecticut State Geologist at the time (Ralph Lewis, 2002, pers. comm.). The mapping resulted in an NEIGC (New England Intercollegiate Geologic Conference) field guide (Burton et al., 2005) and a 1:12,000-scale USGS open-file map (Burton, 2006) and was funded by the USGS National Cooperative Geologic Mapping Program (NCGMP).
The second phase of the mapping began in 2016 after the discovery of elevated levels of uranium and arsenic in domestic water wells in the igneous and metamorphic rocks that surround the sedimentary and volcanic rocks of the Pomperaug basin (Flanagan and Brown, 2017). Structural measurements in the surrounding crystalline rocks were made during Pomperaug basin mapping to better understand the tectonic setting, but a revision of the crystalline map units on Rodgers’ 1985 geologic map was not attempted. Nonetheless, discrepancies were noted between the new mapping and the 1985 map, particularly within the two northern quadrangles of Woodbury and Roxbury, which were originally mapped by Gates (1954) and Gates (1959), respectively. Based on these discrepancies, a new NCGMP project was proposed to remap the Woodbury and Roxbury 7.5 min quadrangles, commencing in the fall of 2016. The expected USGS product will be a two-quadrangle, 1:24,000-scale Scientific Investigations Map (SIM).
This field guide is not meant as a comprehensive review of all of the geologic research done in this area of Connecticut; rather, it looks at previous bedrock geologic mapping from the perspective of new and recent mapping in the four-quadrangle area and discusses the structural, stratigraphic, and nomenclatural revisions necessary for the next revision of the state geologic map.
Raikoke, a small, unmonitored volcano in the Kuril Islands, erupted in June 2019. We integrate data from satellites (including Sentinel-2, TROPOMI, MODIS, Himawari-8), the International Monitoring System (IMS) infrasound network, and global lightning detection network (GLD360) with information from local authorities and social media to retrospectively characterize the eruptive sequence and improve understanding of the pre-, syn- and post- eruptive behavior. We observe six infrasound pulses beginning on 21 June at 17:49:55 UTC as well as the main Plinian phase on 21 June at 22:29 UTC. Each pulse is tracked in space and time using lightning and satellite imagery as the plumes drift eastward. Post-eruption visible satellite imagery shows expansion of the island's surface area, an increase in crater size, and a possibly-linked algal bloom south of the island. We use thermal satellite imagery and plume modeling to estimate plume height at 10–12 km asl and 1.5–2 × 106 kg/s mass eruption rate. Remote infrasound data provide insight into syn-eruptive changes in eruption intensity. Our analysis illustrates the value of interdisciplinary analyses of remote data to illuminate eruptive processes. However, our inability to identify deformation, pre-eruptive outgassing, and thermal signals, which may reflect the relatively short duration (~12 h) of the eruption and minimal land area around the volcano and/or the character of closed-system eruptions, highlights current limitations in the application of remote sensing for eruption detection and characterization.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2021.107354","usgsCitation":"McKee, K., Smith, C.M., Reath, K., Snee, E., Maher, S., Matoza, R.S., Carn, S.A., Mastin, L.G., Anderson, K.R., Damby, D., Roman, D., Degterev, A., Rybin, A., Chibisova, M., Assink, J.D., de Negri Levia, R., and Perttu, A., 2021, Evaluating the state-of-the-art in remote volcanic eruption characterization Part I: Raikoke volcano, Kuril Islands: Journal of Volcanology and Geothermal Research, v. 419, p. 1-14, https://doi.org/10.1016/j.jvolgeores.2021.107354.","productDescription":"107354, 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-131053","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":450671,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2021.107354","text":"Publisher Index Page"},{"id":389731,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Japan, Russia","state":"Hokkaido, Sakhalin Oblast","otherGeospatial":"Kuril Islands, Raikoke Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -206.73900604248047,\n 48.283078663405014\n ],\n [\n -206.7290496826172,\n 48.291531147204644\n ],\n [\n -206.73248291015625,\n 48.29963964777105\n ],\n [\n -206.74072265625,\n 48.30226606899445\n ],\n [\n -206.7620086669922,\n 48.30215187957729\n ],\n [\n -206.76630020141602,\n 48.29872607827854\n ],\n [\n -206.7688751220703,\n 48.292901688435585\n ],\n [\n -206.7688751220703,\n 48.29107429195301\n ],\n [\n -206.7614936828613,\n 48.28364982124273\n ],\n [\n -206.75445556640625,\n 48.28227903170258\n ],\n [\n -206.73900604248047,\n 48.283078663405014\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"419","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McKee, Kathleen 0000-0003-3189-9189","orcid":"https://orcid.org/0000-0003-3189-9189","contributorId":265977,"corporation":false,"usgs":false,"family":"McKee","given":"Kathleen","email":"","affiliations":[{"id":54848,"text":"Earth and Planets Laboratory, Carnegie Institution for Science, Washington, DC, USA","active":true,"usgs":false}],"preferred":false,"id":823913,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Cassandra Marie 0000-0003-2653-4249 cassandrasmith@usgs.gov","orcid":"https://orcid.org/0000-0003-2653-4249","contributorId":257000,"corporation":false,"usgs":true,"family":"Smith","given":"Cassandra","email":"cassandrasmith@usgs.gov","middleInitial":"Marie","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":823914,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reath, Kevin","contributorId":194091,"corporation":false,"usgs":false,"family":"Reath","given":"Kevin","email":"","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":823915,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Snee, Eveanjelene 0000-0002-3660-4020","orcid":"https://orcid.org/0000-0002-3660-4020","contributorId":265978,"corporation":false,"usgs":false,"family":"Snee","given":"Eveanjelene","email":"","affiliations":[{"id":54849,"text":"School of Earth and Ocean Sciences, Cardiff University, Cardiff, Wales, UK","active":true,"usgs":false}],"preferred":false,"id":823916,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maher, Sean","contributorId":265979,"corporation":false,"usgs":false,"family":"Maher","given":"Sean","affiliations":[{"id":54850,"text":"Department of Earth Science and Earth Research Institute, University of California, Santa Barbara, Santa Barbara, CA, USA","active":true,"usgs":false}],"preferred":false,"id":823917,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Matoza, Robin S.","contributorId":257265,"corporation":false,"usgs":false,"family":"Matoza","given":"Robin","email":"","middleInitial":"S.","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":823918,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carn, Simon A","contributorId":191165,"corporation":false,"usgs":false,"family":"Carn","given":"Simon","email":"","middleInitial":"A","affiliations":[],"preferred":false,"id":823919,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mastin, Larry G. 0000-0002-4795-1992 lgmastin@usgs.gov","orcid":"https://orcid.org/0000-0002-4795-1992","contributorId":555,"corporation":false,"usgs":true,"family":"Mastin","given":"Larry","email":"lgmastin@usgs.gov","middleInitial":"G.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":823920,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":823921,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Damby, David 0000-0002-3238-3961","orcid":"https://orcid.org/0000-0002-3238-3961","contributorId":206614,"corporation":false,"usgs":true,"family":"Damby","given":"David","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":823922,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Roman, Diana","contributorId":237832,"corporation":false,"usgs":false,"family":"Roman","given":"Diana","affiliations":[{"id":47620,"text":"Dept. of Terrestrial Magnetism, Carnegie Institution for Science, Washington DC 20015","active":true,"usgs":false}],"preferred":false,"id":823923,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Degterev, Artem 0000-0001-6284-8830","orcid":"https://orcid.org/0000-0001-6284-8830","contributorId":265980,"corporation":false,"usgs":false,"family":"Degterev","given":"Artem","email":"","affiliations":[{"id":54851,"text":"Sakhalin Volcanic Eruptions Response Team (SVERT), Institute of Marine Geology and Geophysics, Yuzhno-Sakhalinsk, Russia","active":true,"usgs":false}],"preferred":false,"id":823924,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Rybin, Alexander 0000-0002-7734-0172","orcid":"https://orcid.org/0000-0002-7734-0172","contributorId":265981,"corporation":false,"usgs":false,"family":"Rybin","given":"Alexander","email":"","affiliations":[{"id":54851,"text":"Sakhalin Volcanic Eruptions Response Team (SVERT), Institute of Marine Geology and Geophysics, Yuzhno-Sakhalinsk, Russia","active":true,"usgs":false}],"preferred":false,"id":823925,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Chibisova, Marina 0000-0003-0677-6945","orcid":"https://orcid.org/0000-0003-0677-6945","contributorId":265982,"corporation":false,"usgs":false,"family":"Chibisova","given":"Marina","email":"","affiliations":[{"id":54851,"text":"Sakhalin Volcanic Eruptions Response Team (SVERT), Institute of Marine Geology and Geophysics, Yuzhno-Sakhalinsk, Russia","active":true,"usgs":false}],"preferred":false,"id":823926,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Assink, Jelle D.","contributorId":236650,"corporation":false,"usgs":false,"family":"Assink","given":"Jelle","email":"","middleInitial":"D.","affiliations":[{"id":47493,"text":"R and D Seismology and Acoustics, Royal Netherlands Meteorological Institute (KNMI), Utrechtseweg 297, 3731 GA De Bilt, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":823927,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"de Negri Levia, Rodrigo 0000-0003-1283-2579","orcid":"https://orcid.org/0000-0003-1283-2579","contributorId":265983,"corporation":false,"usgs":false,"family":"de Negri Levia","given":"Rodrigo","email":"","affiliations":[{"id":54852,"text":"Department of Earth Science and Earth Research Institute, University of California, Santa Barbara, Santa Barbara, CA, USA; NDC-CTBT of the Chilean Nuclear Energy Commission, Chile","active":true,"usgs":false}],"preferred":false,"id":823928,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Perttu, Anna 0000-0003-3590-1549","orcid":"https://orcid.org/0000-0003-3590-1549","contributorId":265984,"corporation":false,"usgs":false,"family":"Perttu","given":"Anna","email":"","affiliations":[{"id":48937,"text":"Earth Observatory of Singapore, Nanyang Technological University, Singapore","active":true,"usgs":false}],"preferred":false,"id":823929,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70223871,"text":"sir20215015 - 2021 - Methods for estimating regional skewness of annual peak flows in parts of eastern New York and Pennsylvania, based on data through water year 2013","interactions":[],"lastModifiedDate":"2021-09-27T12:03:37.39516","indexId":"sir20215015","displayToPublicDate":"2021-09-24T09:50:00","publicationYear":"2021","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":"2021-5015","displayTitle":"Methods for Estimating Regional Skewness of Annual Peak Flows in Parts of Eastern New York and Pennsylvania, Based on Data Through Water Year 2013","title":"Methods for estimating regional skewness of annual peak flows in parts of eastern New York and Pennsylvania, based on data through water year 2013","docAbstract":"Bulletin 17C (B17C) recommends fitting the log-Pearson Type III (LP−III) distribution to a series of annual peak flows at a streamgage by using the method of moments. The third moment, the skewness coefficient (or skew), is important because the magnitudes of annual exceedance probability (AEP) flows estimated by using the LP–III distribution are affected by the skew; interest is focused on the right-hand tail of the distribution, which represents the larger annual peak flows that correspond to small AEPs. For streamgages having modest record lengths, the skew is sensitive to extreme events like large floods, which cause a sample to be highly asymmetrical or “skewed.” For this reason, B17C recommends using a weighted-average skew computed from the skew of the annual peak flows for a given streamgage and a regional skew. This report presents an estimate of regional skew for a study area encompassing parts of eastern New York and Pennsylvania. A total of 232 candidate U.S. Geological Survey streamgages that were unaffected by extensive regulation, diversion, urbanization, or channelization were considered for use in the skew analysis; after screening for redundancy and pseudo record length (PRL) of at least 36 years, 183 streamgages were selected for use in the study.
Flood frequencies for candidate streamgages were analyzed by employing the expected moments algorithm, which extends the method of moments so that it can accommodate interval, censored, and historical/paleo flow data, as well as the multiple Grubbs-Beck test to identify potentially influential low floods in the data series. Bayesian weighted least squares/Bayesian generalized least squares regression was used to develop a regional skew model for the study area that would incorporate possible variables (basin characteristics) to explain the variation in skew in the study area. Ten basin characteristics were considered as possible explanatory variables; however, none produced a pseudo coefficient of determination greater than 1 percent; as a result, these characteristics did not help to explain the variation in skew in the study area. Therefore, a constant model that had a regional skew coefficient of 0.32 and an average variance of prediction at a new streamgage (AVPnew, which corresponds to the mean square error [MSE] of 0.11) was selected. The AVPnew corresponds to an effective record length of 68 years, a marked improvement over the Bulletin 17B national skew map, whose reported MSE of 0.302 indicated a corresponding effective record length of only 17 years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215015","usgsCitation":"Veilleux, A.G., and Wagner, D.M., 2021, Methods for estimating regional skewness of annual peak flows in parts of eastern New York and Pennsylvania, based on data through water year 2013: U.S. Geological Survey Scientific Investigations Report 2021–5015, 38 p., https://doi.org/10.3133/sir20215015.","productDescription":"Report: vi, 38 p.; Data Release","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-114558","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":389079,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5015/coverthb.jpg"},{"id":389080,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5015/sir20215015.pdf","text":"Report","size":"6.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5015"},{"id":389081,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PGAL0D","text":"USGS data release","linkHelpText":"Regional flood skew for parts of the mid-Atlantic region (hydrologic unit 02) in eastern New York and Pennsylvania"}],"country":"United States","state":"New York, Pennsylvania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.56396484375,\n 39.740986355883564\n ],\n [\n -75.03662109375,\n 40.04443758460856\n ],\n [\n -74.86083984375,\n 40.34654412118006\n ],\n [\n -75.16845703124999,\n 40.713955826286046\n ],\n [\n -74.970703125,\n 41.062786068733026\n ],\n [\n -74.619140625,\n 41.393294288784865\n ],\n [\n -74.1796875,\n 41.65649719441145\n ],\n [\n -73.54248046875,\n 42.17968819665961\n ],\n [\n -73.23486328124999,\n 43.02071359427862\n ],\n [\n -73.32275390625,\n 43.628123412124616\n ],\n [\n -73.45458984375,\n 43.77109381775651\n ],\n [\n -73.80615234375,\n 43.35713822211053\n ],\n [\n -74.28955078125,\n 43.14909399920127\n ],\n [\n -74.77294921875,\n 42.79540065303723\n ],\n [\n -75.34423828125,\n 42.73087427928485\n ],\n [\n -75.82763671875,\n 42.68243539838623\n ],\n [\n -76.35498046875,\n 42.68243539838623\n ],\n [\n -76.88232421875,\n 42.68243539838623\n ],\n [\n -77.23388671874999,\n 42.45588764197166\n ],\n [\n -77.607421875,\n 42.19596877629178\n ],\n [\n -77.607421875,\n 42.01665183556825\n ],\n [\n -77.6513671875,\n 41.409775832009565\n ],\n [\n -77.80517578125,\n 41.1290213474951\n ],\n [\n -77.9150390625,\n 40.53050177574321\n ],\n [\n -78.11279296875,\n 40.16208338164617\n ],\n [\n -78.46435546875,\n 39.67337039176558\n ],\n [\n -75.56396484375,\n 39.740986355883564\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Integrated Modeling and Prediction Division
Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Geodetically modeled reservoir volume changes during volcanic eruptions are commonly much smaller than the observed eruptive volumes. This discrepancy is thought to be partially due to the compressibility of magma, which is largely controlled by the presence of exsolved volatiles. The 2006 eruption of Augustine Volcano, Alaska, produced an eruptive volume that was ∼3 times larger than the geodetically estimated syn-eruptive subsurface volume change. In this study, we use a multistep methodology that combines constraints from geodetic, volcanic gas, geologic, and petrologic data together with equations relating physical processes to observable parameters. We apply a Monte Carlo approach to quantify uncertainties. Ultimately, we solve for the exsolved volatile volume fraction and the magma compressibility. We estimate Augustine's 2006 pre-eruptive exsolved volatile phase to be ∼5.5 vol% of the magma at storage depths, yielding a bulk magma compressibility of ∼3.8 × 10−10 Pa−1. We develop a novel approach to estimate the H2O/CO2 ratio of the syn-eruptive gas emissions in the absence of direct H2O emission measurements which are hard to obtain due to the high background levels in ambient air. We find a best-fit H2O/CO2 molar ratio of 29. We also investigate the effects of applying different equations of state to our model. We find that the Ideal Gas Law might be used as a first approximation due to its simplicity; however, it overestimates volatile density and compressibility significantly at storage depths. This project capitalizes on the insights that can be gained by integrating multidisciplinary data with models of physical processes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021GC009911","usgsCitation":"Wasser, V.K., Lopez, T., Anderson, K.R., Izbekov, P.E., and Freymueller, J., 2021, Multidisciplinary constraints on magma compressibility, the pre-eruptive exsolved volatile fraction, and the H2O/CO2 molar ratio for the 2006 Augustine eruption, Alaska: Geochemistry, Geophysics, Geosystems (G-Cubed), v. 22, no. 9, p. 1-24, https://doi.org/10.1029/2021GC009911.","productDescription":"e2021GC009911, 24 p.","startPage":"1","endPage":"24","ipdsId":"IP-116941","costCenters":[{"id":153,"text":"California Volcano Observatory","active":false,"usgs":true}],"links":[{"id":489770,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021gc009911","text":"Publisher Index Page"},{"id":389721,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Augustine Volcano, Cook Inlet","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.38150024414062,\n 59.404209722248545\n ],\n [\n -153.38356018066406,\n 59.411198752750096\n ],\n [\n -153.4295654296875,\n 59.417836996163324\n ],\n [\n -153.46939086914062,\n 59.40840331358838\n ],\n [\n -153.47488403320312,\n 59.39966607911177\n ],\n [\n -153.51470947265625,\n 59.395471406154286\n ],\n [\n -153.52500915527344,\n 59.38987770116238\n ],\n [\n -153.5504150390625,\n 59.39652012307085\n ],\n [\n -153.56483459472653,\n 59.39337387496283\n ],\n [\n -153.57650756835938,\n 59.38218484937568\n ],\n [\n -153.58200073242188,\n 59.37379065648717\n ],\n [\n -153.577880859375,\n 59.36924293446236\n ],\n [\n -153.55934143066406,\n 59.36644403325385\n ],\n [\n -153.5504150390625,\n 59.345795005122895\n ],\n [\n -153.55865478515625,\n 59.33564087770051\n ],\n [\n -153.5504150390625,\n 59.32968704671643\n ],\n [\n -153.52157592773438,\n 59.3167251017617\n ],\n [\n -153.42613220214844,\n 59.32443279994899\n ],\n [\n -153.41720581054688,\n 59.32022881759866\n ],\n [\n -153.39797973632812,\n 59.32583401185328\n ],\n [\n -153.358154296875,\n 59.33669144545403\n ],\n [\n -153.336181640625,\n 59.35734600959442\n ],\n [\n -153.34304809570312,\n 59.380785961506966\n ],\n [\n -153.37051391601562,\n 59.39092659116679\n ],\n [\n -153.38150024414062,\n 59.404209722248545\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"22","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Wasser, Valerie K.","contributorId":265989,"corporation":false,"usgs":false,"family":"Wasser","given":"Valerie","email":"","middleInitial":"K.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":823947,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lopez, Taryn M.","contributorId":265990,"corporation":false,"usgs":false,"family":"Lopez","given":"Taryn M.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":823948,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":823949,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Izbekov, Pavel E.","contributorId":265991,"corporation":false,"usgs":false,"family":"Izbekov","given":"Pavel","email":"","middleInitial":"E.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":823950,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Freymueller, Jeffrey T.","contributorId":96841,"corporation":false,"usgs":false,"family":"Freymueller","given":"Jeffrey T.","affiliations":[{"id":26875,"text":"Michigan State University, East Lansing, MI","active":true,"usgs":false}],"preferred":false,"id":823951,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224546,"text":"70224546 - 2021 - The Biscuit Brook and Neversink Reservoir Watersheds: Long-term investigations of stream chemistry, soil chemistry, and aquatic ecology in the Catskill Mountains, New York, USA, 1983 to 2020","interactions":[],"lastModifiedDate":"2021-10-18T15:08:18.866454","indexId":"70224546","displayToPublicDate":"2021-09-24T08:50:09","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"The Biscuit Brook and Neversink Reservoir Watersheds: Long-term investigations of stream chemistry, soil chemistry, and aquatic ecology in the Catskill Mountains, New York, USA, 1983 to 2020","docAbstract":"This data note describes the Biscuit Brook and Neversink Reservoir watershed Long-Term Monitoring Data that includes: 1) stream discharge, (1983 – 2020 for Biscuit Brook and 1937 – 2020 for the Neversink Reservoir watershed), 2) stream water chemistry, 1983-2020, at 4 stations, 3) fish survey data from 16 locations in the watershed 1990-2019, 4) soil chemistry data from 2 headwater sub-watersheds, 1993-2012, and 5) periodic stream water chemistry sampling data from 364 locations throughout the watershed, 1983-2020. The Neversink Reservoir watershed in the Catskill Mountains of New York, USA drains an area of 172.5 km2. The watershed feeds one of 6 reservoirs in New York City's West of Hudson water supply, which accounts for about 90% of the city's water supply. Biscuit Brook is a 9.63 km2 tributary sub-watershed within the Neversink Reservoir watershed.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14394","usgsCitation":"Murdoch, P.S., Burns, D., McHale, M., Siemion, J., Baldigo, B., Lawrence, G.B., George, S.D., Antidormi, M.R., and Bonville, D.B., 2021, The Biscuit Brook and Neversink Reservoir Watersheds: Long-term investigations of stream chemistry, soil chemistry, and aquatic ecology in the Catskill Mountains, New York, USA, 1983 to 2020: Hydrological Processes, v. 35, e14394, 12 p., https://doi.org/10.1002/hyp.14394.","productDescription":"e14394, 12 p.","ipdsId":"IP-126065","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":450676,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.14394","text":"Publisher Index Page"},{"id":389807,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Biscuit Brook and Neversink Reservoir Watersheds","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.62188720703125,\n 41.840920397579936\n ],\n [\n -74.60403442382812,\n 41.85319643776675\n ],\n [\n -74.53811645507812,\n 41.881831370505594\n ],\n [\n -74.44129943847656,\n 41.92629234083705\n ],\n [\n -74.28337097167969,\n 42.007978804701\n ],\n [\n -74.278564453125,\n 42.06509700139039\n ],\n [\n -74.30671691894531,\n 42.11095834849246\n ],\n [\n -74.3341827392578,\n 42.13133052651052\n ],\n [\n -74.4049072265625,\n 42.132858175814626\n ],\n [\n -74.44747924804688,\n 42.11707068963613\n ],\n [\n -74.48867797851562,\n 42.042153895364\n ],\n [\n -74.57725524902344,\n 41.984504674276074\n ],\n [\n -74.652099609375,\n 41.9528519300999\n ],\n [\n -74.73518371582031,\n 41.89869952106346\n ],\n [\n -74.74273681640625,\n 41.86291329896065\n ],\n [\n -74.70497131347656,\n 41.81636125072054\n ],\n [\n -74.64866638183594,\n 41.81175536180908\n ],\n [\n -74.62188720703125,\n 41.840920397579936\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","noUsgsAuthors":false,"publicationDate":"2021-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Murdoch, Peter S. 0000-0001-9243-505X pmurdoch@usgs.gov","orcid":"https://orcid.org/0000-0001-9243-505X","contributorId":2453,"corporation":false,"usgs":true,"family":"Murdoch","given":"Peter","email":"pmurdoch@usgs.gov","middleInitial":"S.","affiliations":[{"id":5067,"text":"Northeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":824014,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":824015,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McHale, Michael 0000-0003-3780-1816 mmchale@usgs.gov","orcid":"https://orcid.org/0000-0003-3780-1816","contributorId":177292,"corporation":false,"usgs":true,"family":"McHale","given":"Michael","email":"mmchale@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824016,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Siemion, Jason 0000-0001-5635-6469 jsiemion@usgs.gov","orcid":"https://orcid.org/0000-0001-5635-6469","contributorId":127562,"corporation":false,"usgs":true,"family":"Siemion","given":"Jason","email":"jsiemion@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824017,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baldigo, Barry P. 0000-0002-9862-9119","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":25174,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824018,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lawrence, Gregory B. 0000-0002-8035-2350 glawrenc@usgs.gov","orcid":"https://orcid.org/0000-0002-8035-2350","contributorId":867,"corporation":false,"usgs":true,"family":"Lawrence","given":"Gregory","email":"glawrenc@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824019,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824020,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Antidormi, Michael R. 0000-0002-3967-1173 mantidormi@usgs.gov","orcid":"https://orcid.org/0000-0002-3967-1173","contributorId":150722,"corporation":false,"usgs":true,"family":"Antidormi","given":"Michael","email":"mantidormi@usgs.gov","middleInitial":"R.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824021,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bonville, Donald B. 0000-0003-4480-9381","orcid":"https://orcid.org/0000-0003-4480-9381","contributorId":248849,"corporation":false,"usgs":true,"family":"Bonville","given":"Donald","email":"","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824022,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70244091,"text":"70244091 - 2021 - Evaluating the impact of watershed development and climate change on stream ecosystems: A Bayesian network modeling approach","interactions":[],"lastModifiedDate":"2023-06-01T14:04:48.814131","indexId":"70244091","displayToPublicDate":"2021-09-24T08:41:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the impact of watershed development and climate change on stream ecosystems: A Bayesian network modeling approach","docAbstract":"A continuous-variable Bayesian network (cBN) model is used to link watershed development and climate change to stream ecosystem indicators. A graphical model, reflecting our understanding of the connections between climate change, weather condition, loss of natural land cover, stream flow characteristics, and stream ecosystem indicators is used as the basis for selecting flow metrics for predicting macroinvertebrate-based indicators. Selected flow metrics were then linked to variables representing watershed development and climate change. We fit the model to data from two river basins in southeast US and the resulting model was used to simulate future stream ecological conditions using projected future climate and development scenarios. The three climate models predicted varying ecological condition trajectories, but similar worst-case ecological conditions. The established modeling approach couples mechanistic understanding with field data to develop predictions of management-relevant variables across a heterogeneous landscape. We discussed the transferability of the modeling approach.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2021.117685","usgsCitation":"Qian, S.S., Kennen, J., May, J., Freeman, M., and Cuffney, T.F., 2021, Evaluating the impact of watershed development and climate change on stream ecosystems: A Bayesian network modeling approach: Water Research, v. 205, 117685, 11 p., https://doi.org/10.1016/j.watres.2021.117685.","productDescription":"117685, 11 p.","ipdsId":"IP-125255","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":450679,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2021.117685","text":"Publisher Index Page"},{"id":417645,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, South Carolina, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.52179800853753,\n 32.86310990611119\n ],\n [\n -78.95424437482033,\n 33.20732442214225\n ],\n [\n -78.83061042748197,\n 33.62402639579081\n ],\n [\n -78.03123021414571,\n 33.81848745598903\n ],\n [\n -77.49960057459164,\n 34.229007941502374\n ],\n [\n -77.20942075405912,\n 34.57053494448374\n ],\n [\n -78.04925882655441,\n 35.593623760054015\n ],\n [\n -79.61149509648914,\n 36.3847977489354\n ],\n [\n -80.69994996842892,\n 36.980415621762745\n ],\n [\n -81.2368093995407,\n 36.697683304342775\n ],\n [\n -81.66006417146134,\n 35.81807327161229\n ],\n [\n -80.92321025597303,\n 33.67553987083947\n ],\n [\n -80.06864524456462,\n 32.587876038945694\n ],\n [\n -79.52179800853753,\n 32.86310990611119\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"205","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Qian, Song S. 0000-0002-2346-4903","orcid":"https://orcid.org/0000-0002-2346-4903","contributorId":306033,"corporation":false,"usgs":false,"family":"Qian","given":"Song","email":"","middleInitial":"S.","affiliations":[{"id":62440,"text":"Department of Environmental Sciences, University of Toledo, Toledo, OH 43606","active":true,"usgs":false}],"preferred":false,"id":874463,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kennen, Jonathan G. 0000-0002-5426-4445 jgkennen@usgs.gov","orcid":"https://orcid.org/0000-0002-5426-4445","contributorId":574,"corporation":false,"usgs":true,"family":"Kennen","given":"Jonathan G.","email":"jgkennen@usgs.gov","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":874464,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"May, Jason 0000-0002-5699-2112","orcid":"https://orcid.org/0000-0002-5699-2112","contributorId":224991,"corporation":false,"usgs":false,"family":"May","given":"Jason","affiliations":[{"id":41015,"text":"Deceased (ex-USGS)","active":true,"usgs":false}],"preferred":false,"id":874465,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":874466,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cuffney, Thomas F 0000-0003-1164-5560","orcid":"https://orcid.org/0000-0003-1164-5560","contributorId":306032,"corporation":false,"usgs":false,"family":"Cuffney","given":"Thomas","email":"","middleInitial":"F","affiliations":[],"preferred":false,"id":874467,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224522,"text":"ofr20201138 - 2021 - Historical streamflow and stage data compilation for the Lower Columbia River, Pacific Northwest","interactions":[],"lastModifiedDate":"2021-09-27T12:07:04.69961","indexId":"ofr20201138","displayToPublicDate":"2021-09-24T07:39:36","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1138","displayTitle":"Historical Streamflow and Stage Data Compilation for the Lower Columbia River, Pacific Northwest","title":"Historical streamflow and stage data compilation for the Lower Columbia River, Pacific Northwest","docAbstract":"The U.S. Geological Survey mined data from a variety of national and state agencies including USGS, Oregon Water Resources Department, National Oceanic and Atmospheric Administration, Washington Department of Ecology, Pacific Northwest National Laboratory, Portland State University, and U.S. Army Corps of Engineers. A comprehensive dataset of streamflow, stage, and tidal elevations for the Lower Columbia River basin was compiled. Data were compiled from gaging stations in Oregon and Washington along the Columbia River from Astoria to The Dalles and along the Willamette River from Salem to Portland. Tidal gages along the Washington, Oregon, and California coasts were also compiled. Seasonal maximum values were calculated for both streamflow and stage for the winter (November–March) and spring (April–July) flow seasons, as well as for the full water year when underlying data were available. The aggregated datasets are available at https://doi.org/10.5066/P9R6RT0Z.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201138","collaboration":"Prepared in cooperation with U.S. Army Corps of Engineers","usgsCitation":"Boudreau, C.L., Stewart, M.A., and Stonewall, A.J., 2021, Historical streamflow and stage data compilation for the Lower Columbia River, Pacific Northwest: U.S. Geological Survey Open-File Report 2020–1138, 50 p., https://doi.org/10.3133/ofr20201138.","productDescription":"Report: viii, 50 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-101122","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":389696,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1138/coverthb.jpg"},{"id":389697,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1138/ofr20201138.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1138"},{"id":389698,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R6RT0Z","text":"USGS data release","description":"USGS Data release","linkHelpText":"Historical streamflow and stage data for the lower Columbia River basin and the coasts of Washington, Oregon, and northern California"}],"country":"United States","state":"California, Oregon, Washington","otherGeospatial":"Lower Columbia River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.63964843750001,\n 41.672911819602085\n ],\n [\n -120.80566406250001,\n 41.672911819602085\n ],\n [\n -120.80566406250001,\n 49.26780455063753\n ],\n [\n -125.63964843750001,\n 49.26780455063753\n ],\n [\n -125.63964843750001,\n 41.672911819602085\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Lakes and other surface fresh waterbodies provide drinking water, recreational and economic opportunities, food, and other critical support for humans, aquatic life, and ecosystem health. Lakes are also productive ecosystems that provide habitats and influence global cycles. Chlorophyll concentration provides a common metric of water quality, and is frequently used as a proxy for lake trophic state. Here, we document the generation and distribution of the complete MEdium Resolution Imaging Spectrometer (MERIS; Appendix A provides a complete list of abbreviations) radiometric time series for over 2300 satellite resolvable inland bodies of water across the contiguous United States (CONUS) and more than 5,000 in Alaska. This contribution greatly increases the ease of use of satellite remote sensing data for inland water quality monitoring, as well as highlights new horizons in inland water remote sensing algorithm development. We evaluate the performance of satellite remote sensing Cyanobacteria Index (CI)-based chlorophyll algorithms, the retrievals for which provide surrogate estimates of phytoplankton concentrations in cyanobacteria dominated lakes. Our analysis quantifies the algorithms' abilities to assess lake trophic state across the CONUS. As a case study, we apply a bootstrapping approach to derive a new CI-to-chlorophyll relationship, ChlBS, which performs relatively well with a multiplicative bias of 1.11 (11%) and mean absolute error of 1.60 (60%). While the primary contribution of this work is the distribution of the MERIS radiometric timeseries, we provide this case study as a roadmap for future stakeholders' algorithm development activities, as well as a tool to assess the strengths and weaknesses of applying a single algorithm across CONUS.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2021.112685","usgsCitation":"Seegers, B., Werdell, P., Vandermeulen, R., Salls, W., Stumpf, R., Schaeffer, B., Owens, T., Bailey, S., Scott, J., and Loftin, K.A., 2021, Satellites for long-term monitoring of inland U.S. lakes: The MERIS time series and application for chlorophyll-a: Remote Sensing of the Environment, v. 266, 112685, 14 p., https://doi.org/10.1016/j.rse.2021.112685.","productDescription":"112685, 14 p.","ipdsId":"IP-129074","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":450699,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2021.112685","text":"Publisher Index Page"},{"id":392623,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.42675781249999,\n 43.229195113965005\n ],\n [\n -89.2529296875,\n 43.229195113965005\n ],\n [\n -89.2529296875,\n 49.15296965617042\n ],\n [\n -97.42675781249999,\n 49.15296965617042\n ],\n [\n -97.42675781249999,\n 43.229195113965005\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -130.078125,\n 53.74871079689897\n ],\n [\n -128.32031249999997,\n 55.677584411089526\n ],\n [\n -134.82421875,\n 60.75915950226991\n ],\n [\n -139.5703125,\n 61.438767493682825\n ],\n [\n -140.09765625,\n 69.71810669906763\n ],\n [\n -156.09375,\n 71.85622888185527\n ],\n [\n -166.2890625,\n 68.84766505841037\n ],\n [\n -167.6953125,\n 65.29346780107583\n ],\n [\n -166.2890625,\n 59.44507509904714\n ],\n [\n -161.89453125,\n 54.36775852406841\n ],\n [\n -153.80859375,\n 55.87531083569679\n ],\n [\n -145.01953124999997,\n 59.80063426102869\n ],\n [\n -134.47265625,\n 55.07836723201515\n ],\n [\n -132.36328125,\n 51.83577752045248\n ],\n [\n -131.1328125,\n 52.05249047600099\n ],\n [\n -130.078125,\n 53.74871079689897\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"266","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Seegers, Bridget","contributorId":269867,"corporation":false,"usgs":false,"family":"Seegers","given":"Bridget","affiliations":[{"id":37453,"text":"National Aeronautics and Space Administration","active":true,"usgs":false}],"preferred":false,"id":828030,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Werdell, P. Jeremy","contributorId":269868,"corporation":false,"usgs":false,"family":"Werdell","given":"P. Jeremy","affiliations":[{"id":37453,"text":"National Aeronautics and Space Administration","active":true,"usgs":false}],"preferred":false,"id":828031,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vandermeulen, Ryan","contributorId":269869,"corporation":false,"usgs":false,"family":"Vandermeulen","given":"Ryan","email":"","affiliations":[{"id":37453,"text":"National Aeronautics and Space Administration","active":true,"usgs":false}],"preferred":false,"id":828032,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Salls, Wilson","contributorId":269870,"corporation":false,"usgs":false,"family":"Salls","given":"Wilson","affiliations":[{"id":35215,"text":"Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":828033,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stumpf, Richard","contributorId":269871,"corporation":false,"usgs":false,"family":"Stumpf","given":"Richard","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":828034,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schaeffer, Blake","contributorId":269872,"corporation":false,"usgs":false,"family":"Schaeffer","given":"Blake","affiliations":[{"id":35215,"text":"Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":828035,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Owens, Tommy","contributorId":269873,"corporation":false,"usgs":false,"family":"Owens","given":"Tommy","email":"","affiliations":[{"id":37453,"text":"National Aeronautics and Space Administration","active":true,"usgs":false}],"preferred":false,"id":828036,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bailey, Sean","contributorId":269874,"corporation":false,"usgs":false,"family":"Bailey","given":"Sean","affiliations":[{"id":37453,"text":"National Aeronautics and Space Administration","active":true,"usgs":false}],"preferred":false,"id":828037,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Scott, Joel","contributorId":269875,"corporation":false,"usgs":false,"family":"Scott","given":"Joel","email":"","affiliations":[{"id":37453,"text":"National Aeronautics and Space Administration","active":true,"usgs":false}],"preferred":false,"id":828038,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Loftin, Keith A. 0000-0001-5291-876X","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":221964,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":828039,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70224434,"text":"sir20215095 - 2021 - Discharge and dissolved-solids characteristics of Blacks Fork above Smiths Fork, Wyoming, April 2018 through September 2019","interactions":[],"lastModifiedDate":"2021-09-24T03:06:17.53203","indexId":"sir20215095","displayToPublicDate":"2021-09-23T22:04:41","publicationYear":"2021","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":"2021-5095","displayTitle":"Discharge and Dissolved-Solids Characteristics of Blacks Fork above Smiths Fork, Wyoming, April 2018 through September 2019","title":"Discharge and dissolved-solids characteristics of Blacks Fork above Smiths Fork, Wyoming, April 2018 through September 2019","docAbstract":"The Colorado River Basin Salinity Control Forum was formed in 1973 to coordinate salinity control efforts among the States in the Colorado River Basin, including Wyoming. The Colorado River Salinity Control Act of 1974 (Public Law 93–320) authorized “the construction, operation, and maintenance of certain works in the Colorado River Basin to control the salinity of water delivered to users in the United States and Mexico.” Water-quality standards for salinity in the lower Colorado River Basin were adopted in 1975. To help meet these standards, the Bureau of Reclamation, Natural Resource Conservation Service, and States within the Colorado River Basin have implemented salinity control projects that focus on reducing salt loading associated with irrigated agriculture by improving water delivery systems and water management practices. The term salinity is synonymous with dissolved solids in this report.
The Bureau of Reclamation, in conjunction with the Colorado River Basin Salinity Control Forum, was interested in determining the contribution of dissolved solids from Blacks Fork above Smiths Fork to the Colorado River and initiated a study of Blacks Fork above Smiths Fork in 2018. In early 2018, the U.S. Geological Survey installed a streamgage at the most downstream location on the Blacks Fork, upstream from the convergence with Smiths Fork, to characterize the stream. The Blacks Fork above Smiths Fork, near Lyman, Wyoming, streamgage (U.S. Geological Survey identifier 09219200) was operated from April 4, 2018, through September 30, 2019, collecting continuous stream stage and specific-conductance data, from which continuous discharge, dissolved-solids concentrations, and dissolved-solids loads were calculated. Seven sites were selected on Blacks Fork and a tributary to describe a snapshot of the discharge and dissolved-solids characteristics. These sites were sampled during July, August, and September 2018 and June, July, August, and September 2019 report.
Discharge at the Blacks Fork above Smiths Fork, near Lyman, Wyo., streamgage (09219200) from April through September in 2018 was lower and less variable than during the same period in 2019. The mean daily (mean of the daily means) discharge during those 6 months in 2018 (15.1 cubic feet per second [ft3/s]) was about one-tenth of the discharge during the same period in 2019 (152 ft3/s). The cumulative monthly discharge during April through September in 2018 was 5,360 acre-feet, about one-tenth of the discharge during the same period in 2019 which was 54,700 acre-feet. Similar differences in discharge between the 2018 and 2019 periods also are noted at other Blacks Fork streamgages in the area.
Continuous specific conductance data and the statistical relation between specific conductance and dissolved-solids concentrations were used to calculate the daily mean dissolved-solids concentrations. Dissolved solids often have an inverse relation with discharge because higher discharges typically have a diluting effect that lowers the dissolved-solids concentrations. In general, when discharges at the Blacks Fork above Smiths Fork streamgage (09219200) are higher, dissolved-solids concentrations are generally lower. However, the high dissolved-solids concentrations that are measured during high discharges indicate that the system has natural variability and the dissolved-solids concentrations are determined by more factors than just discharge. The mean daily dissolved-solids concentration during April through September 2018 was 1,630 milligrams per liter and during the same period in 2019 was 1,100 milligrams per liter.
Dissolved-solids loads were calculated as the product of the discharge and dissolved-solids concentration. The daily mean dissolved-solids loads during 2018 were typically lower than during 2019. This result is primarily because the discharge was much lower in 2018 than in 2019. Therefore, although the daily mean dissolved-solids concentrations tended to be higher in 2018, the substantially higher discharges in 2019 had more of an effect on the dissolved-solids loads than the dissolved-solids concentrations.
The cumulative dissolved-solids load at the Blacks Fork above Smiths Fork, near Lyman, Wyo., streamgage (09219200) during the 18-month study was 81,200 tons, with a mean daily load of 149 tons per day. During the 6-month period from April through September 2018, the cumulative dissolved-solids load at the streamgage was estimated to be 8,740 tons and, during the same 6 months in 2019, the cumulative dissolved-solids load was estimated to be 60,900 tons. During the fall and winter between the two periods, the cumulative dissolved-solids load was 11,600 tons.
Discharge and dissolved-solids concentrations from samples collected during the synoptic sampling events were highly variable among most sites during most synoptic sampling events and also highly variable at most sites among different sampling events. The two sites upstream from the tributary input from Threemile Creek had lower dissolved-solids concentrations than sites including and downstream from the tributary. Sites including and downstream from the tributary had similar values and variability of dissolved-solids loads, with the exception of the farthest downstream site at the Blacks Fork above Smiths Fork, near Lyman, Wyo., streamgage (09219200) that tended to have larger dissolved-solids loads and higher variability among synoptic sampling events.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215095","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Eddy-Miller, C.A., Wheeler, J.D., Law, R.M., and Moran, S.W., 2021, Discharge and dissolved-solids characteristics of Blacks Fork above Smiths Fork, Wyoming, April 2018 through September 2019: U.S. Geological Survey Scientific Investigations Report 2021–5095, 32 p., https://doi.org/10.3133/sir20215095.","productDescription":"vii, 32 p.","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-125542","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":389699,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5095/sir20215095.xml","size":"220 kB","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2021–5095 xml"},{"id":389653,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5095/sir20215095.pdf","text":"Report","size":"2.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5095"},{"id":389652,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5095/coverthb3.jpg"}],"contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
521 Progress Circle, Suite 6
Cheyenne, WY 82007
The Earth Resources Observation and Science (EROS) Center is the largest facility of its kind within the U.S. Geological Survey. As both a science and data center, EROS serves a unique and critical role in shaping our understanding of a changing planet.
EROS opened its doors in 1973 as a receiving station, data archive, and data distribution hub for the USGS Landsat series of Earth observing satellites. In the nearly five decades since, EROS has grown into a globally recognized leader in land change science.
The Arctic Ocean is undergoing rapid transformation toward a seasonally ice-free ecosystem. As ice-adapted apex predators, polar bears (Ursus maritimus) are challenged to cope with ongoing habitat degradation and changes in their prey base driven by food-web response to climate warming. Knowledge of polar bear response to environmental change is necessary to understand ecosystem dynamics and inform conservation decisions. In the southern Beaufort Sea (SBS) of Alaska and western Canada, sea ice extent has declined since satellite observations began in 1979 and available evidence suggests that the carrying capacity of the SBS for polar bears has trended lower for nearly two decades. In this study, we investigated the population dynamics of polar bears in Alaska's SBS from 2001 to 2016 using a multistate Cormack–Jolly–Seber mark–recapture model. States were defined as geographic regions, and we used location data from mark–recapture observations and satellite-telemetered bears to model transitions between states and thereby explain heterogeneity in recapture probabilities. Our results corroborate prior findings that the SBS subpopulation experienced low survival from 2003 to 2006. Survival improved modestly from 2006 to 2008 and afterward rebounded to comparatively high levels for the remainder of the study, except in 2012. Abundance moved in concert with survival throughout the study period, declining substantially from 2003 and 2006 and afterward fluctuating with lower variation around an average of 565 bears (95% Bayesian credible interval [340, 920]) through 2015. Even though abundance was comparatively stable and without sustained trend from 2006 to 2015, polar bears in the Alaska SBS were less abundant over that period than at any time since passage of the U.S. Marine Mammal Protection Act. The potential for recovery is likely limited by the degree of habitat degradation the subpopulation has experienced, and future reductions in carrying capacity are expected given current projections for continued climate warming.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8139","usgsCitation":"Bromaghin, J.F., Douglas, D.C., Durner, G.M., Simac, K.S., and Atwood, T.C., 2021, Survival and abundance of polar bears in Alaska’s Beaufort Sea, 2001–2016: Ecology and Evolution, v. 11, no. 20, p. 14250-14267, https://doi.org/10.1002/ece3.8139.","productDescription":"18 p.","startPage":"14250","endPage":"14267","ipdsId":"IP-125254","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":450707,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.8139","text":"External Repository"},{"id":389724,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska","otherGeospatial":"Beaufort Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -166.2890625,\n 68.23682270936281\n ],\n [\n -156.4453125,\n 71.24435551310674\n ],\n [\n -140.9765625,\n 69.59589006237648\n ],\n [\n -141.15234374999997,\n 76.24781659441473\n ],\n [\n -166.55273437499997,\n 76.03731657616542\n ],\n [\n -166.2890625,\n 68.23682270936281\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"20","noUsgsAuthors":false,"publicationDate":"2021-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Bromaghin, Jeffrey F. 0000-0002-7209-9500 jbromaghin@usgs.gov","orcid":"https://orcid.org/0000-0002-7209-9500","contributorId":139899,"corporation":false,"usgs":true,"family":"Bromaghin","given":"Jeffrey","email":"jbromaghin@usgs.gov","middleInitial":"F.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":823891,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":823892,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Durner, George M. 0000-0002-3370-1191 gdurner@usgs.gov","orcid":"https://orcid.org/0000-0002-3370-1191","contributorId":3576,"corporation":false,"usgs":true,"family":"Durner","given":"George","email":"gdurner@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":823893,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Simac, Kristin S. 0000-0002-4072-1940 ksimac@usgs.gov","orcid":"https://orcid.org/0000-0002-4072-1940","contributorId":131096,"corporation":false,"usgs":true,"family":"Simac","given":"Kristin","email":"ksimac@usgs.gov","middleInitial":"S.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":823894,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":823895,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70230404,"text":"70230404 - 2021 - Informing future condition scenario planning for habitat specialists of the imperiled pine rockland ecosystem of South Florida","interactions":[],"lastModifiedDate":"2022-04-12T13:20:09.477273","indexId":"70230404","displayToPublicDate":"2021-09-23T08:12:11","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":7504,"text":"Final Report","active":true,"publicationSubtype":{"id":1}},"title":"Informing future condition scenario planning for habitat specialists of the imperiled pine rockland ecosystem of South Florida","docAbstract":"This project evaluated habitat conditions for two species found in the imperiled pine rockland ecosystem—the Rim Rock Crowned Snake (Tantilla oolitica) and the Key Ring-Necked Snake (Diadophis punctatus acricus). The Rim Rock Crowned Snake historically occurred in eastern Miami-Dade County (hereafter, mainland) as well as throughout the Florida Keys, whereas the Key Ring-Necked Snake occurs only in lower Florida Keys (Enge et al. 2004; Mays and Enge 2016). Both species are very elusive, small (< 20 cm in length) and primarily fossorial. Pine rockland habitat is rapidly disappearing in South Florida, with < 3 percent of its original extent remaining. Saltwater intrusion from hurricanes and sea-level rise (SLR), and human development pose the greatest threats to the longevity of this ecosystem which, in turn, places species that are endemic to this unique habitat at risk of extinction.
The Rim Rock Crowned Snake and the Key Ringed-Necked Snake are being considered for listing by the U.S. Fish and Wildlife Service (USFWS). To aid the agency’s decision, it must be able to forecast species’ responses to potential future environmental conditions, as well as to different conservation and management actions. Yet, the information needed to complete these forecasts—such as population trends, life history traits, habitat use, and future land use and climate conditions—is often lacking for most rare species. This is especially problematic for assessments of species resiliency to changes in climate and land use.
When these types of data are lacking, information on habitat quality can be used to help determine how a species will respond to change. First, this project gathered current and historical records for both species from various sources such as museum specimens, inventories, and other personal account. Then, we identified potential future changes in habitat that could result from different management actions, such as habitat acquisition or restoration, and environmental conditions, such as changes in the frequency and intensity of tropical storms and rates of SLR. Researchers then explored the potential impacts of these habitat condition changes on the Rim Rock Crowned Snake and Key Ring-Necked Snake.
This information can be used by the USFWS to help make decisions about the need to protect these species under the Endangered Species Act and could inform the conservation, management, and recovery of other at-risk species found in the pine rockland ecosystem. This work supports the Secretary of Interior’s priority to create a conservation stewardship legacy by using science to identify best practices to manage land and water resource and adapt to changes in the environment.
","language":"English","publisher":"Southeast Climate Adaptation Science Center","usgsCitation":"Walls, S.C., 2021, Informing future condition scenario planning for habitat specialists of the imperiled pine rockland ecosystem of South Florida: Final Report, 18 p.","productDescription":"18 p.","ipdsId":"IP-129367","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":398537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398518,"type":{"id":15,"text":"Index Page"},"url":"https://secasc.ncsu.edu/science/pine-rocklands/"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.0458984375,\n 24.287026865376436\n ],\n [\n -79.9365234375,\n 24.287026865376436\n ],\n [\n -79.9365234375,\n 26.244156283890756\n ],\n [\n -82.0458984375,\n 26.244156283890756\n ],\n [\n -82.0458984375,\n 24.287026865376436\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Walls, Susan C. 0000-0001-7391-9155 swalls@usgs.gov","orcid":"https://orcid.org/0000-0001-7391-9155","contributorId":138952,"corporation":false,"usgs":true,"family":"Walls","given":"Susan","email":"swalls@usgs.gov","middleInitial":"C.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":840331,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70225164,"text":"70225164 - 2021 - Helium-carbon systematics of groundwaters in the Lassen Peak Region","interactions":[],"lastModifiedDate":"2021-10-15T13:13:47.629382","indexId":"70225164","displayToPublicDate":"2021-09-23T08:08:31","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1213,"text":"Chemical Geology","active":true,"publicationSubtype":{"id":10}},"title":"Helium-carbon systematics of groundwaters in the Lassen Peak Region","docAbstract":"Carbon dioxide emissions from active subaerial volcanoes represent 20–50% of the annual global volcanic CO2 flux (Barry et al., 2014). Passive degassing of carbon from the flanks of volcanoes, and the associated accumulation of dissolved inorganic carbon (DIC) within nearby groundwater, also represents a potentially important, yet poorly constrained flux of carbon to the surface (Werner et al., 2019). Here we investigate sources and sinks of DIC in groundwaters in the Lassen Peak region of California. Specifically, we report and interpret the relative abundance and isotopic composition of helium (3He, 4He) and carbon (12C, 13C, 14C) in 37 groundwater samples, from 24 distinct wells, collected between 20 and 60 km from Lassen Peak. Measured groundwater samples have air-corrected 3He/4He values between 0.19 and 7.44 RA (where RA = air 3He/4He = 1.39 × 10−6), all in excess of the radiogenic production value (~0.05 RA), indicating pervasive mantle-derived helium additions to the groundwater system in the Lassen Peak region. Stable carbon isotope ratios of DIC (δ13C) vary between −12.6 and − 27.7‰ (vs. VPDB). Measured groundwater DIC/3He values fall in the range of 2.2 × 1010 to 1.1 × 1012. Using helium and carbon isotope data, we explore several conceptual models to estimate surface carbon contributions and to differentiate between DIC derived from soil CO2 versus DIC derived from external (slab and mantle) carbon sources. Specifically, if we use 14C to identify soil-derived DIC (assuming decadal-to-centennial groundwater ages and a soil CO2 14C activity equal to that of the atmosphere), we calculate that a hypothetical external carbon source would have an apparent δ13C signature between −10.3 and − 59.3‰ (vs. Vienna Pee Dee Belemnite (VPDB)) and an apparent C/3He between 7.0 × 109 and 1.0 × 1012. These apparent δ13C and C/3He values are substantially isotopically lighter than and greater than canonical MORB values, respectively. We suggest that >95% of any external (non-soil-derived) DIC in groundwater must thus be non-mantle in origin (i.e., slab derived or assimilated organic carbon). We further investigate possible sources of external DIC to groundwater using two idealized conceptual approaches: a pure (unfractionated) source mixing model (after Sano and Marty, 1995) and a scenario that invokes fractionation due to calcite precipitation. Because the former model requires carbon contributions from an organic source component with unrealistically low δ13C (~ − 60‰), we suggest that the second scenario is more plausible. Importantly, however, we caution that all conceptual models are dependent on assumptions about initial 14C activity. Thus, we cannot rule out the possibility that the true fraction of non-surface-derived DIC in these samples is lower or negligible, despite the pervasive mantle-derived He isotope signatures throughout the region. Following the 14C approach to deconvolving sources of DIC, we determine that the maximum passive carbon flux could be up to ~2.2 × 106 kg/yr, which is lower than previous magmatic carbon flux estimates from the Lassen region (Rose and Davisson, 1996). We find that the passive dissolved carbon flux could represent a maximum of ~4–18% of the total Lassen geothermal CO2 degassing flux (estimated to be ~3.5 × 107 kg/yr Rose and Davisson, 1996; Gerlach et al., 2008), which is still more than an order of magnitude smaller than soil gas CO2 flux estimates (7.3–11 × 107 kg/yr) for nearby volcanoes (Sorey et al., 1998; Gerlach et al., 1999; Evans et al., 2002; Werner et al., 2014). We conclude that passive dissolved carbon fluxes should be combined with geothermal fluxes and soil gas fluxes to obtain a complete picture of volcanic carbon emissions globally. Our approach highlights the utility of measuring helium isotopes in concert with the full suite of noble gas abundances, tritium, δ13C and 14C, which when interpreted together can be used to better elucidate the various sources of DIC in groundwater.
Information about past ecosystem dynamics and human activities is stored in the ice of Colle Gnifetti glacier in the Swiss Alps. Adverse climatic intervals incurred crop failures and famines and triggered reestablishment of forest vegetation but also societal resilience through innovation. Historical documents and lake sediments record these changes at local—regional scales but often struggle to comprehensively document continental-scale impacts on ecosystems. Here, we provide unique multiproxy evidence of broad-scale ecosystem, land use, and climate dynamics over the past millennium from a Colle Gnifetti microfossil and oxygen isotope record. Microfossil data indicate that before 1750 CE forests and fallow land rapidly replaced crop cultivation during historically documented societal crises caused by climate shifts and epidemics. Subsequently, with technology and the introduction of more resilient crops, European societies adapted to the Little Ice Age cold period, but resource overexploitation and industrialization led to new regional to global-scale environmental challenges.
For the past ∼12 years the Pacific Northwest Seismic Network has been automatically detecting and locating tectonic tremor across the Cascadia subduction zone, resulting in a catalog of more than 500,000 tremor epicenters to date, which has served as a valuable resource for tremor and slip research. This manuscript presents an updated methodology for routine tremor detection in Cascadia and a new catalog of over 180,000 tremor epicenters including amplitudes detected along the subduction zone margin from 2017 to 2021. The events are detected via cross-correlation of continuous vertical envelope data of 128 stations from northern California to northern Vancouver Island. The modified approach results in less scatter and a 55% increase in detected epicenters than previously observed, as well as a newly identified tremor source offset updip from the main tremor and slip region at the southern edge of the subduction zone. Radiated seismic energy in the 1.5–5 Hz band is used to assign epicenters an energy magnitude (MeL), which is calibrated to the ML of local earthquakes. Southern Cascadia is most active, but the highest tremor energy rates occur in northern Cascadia. Tremor in central Cascadia is systematically weaker and less frequent. Individual epicenter magnitudes range from ∼0.5–2 and spatiotemporally cluster into 1,060 swarms with cumulative MeL ranging from ∼0.8 to 3.7. The swarms reflect underlying slow slip events and occur with an earthquake-like energy distribution with a b value ∼1. Tremor epicenters, however, follow a tapered Gutenberg-Richter distribution with high b values, suggesting individual tremor bursts and their constituent low-frequency earthquakes are fault-dimension limited.
Red Knots (Calidris canutus rufa) stop at Delaware Bay during northward migration to feed on eggs of horseshoe crabs (Limulus polyphemus). The northward migration of C. c. rufa coincides with the spawning of horseshoe crabs whose eggs are the perfect food for a migrating Red Knot (Karpanty et al. 2006, Haramis et al. 2007). Horseshoe crabs are therefore an important food resource for Red Knots as well as other shorebirds at Delaware Bay.
Horseshoe crabs have been harvested since at least 1990 for use as bait in American eel (Anguilla rostrata) and whelk (Busycon) fisheries (Kreamer and Michels 2009). In the late 1990s and early 2000s the number of Red Knots found at Delaware Bay declined dramatically from ~50,000 to ~13,000 (Niles et al. 2008). At the same time the number of horseshoe crabs harvested also declined and avian conservation biologists hypothesized that unregulated harvest of horseshoe crabs from Delaware Bay in the 1990s prevented sufficient refueling during stopover for successful migration to the breeding grounds, nesting, and survival for the remainder of the annual cycle (McGowan et al. 2011).
The harvest of horseshoe crabs in the Delaware Bay region has been managed by the Atlantic States Marine Fisheries Commission (ASMFC) since 2012 using an Adaptive Resource Management (ARM) framework (McGowan et al. 2015b). The ARM framework was designed to constrain the harvest so that number of spawning crabs would not limit the number of Red Knots stopping at Delaware Bay during migration. This management framework to achieve multiple objectives requires an estimate each year of both the crab population and the Red Knot stopover population size to inform harvest recommendations (McGowan et al. 2015a). We have estimated the stopover population size using mark-resight data on individually-marked birds and a Jolly-Seber model for open populations since 2011.
","language":"English","publisher":"Atlantic States Marine Fisheries Commission","usgsCitation":"Lyons, J.E., 2021, Red knot stopover population size and migration ecology at Delaware Bay, USA, 2021, 21 p.","productDescription":"21 p.","ipdsId":"IP-135416","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":427147,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398095,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://dnrec.delaware.gov/fish-wildlife/conservation/shorebirds/research/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Delaware, New Jersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.09403509407363,\n 38.74026331013363\n ],\n [\n -74.92922524720622,\n 38.95518554423097\n ],\n [\n -74.86023507875021,\n 39.17242937484494\n ],\n [\n -75.47348102058082,\n 39.53695640590777\n ],\n [\n -75.45818237996806,\n 39.725833415896574\n ],\n [\n -75.62300710583959,\n 39.7375634879601\n ],\n [\n -75.68813576821344,\n 39.5812414619472\n ],\n [\n -75.61529784001694,\n 39.40677166373757\n ],\n [\n -75.46198775359684,\n 39.16648631014266\n ],\n [\n -75.34700185696957,\n 38.90151720709986\n ],\n [\n -75.09403509407363,\n 38.74026331013363\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lyons, James E. 0000-0002-9810-8751","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":222844,"corporation":false,"usgs":true,"family":"Lyons","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":839581,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70226497,"text":"70226497 - 2021 - Frequency distribution","interactions":[],"lastModifiedDate":"2021-11-22T14:23:44.401647","indexId":"70226497","displayToPublicDate":"2021-09-22T08:20:57","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Frequency distribution","docAbstract":"Given a numerical dataset, a frequency distribution is a summary displaying fluctuations of an attribute within the range of values. In contrast to an analytical probability distribution, a frequency distribution always deals with empirically observed values (Everitt and Skondall 2010). In general, the larger the number of values, the more useful is the frequency distribution relative to listing all values. Today, multiple software packages allow easy display of a frequency distribution.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Encyclopedia of mathematical geosciences","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","doi":"10.1007/978-3-030-26050-7","usgsCitation":"Olea, R., 2021, Frequency distribution, chap. of Encyclopedia of mathematical geosciences, HTML Document, https://doi.org/10.1007/978-3-030-26050-7.","productDescription":"HTML Document","ipdsId":"IP-122768","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":498722,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://pure.qub.ac.uk/en/publications/fce9c7cb-69b5-4b16-9f9a-f81c0c89e272","text":"External Repository"},{"id":391979,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Olea, Ricardo A. 0000-0003-4308-0808","orcid":"https://orcid.org/0000-0003-4308-0808","contributorId":224285,"corporation":false,"usgs":true,"family":"Olea","given":"Ricardo A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":827107,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70224612,"text":"70224612 - 2021 - Revisiting the declustering of spatial data with preferential sampling","interactions":[],"lastModifiedDate":"2021-09-30T11:57:29.597973","indexId":"70224612","displayToPublicDate":"2021-09-22T06:56:10","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1315,"text":"Computers & Geosciences","printIssn":"0098-3004","active":true,"publicationSubtype":{"id":10}},"title":"Revisiting the declustering of spatial data with preferential sampling","docAbstract":"Preferential sampling is a form of data collection that may significantly distort the histogram and the semivariogram of spatially correlated data. Typical situations are a higher sampling density at high-valued areas favorable for mining, and highly contaminated areas in need of environmental remediation. Multiple statistical procedures are devoted to obtaining representative statistics, whose magnitudes should be close to the respective population values. This paper proposes a resampling method that can compensate for preferential sampling of spatially correlated data without using declustering weights. The application of the method herein generates a dataset of median estimates of quantiles of multiple stratified resamples that is free of preferential sampling. The methodology is illustrated with two examples. The first one involves values actually measured in the field and has the advantage of representing a real scenario of spatial fluctuations and preferential sampling. A second dataset is synthetic and has the main benefit of a priori knowledge of the underlying spatial distribution, thus allowing a satisfactory evaluation of the results against the known baseline. Access to computer code is offered for practical application of the method.
The Smallmouth Bass (Micropterus dolomieu; SMB) is a widely distributed black bass species, but the southwestern edge of the species range within the Interior Highlands contains some of the most divergent ecotypes. The Neosho subspecies (M. d. velox) inhabits tributaries of the Arkansas River within the Ozark Mountains and a second lineage is reported from drainages of the Ouachita Mountains. We sought to develop a single nucleotide polymorphism (SNP) panel to (1) diagnose hybridization with sympatric Spotted Bass (Micropterus punctulatus; SPB) and non-native Northern SMB (M. d. dolomieu) stocked in the region, and (2) delineate population structure within the ranges of the Neosho and Ouachita SMB lineages. We obtained 76 individual SMB samples from across their range but concentrated within the Interior Highlands (n = 50). We also included 3 SPB to allow for hybrid detection and 3 Shoal Bass (Micropterus cataractae) as an outgroup. Phylogenetic trees constructed with the generated SNP data corroborated the existence of at least three major lineages of SMB (Northern, Neosho, and Ouachita), each containing varying degrees of differentiation among major drainages. Simulation analyses revealed that chosen SNPs had high power (> 0.9) to assign SMB × SPB hybrid categories and similarly high power (> 0.8) for Northern SMB × Interior Highlands SMB hybrids. Clustering methods delineated major inter-basin population structure within the native ranges of Neosho and Ouachita SMB with chosen SNPs. Anticipated uses of the resulting 192-loci SNP panel include conservation planning, fisheries management assessments, and ecological investigations of the Neosho and Ouachita SMB lineages.
","language":"English","publisher":"Springer Link","doi":"10.1007/s12686-020-01170-8","usgsCitation":"Long, J.M., Taylor, A.T., and Buonaccorsi, V., 2021, A conservation-oriented SNP panel for Smallmouth Bass (Micropterus dolomieu), with emphasis on Interior Highlands lineages: Conservation Genetics Resources, v. 13, p. 47-59, https://doi.org/10.1007/s12686-020-01170-8.","productDescription":"13 p.","startPage":"47","endPage":"59","ipdsId":"IP-115509","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395780,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Missouri, Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.8447265625,\n 33.358061612778876\n ],\n [\n -91.0986328125,\n 33.358061612778876\n ],\n [\n -91.0986328125,\n 37.405073750176925\n ],\n [\n -95.8447265625,\n 37.405073750176925\n ],\n [\n -95.8447265625,\n 33.358061612778876\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationDate":"2020-09-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":834205,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taylor, A. T.","contributorId":275351,"corporation":false,"usgs":false,"family":"Taylor","given":"A.","email":"","middleInitial":"T.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":834206,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buonaccorsi, V.","contributorId":275670,"corporation":false,"usgs":false,"family":"Buonaccorsi","given":"V.","email":"","affiliations":[{"id":56875,"text":"The Center for Aquaculture Technologies","active":true,"usgs":false}],"preferred":false,"id":834207,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227095,"text":"70227095 - 2021 - White-nose Syndrome and environmental correlates to landscape-scale bat presence","interactions":[],"lastModifiedDate":"2021-12-29T14:40:46.904017","indexId":"70227095","displayToPublicDate":"2021-09-21T08:35:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"White-nose Syndrome and environmental correlates to landscape-scale bat presence","docAbstract":"Over the past 13 years, White-nose Syndrome (WNS) has caused North American bat population declines and shifted community structure towards species less or unaffected by the disease. Mist-netting, acoustic surveys, and cave count data have been used to document changes in bat presence and activity through site-specific, pre- and post-WNS studies. Management and survey guidance often must be applied at a combined landscape and site-specific scale. Our objective was to explore the relationships among WNS impact, influence of available hibernacula, and environmental factors for the nightly presence of 3 WNS-affected bats: the Indiana bat (Myotis sodalis), northern long-eared bat (M. septentrionalis), and big brown bat (Eptesicus fuscus). We used recordings from 10 acoustic monitoring study areas, each with 3 survey locations across the states of Virginia, West Virginia, Ohio and Kentucky to assess changes in nightly bat presence during the summer of 2017. There were significant positive and negative correlates of broad land-cover categories for presence of all 3 bat species. Our findings also corroborated trends in abundance and distribution patterns found in prior, smaller-scale studies, supporting the relevance of land cover categories in a large-scale acoustic monitoring framework. We observed a negative association between WNS impact-years and nightly northern long-eared bat presence, but low occurrence and patchy distribution reduced our ability to infer strong relationships. Big brown bat presence showed a significant positive relationship with WNS occurrence on the landscape, providing evidence that big brown bats are maintaining populations after years of exposure. Indiana bats were the least-documented species, limiting the strength of our conclusions, but we did observe significant temporal patterns in nightly presence, with higher probabilities of presence earlier in the summer. Our results show the potential efficacy of using a WNS impact metric to predict summer bat presence, inform current U.S. Fish and Wildlife Service acoustic monitoring guidelines, and highlight which environmental variables are relevant for large-scale acoustic monitoring.
Avian populations must mount effective immune responses upon exposure to environmental stressors such as avian influenza and xenobiotics. Although multiple immune assays have been tested and applied to various avian species, antibody-mediated immune responses in non-model avian species are not commonly reported due to the lack of commercially available species-specific antibodies. The objectives of the present study were to advance methods for studying wild bird immune responses and to apply these to the evaluation of cytological responses after exposure of American kestrels, Falco sparverius, to a commercial flame retardant mixture containing isopropylated triarylphosphate isomers (ITP). Hatchlings were gavaged daily with safflower oil or 1.5 ug/g bw/day of ITP suspended in safflower oil, then bled on days 9, 17, and 21. The ITP treatment group (n = 18) and a subset of controls (Poly I:C treatment group; n = 10) were injected on days 9 and 15 with a synthetic analog of viral double-stranded RNA, polyinosinic:polycytidylic acid (Poly I:C), a toll-like receptor ligand and synthetic viral mimic, and responses compared to a sham injected control group (n = 8). The hypotheses tested whether kestrels showed immunological differences among treatment groups, genetic sex, and/or white blood cell (WBC) subpopulation type over time. A flow cytometry (FCM) gating strategy categorized heterophils (H), lymphocytes (L), and monocytes (M) and their proportions, and measured relative fluorescence in response to anti-chicken CD4 binding. Fluorescent cell surfaces and some granular/vacuolar inclusions were visualized by epifluorescence microscopy. A fourth subpopulation with higher levels of granularity than M but less than H became increasingly apparent with time and was gated along with the H subpopulation; its frequency of occurrence was lowest in the ITP group (P = 0.0023). The percentages of cells differed among treatment groups, days, and sexes (P = 0.0001). For both sexes, percentages of H and L were higher than M in control and Poly I:C. In the ITP group, L percentages were higher than H and M (P = 0.0457), and H and L were higher than M on days 9 and 21 (P = 0.0001). The ratios of H:L and H:WBC, indicators of robust immunity, were also higher on days 9 and 21 than on 17 (P = 0.0079). For each sex, the highest levels of activity measured by FCM geometric means (GEO) of fluorescence (indicative of antibody binding) were observed on day 9 (P = 0.0001 female, and P = 0.0011 male) in H over both L and M (P < 0.0001 for each). In males, GEO of the Poly I:C group was higher than that of the ITP group (P = 0.0374), with no difference observed among females over all days. By using a FCM algorithm for population comparisons of fluorescence to investigate binding within H, the T(x) scores indicated higher fluorescence in control and Poly I:C groups over ITP (P = 0.0001). Unlike chickens, Gallus gallus, which express CD4 primarily on L, kestrels bound the commercial antibody primarily within the gated H subpopulation, suggesting an immunophenotypic difference between taxa, despite a ~60% identity of Falco CD4 amino acid sequences with chicken CD4. The emergent cell subset within the gated H presented dendritic-like cell (DLC) morphological and functional properties, apparently serving as an effector cell. This study adds interpretive context to ecological investigations of infection and of potential immunomodulation by emerging compounds, whereby the early innate responses are mediated by the various cell subsets serving as useful quantitative markers of immunological condition. Data showed that dietary exposure to ITP was immunosuppressive for male and female kestrels over the course of the experiment, reducing DLC frequency compared to the Poly I:C controls. Heterophils and DLC were important in facilitating innate immunological responses.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envint.2021.106779","usgsCitation":"Jenkins, J., Baudoin, B.A., Johnson, D., Fernie, K.J., Stapelton, H.M., and Karouna-Renier, N., 2021, Establishment of baseline cytology metrics in nestling American kestrels (Falco sparverius): Immunomodulatory effects of the flame retardant isopropylated triarylphosphate isomers: Environment International, v. 157, 106779, 15 p.; Data Release, https://doi.org/10.1016/j.envint.2021.106779.","productDescription":"106779, 15 p.; Data Release","ipdsId":"IP-116785","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450748,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envint.2021.106779","text":"Publisher Index Page"},{"id":436196,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P7ZTMU","text":"USGS data release","linkHelpText":"Laboratory analysis assessing immune response after flame retardant exposure in American kestrels, Falco sparverius, through 21 days post-hatch"},{"id":436195,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SGX37F","text":"USGS data release","linkHelpText":"Discerning innate immunity in American kestrels, Falco sparverius, through 21 days post-hatch"},{"id":390889,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417862,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/p9sgx37f"}],"volume":"157","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jenkins, Jill 0000-0002-5087-0894","orcid":"https://orcid.org/0000-0002-5087-0894","contributorId":206575,"corporation":false,"usgs":true,"family":"Jenkins","given":"Jill","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":825642,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baudoin, Brooke A 0000-0003-2874-1604","orcid":"https://orcid.org/0000-0003-2874-1604","contributorId":267938,"corporation":false,"usgs":true,"family":"Baudoin","given":"Brooke","email":"","middleInitial":"A","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":825643,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Darren 0000-0002-0502-6045","orcid":"https://orcid.org/0000-0002-0502-6045","contributorId":203921,"corporation":false,"usgs":true,"family":"Johnson","given":"Darren","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":825644,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fernie, Kim J.","contributorId":211241,"corporation":false,"usgs":false,"family":"Fernie","given":"Kim","email":"","middleInitial":"J.","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":825645,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stapelton, Heather M. 0000-0002-9995-6517","orcid":"https://orcid.org/0000-0002-9995-6517","contributorId":267940,"corporation":false,"usgs":false,"family":"Stapelton","given":"Heather","email":"","middleInitial":"M.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":825646,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Karouna-Renier, Natalie 0000-0001-7127-033X nkarouna@usgs.gov","orcid":"https://orcid.org/0000-0001-7127-033X","contributorId":200983,"corporation":false,"usgs":true,"family":"Karouna-Renier","given":"Natalie","email":"nkarouna@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":825647,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}