The National Park Service, Natural Resource Stewardship and Science office in Fort Collins, Colorado, publishes a range of reports that address natural resource topics. These reports are of interest and applicability to a broad audience in the National Park Service and others in natural resource management, including scientists, conservation and environmental constituencies, and the public. The Natural Resource Report Series is used to disseminate comprehensive information and analysis about natural resources and related topics concerning lands managed by the National Park Service. The series supports the advancement of science, informed decision-making, and the achievement of the National Park Service mission. The series also provides a forum for presenting lengthier results that may not be accepted by publications with page limitations. All manuscripts in the series receive the appropriate level of peer review to ensure that the information is scientifically credible, technically accurate, appropriately written for the intended audience, and designed and published in a professional manner. This report received formal peer review by subject-matter experts whose background and expertise put them on par technically and scientifically with the authors of the information. The peer review was led according to the Fundamental Science Practices of the U.S. Geological Survey. Views, statements, findings, conclusions, recommendations, and data in this report do not necessarily reflect views and policies of the National Park Service, U.S. Department of the Interior. Mention of trade names or commercial products does not constitute endorsement or recommendation for use by the U.S. Government. This report is available in digital format from the Gulf Coast Network website and the Natural Resource Publications Management website.
","language":"English","publisher":"National Park Service","usgsCitation":"Hop, K.D., Strassman, A.C., Sattler, S., Pyne, M., Teague, J., White, R., Ruhser, J., Hlavacek, E., and Dieck, J., 2017, National Park Service Vegetation Mapping Inventory Program: Jean Lafitte National Historical Park and Preserve Vegetation Mapping Project: Natural Resource Technical Report NPS/GULN/NRR—2017/1528, 370 p.","productDescription":"370 p.","ipdsId":"IP-086142","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":350904,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":349607,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/DataStore/Reference/Profile/2247523"}],"country":"United States","state":"Louisiana","otherGeospatial":"Jean Lafitte National Historical Park and Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.23963928222656,\n 29.690737021847788\n ],\n [\n -90.0669479370117,\n 29.690737021847788\n ],\n [\n -90.0669479370117,\n 29.875183016261957\n ],\n [\n -90.23963928222656,\n 29.875183016261957\n ],\n [\n -90.23963928222656,\n 29.690737021847788\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a743585e4b0a9a2e9e25ca8","contributors":{"authors":[{"text":"Hop, Kevin D. 0000-0002-9928-4773 khop@usgs.gov","orcid":"https://orcid.org/0000-0002-9928-4773","contributorId":1438,"corporation":false,"usgs":true,"family":"Hop","given":"Kevin","email":"khop@usgs.gov","middleInitial":"D.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":724227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Strassman, Andrew C. 0000-0002-9792-7181 astrassman@usgs.gov","orcid":"https://orcid.org/0000-0002-9792-7181","contributorId":4575,"corporation":false,"usgs":true,"family":"Strassman","given":"Andrew","email":"astrassman@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":724228,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sattler, Stephanie 0000-0003-4417-2480 ssattler@usgs.gov","orcid":"https://orcid.org/0000-0003-4417-2480","contributorId":191016,"corporation":false,"usgs":true,"family":"Sattler","given":"Stephanie","email":"ssattler@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":724229,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pyne, Milo","contributorId":201061,"corporation":false,"usgs":false,"family":"Pyne","given":"Milo","email":"","affiliations":[],"preferred":false,"id":724230,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Teague, Judy","contributorId":201062,"corporation":false,"usgs":false,"family":"Teague","given":"Judy","email":"","affiliations":[],"preferred":false,"id":724231,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"White, Rickie","contributorId":201063,"corporation":false,"usgs":false,"family":"White","given":"Rickie","email":"","affiliations":[],"preferred":false,"id":724232,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ruhser, Janis 0000-0001-9987-2578 jruhser@usgs.gov","orcid":"https://orcid.org/0000-0001-9987-2578","contributorId":149646,"corporation":false,"usgs":true,"family":"Ruhser","given":"Janis","email":"jruhser@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":724233,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hlavacek, Enrika 0000-0002-9872-2305 ehlavacek@usgs.gov","orcid":"https://orcid.org/0000-0002-9872-2305","contributorId":149114,"corporation":false,"usgs":true,"family":"Hlavacek","given":"Enrika","email":"ehlavacek@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":724234,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Dieck, Jennifer 0000-0002-4388-4534 jdieck@usgs.gov","orcid":"https://orcid.org/0000-0002-4388-4534","contributorId":149647,"corporation":false,"usgs":true,"family":"Dieck","given":"Jennifer","email":"jdieck@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":724235,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70197204,"text":"70197204 - 2017 - Genomics of Arctic cod","interactions":[],"lastModifiedDate":"2018-06-12T11:15:27","indexId":"70197204","displayToPublicDate":"2017-12-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5709,"text":"OCS Study","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"BOEM 2017-066","title":"Genomics of Arctic cod","docAbstract":"The Arctic cod (Boreogadus saida) is an abundant marine fish that plays a vital role in the marine food web. To better understand the population genetic structure and the role of natural selection acting on the maternally-inherited mitochondrial genome (mitogenome), a molecule often associated with adaptations to temperature, we analyzed genetic data collected from 11 biparentally-inherited nuclear microsatellite DNA loci and nucleotide sequence data from from the mitochondrial DNA (mtDNA) cytochrome b (cytb) gene and, for a subset of individuals, the entire mitogenome. In addition, due to potential of species misidentification with morphologically similar Polar cod (Arctogadus glacialis), we used ddRAD-Seq data to determine the level of divergence between species and identify species-specific markers.
Based on the findings presented here, Arctic cod across the Pacific Arctic (Bering, Chukchi, and Beaufort Seas) comprise a single panmictic population with high genetic diversity compared to other gadids. High genetic diversity was indicated across all 13 protein-coding genes in the mitogenome. In addition, we found moderate levels of genetic diversity in the nuclear microsatellite loci, with highest diversity found in the Chukchi Sea. Our analyses of markers from both marker classes (nuclear microsatellite fragment data and mtDNA cytb sequence data) failed to uncover a signal of microgeographic genetic structure within Arctic cod across the three regions, within the Alaskan Beaufort Sea, or between near-shore or offshore habitats. Further, data from a subset of mitogenomes revealed no genetic differentiation between Bering, Chukchi, and Beaufort seas populations for Arctic cod, Saffron cod (Eleginus gracilis), or Walleye pollock (Gadus chalcogrammus). However, we uncovered significant differences in the distribution of microsatellite alleles between the southern Chukchi and central and eastern Beaufort Sea samples of Arctic cod. Finally, using ddRAD-Seq data, we identified species-specific markers and in conjunction with mitogenome data, identified an Arctic cod x Polar cod hybrid in western Canadian Beaufort Sea.
Overall, the lack of genetic structure among Arctic cod within the Bering, Chukchi and Beaufort seas of Alaska is concordant with the absence of geographic barriers to dispersal and typical among marine fishes. Arctic cod may exhibit a genetic pattern of isolation-by-distance, whereby populations in closer geographic proximity are more genetically similar than more distant populations. As this signal is only found between our two fartherest localities, data from populations elsewhere in the species’ global range are needed to determine if this is a general characteristic. Further, tests for selection suggested a limited role for natural selection acting on the mitochondrial genome of Arctic cod, but do not exclude the possibility of selection on genes involved in nuclear-mitogenome interactions. Unlike previous genetic assessment of Arctic cod sampled from the Chukchi Sea, the high levels of genetic diversity found in Arctic cod assayed in this study, across regions, suggests that the species in the Beaufort and Chukchi seas does not suffer from low levels of genetic variation, at least at neutral genetic markers. The large census size of Arctic cod may allow this species to retain high levels of genetic diversity. In addition, we discovered the presence of hybridization between Arctic and Polar cod (although low in frequency). Hybridization is expected to occur when environmental changes modify species distributions that result in contact between species that were previously separated. In such cases, hybridization may be an evolutionary mechanism that promotes an increase in genetic diversity that may provide species occupying changing environments with locally-adapted genotypes and, therefore, phenotypes. Natural selection can only act on the standing genetic variation present within a population. Therefore, given its higher levels of genetic diversity in combination with a large population size, Arctic cod may be resilient to current and future environmental change, as high genetic diversity is expected to increase opportunities for positive selection to act on genetic variants beneficial in different environments, regardless of the source of that genetic variation.
","language":"English","publisher":"Bureau of Ocean Energy Management","usgsCitation":"Wilson, R.E., Sage, G.K., Sonsthagen, S.A., Gravley, M.C., Menning, D.M., and Talbot, S.L., 2017, Genomics of Arctic cod: OCS Study BOEM 2017-066, vi, 81 p.","productDescription":"vi, 81 p.","numberOfPages":"92","ipdsId":"IP-091280","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":354936,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":354385,"type":{"id":11,"text":"Document"},"url":"https://www.boem.gov/BOEM-2017-066-Genomics-of-Arctic-Cod/"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e62fe4b060350a15d262","contributors":{"authors":[{"text":"Wilson, Robert E. 0000-0003-1800-0183 rewilson@usgs.gov","orcid":"https://orcid.org/0000-0003-1800-0183","contributorId":5718,"corporation":false,"usgs":true,"family":"Wilson","given":"Robert","email":"rewilson@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":736173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sage, George K. 0000-0003-1431-2286 ksage@usgs.gov","orcid":"https://orcid.org/0000-0003-1431-2286","contributorId":87833,"corporation":false,"usgs":true,"family":"Sage","given":"George","email":"ksage@usgs.gov","middleInitial":"K.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":736174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sonsthagen, Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":736175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gravley, Megan C. 0000-0002-4947-0236 mgravley@usgs.gov","orcid":"https://orcid.org/0000-0002-4947-0236","contributorId":202812,"corporation":false,"usgs":true,"family":"Gravley","given":"Megan","email":"mgravley@usgs.gov","middleInitial":"C.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":736176,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Menning, Damian M. 0000-0003-3547-3062 dmenning@usgs.gov","orcid":"https://orcid.org/0000-0003-3547-3062","contributorId":205131,"corporation":false,"usgs":true,"family":"Menning","given":"Damian","email":"dmenning@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":736177,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":736178,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70196469,"text":"70196469 - 2017 - LAGOS-NE: a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of US lakes","interactions":[],"lastModifiedDate":"2018-04-10T10:56:16","indexId":"70196469","displayToPublicDate":"2017-12-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5091,"text":"GigaScience","active":true,"publicationSubtype":{"id":10}},"title":"LAGOS-NE: a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of US lakes","docAbstract":"Understanding the factors that affect water quality and the ecological services provided by freshwater ecosystems is an urgent global environmental issue. Predicting how water quality will respond to global changes not only requires water quality data, but also information about the ecological context of individual water bodies across broad spatial extents. Because lake water quality is usually sampled in limited geographic regions, often for limited time periods, assessing the environmental controls of water quality requires compilation of many data sets across broad regions and across time into an integrated database. LAGOS-NE accomplishes this goal for lakes in the northeastern-most 17 US states.
LAGOS-NE contains data for 51 101 lakes and reservoirs larger than 4 ha in 17 lake-rich US states. The database includes 3 data modules for: lake location and physical characteristics for all lakes; ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes; and in situ measurements of lake water quality for a subset of the lakes from the past 3 decades for approximately 2600–12 000 lakes depending on the variable. The database contains approximately 150 000 measures of total phosphorus, 200 000 measures of chlorophyll, and 900 000 measures of Secchi depth. The water quality data were compiled from 87 lake water quality data sets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. This database is one of the largest and most comprehensive databases of its type because it includes both in situ measurements and ecological context data. Because ecological context can be used to study a variety of other questions about lakes, streams, and wetlands, this database can also be used as the foundation for other studies of freshwaters at broad spatial and ecological scales.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gigascience/gix101","usgsCitation":"Soranno, P.A., Bacon, L.C., Beauchene, M., Bednar, K.E., Bissell, E.G., Boudreau, C.K., Boyer, M.G., Bremigan, M.T., Carpenter, S.R., Carr, J.W., Cheruvelil, K.S., Christel, S.T., Claucherty, M., Collins, S.M., Conroy, J.D., Downing, J.A., Dukett, J., Fergus, C.E., Filstrup, C.T., Funk, C., Gonzalez, M.J., Green, L.T., Gries, C., Halfman, J.D., Hamilton, S.K., Hanson, P.C., Henry, E.N., Herron, E.M., Hockings, C., Jackson, J.R., Jacobson-Hedin, K., Janus, L.L., Jones, W.W., Jones, J., Keson, C.M., King, K.B., Kishbaugh, S.A., Lapierre, J., Lathrop, B., Latimore, J.A., Lee, Y., Lottig, N.R., Lynch, J.A., Matthews, L.J., McDowell, W.H., Moore, K.E., Neff, B., Nelson, S.J., Oliver, S., Pace, M.L., Pierson, D.C., Poisson, A.C., Pollard, A.I., Post, D.M., Reyes, P.O., Rosenberry, D., Roy, K.M., Rudstam, L.G., Sarnelle, O., Schuldt, N.J., Scott, C.E., Skaff, N.K., Smith, N.J., Spinelli, N.R., Stachelek, J.J., Stanley, E.H., Stoddard, J.L., Stopyak, S.B., Stow, C.A., Tallant, J.M., Tan, P., Thorpe, A.P., Vanni, M.J., Wagner, T., Watkins, G., Weathers, K.C., Webster, K.E., White, J., Wilmes, M.K., and Yuan, S., 2017, LAGOS-NE: a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of US lakes: GigaScience, v. 6, no. 12, p. 1-22, https://doi.org/10.1093/gigascience/gix101.","productDescription":"22 p.","startPage":"1","endPage":"22","ipdsId":"IP-088165","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":469264,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gigascience/gix101","text":"Publisher Index Page"},{"id":353287,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Iowa, Illinois, Indiana, Massachusetts, 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In a crude oil contaminated aquifer, biodegradation of petroleum hydrocarbons is coupled with the reduction of ferric iron (Fe(III)) hydroxides in aquifer sediments. As a result, naturally occurring arsenic (As) adsorbed to Fe(III) hydroxides in the aquifer sediment is mobilized from sediment into groundwater. However, Fe(III) in sediment of other zones of the aquifer has the capacity to attenuate dissolved As via resorption. In order to better evaluate how long-term biodegradation coupled with Fe-reduction and As mobilization can redistribute As mass in contaminated aquifer, we quantified mass partitioning of Fe and As in the aquifer based on field observation data. Results show that Fe and As are spatially correlated in both groundwater and aquifer sediments. Mass partitioning calculations demonstrate that 99.9% of Fe and 99.5% of As are associated with aquifer sediment. The sediments act as both sources and sinks for As, depending on the redox conditions in the aquifer. Calculations reveal that at least 78% of the original As in sediment near the oil has been mobilized into groundwater over the 35-year lifespan of the plume. However, the calculations also show that only a small percentage of As (∼0.5%) remains in groundwater, due to resorption onto sediment. At the leading edge of the plume, where groundwater is suboxic, sediments sequester Fe and As, causing As to accumulate to concentrations 5.6 times greater than background concentrations. Current As sinks can serve as future sources of As as the plume evolves over time. The mass balance approach used in this study can be applied to As cycling in other aquifers where groundwater As results from biodegradation of an organic carbon point source coupled with Fe reduction.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2017.08.110","usgsCitation":"Ziegler, B.A., Schreiber, M.E., Cozzarelli, I.M., and Crystal, N.G., 2017, A mass balance approach to investigate arsenic cycling in a petroleum plume: Environmental Pollution, v. 231, no. 2, p. 1351-1361, https://doi.org/10.1016/j.envpol.2017.08.110.","productDescription":"11 p.","startPage":"1351","endPage":"1361","ipdsId":"IP-087989","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":469278,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envpol.2017.08.110","text":"Publisher Index Page"},{"id":438139,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7280648","text":"USGS data release","linkHelpText":"Arsenic and Iron data for mass balance calculations to investigate arsenic cycling in a petroleum plume"},{"id":351998,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"231","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee7aae4b0da30c1bfc337","contributors":{"authors":[{"text":"Ziegler, Brady A.","contributorId":138960,"corporation":false,"usgs":false,"family":"Ziegler","given":"Brady","email":"","middleInitial":"A.","affiliations":[{"id":12594,"text":"Department of Geosciences, Virginia Tech, Blacksburg, VA","active":true,"usgs":false}],"preferred":false,"id":729557,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schreiber, Madeline E.","contributorId":138959,"corporation":false,"usgs":false,"family":"Schreiber","given":"Madeline","email":"","middleInitial":"E.","affiliations":[{"id":12594,"text":"Department of Geosciences, Virginia Tech, Blacksburg, VA","active":true,"usgs":false}],"preferred":false,"id":729558,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cozzarelli, Isabelle M. 0000-0002-5123-1007 icozzare@usgs.gov","orcid":"https://orcid.org/0000-0002-5123-1007","contributorId":1693,"corporation":false,"usgs":true,"family":"Cozzarelli","given":"Isabelle","email":"icozzare@usgs.gov","middleInitial":"M.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":729556,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crystal, Ng. 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The considerable morphological diversity of these coasts and the variability of the hydrodynamic forcing that they are exposed to make predicting wave-induced flooding a challenge. A process-based wave-resolving hydrodynamic model (XBeach Non-Hydrostatic, “XBNH”) was used to create a large synthetic database for use in a “Bayesian Estimator for Wave Attack in Reef Environments” (BEWARE), relating incident hydrodynamics and coral reef geomorphology to coastal flooding hazards on reef-lined coasts. Building on previous work, BEWARE improves system understanding of reef hydrodynamics by examining the intrinsic reef and extrinsic forcing factors controlling runup and flooding on reef-lined coasts. The Bayesian estimator has high predictive skill for the XBNH model outputs that are flooding indicators, and was validated for a number of available field cases. It was found that, in order to accurately predict flooding hazards, water depth over the reef flat, incident wave conditions, and reef flat width are the most essential factors, whereas other factors such as beach slope and bed friction due to the presence or absence of corals are less important. BEWARE is a potentially powerful tool for use in early warning systems or risk assessment studies, and can be used to make projections about how wave-induced flooding on coral reef-lined coasts may change due to climate change.
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Satellite radar provides a method for measuring changes in topographic height over a given time period to an accuracy of meters, either by measuring the width of radar shadow cast by steep sided features, or by measuring the difference in radar phase between two sensors separated in space. We measure height changes, and hence estimate extruded lava volume flux, at El Reventador, Ecuador, between 2011 and 2016, using data from the RADARSAT-2 and TanDEM-X satellite missions. We find that 39 new lava flows were extruded between 9 February 2012 and 24 August 2016, with a cumulative volume of 44.8M m3 dense rock equivalent and a gradually decreasing eruption rate. The average dense rock rate of lava extrusion during this time is 0.31 ± 0.02 m3 s−1, which is similar to the long-term average from 1972 to 2016. Apart from a volumetrically small dyke opening event between 9 March and 10 June 2012, lava extrusion at El Reventador is not accompanied by any significant magmatic ground deformation. We use a simple physics-based model to estimate that the volume of the magma reservoir under El Reventador is greater than 3 km3. Our lava extrusion data can be equally well fit by models representing a closed reservoir depressurising during the eruption with no magma recharge, or an open reservoir with a time-constant magma recharge rate of up to 0.35 ± 0.01 m3 s−1.
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F.","contributorId":202024,"corporation":false,"usgs":false,"family":"Naranjo","given":"M.","email":"","middleInitial":"F.","affiliations":[{"id":36324,"text":"Instituto Geofísico, Escuela Politécnica Nacional, Quito, Ecuador","active":true,"usgs":false}],"preferred":false,"id":727396,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mothes, P.","contributorId":202025,"corporation":false,"usgs":false,"family":"Mothes","given":"P.","email":"","affiliations":[{"id":36326,"text":"COMET, School of Earth and Environment, University of Leeds, Leeds, UK","active":true,"usgs":false}],"preferred":false,"id":727397,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70190624,"text":"sir20175092 - 2017 - Groundwater flux and nutrient loading in the northeast section of Bear Lake, Muskegon County, Michigan, 2015","interactions":[],"lastModifiedDate":"2017-11-30T16:58:14","indexId":"sir20175092","displayToPublicDate":"2017-11-30T11:30:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5092","title":"Groundwater flux and nutrient loading in the northeast section of Bear Lake, Muskegon County, Michigan, 2015","docAbstract":"Bear Lake in North Muskegon, Michigan, is listed as part of the Muskegon Lake area of concern as designated by the U.S. Environmental Protection Agency. This area of concern was designated as a result of eutrophication and beneficial use impairments. On the northeast end of Bear Lake, two man-made retention ponds (Willbrandt Pond East and Willbrandt Pond West), formerly used for celery farming, may contribute nutrients to Bear Lake. Willbrandt Ponds (East and West) were previously muck fields that were actively used for celery farming from the early 1900s until 2002. The restoration and reconnection of the Willbrandt Ponds into Bear Lake prompted concerns of groundwater nutrient loading into Bear Lake. Studies done by the State of Michigan and Grand Valley State University revised initial internal phosphorus load estimates and indicated an imbalance in the phosphorus budget in Bear Lake. From June through November 2015, the U.S. Geological Survey (USGS) did an investigative study to quantify the load of nutrients from shallow groundwater around the Willbrandt Ponds in an effort to update the phosphorus budget to Bear Lake. Seven sampling locations were established, including five shallow groundwater wells and two surface-water sites, in the Willbrandt pond study area and Bear Lake. A total of 12 nutrient samples and discrete water-level measurements were collected from each site from June through November 2015. Continuous water-level data were recorded for both surface-water monitoring locations for the entire sampling period.
Water-level data indicated that Willbrandt Pond West had the highest average water-level elevation of all sites monitored, which indicated the general direction of flux is from Willbrandt Pond West to Bear Lake. Nutrient and chloride loading from Willbrandt Pond West to Bear Lake was calculated using two distinct methods: Dupuit and direct seepage methods. Shallow groundwater loading calculations were determined by using groundwater levels to first determine a flux of shallow groundwater, then nutrient concentrations to determine a load. It was determined that Willbrandt Pond East and Willbrandt Pond West contributed between 2 to 4 percent of the total annual phosphorus load to Bear Lake by way of shallow groundwater flow. Annual loads calculated for other constituents include orthophosphate (40–100 pounds per year [lb P/yr]), total nitrogen (200–830 lb/yr), chloride (12,700–32,100 lb/yr), and ammonia (130–670 lb N/yr). Study results indicated that mean groundwater and surface-water nutrient concentrations calculated in this study were higher than reported Michigan statewide values. The data collected in this study allow understanding of groundwater nutrient loading into Bear Lake in an effort to help inform future restoration and management decisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175092","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Totten, A.R., Maurer, J.A., and Duris, J.W., 2017, Groundwater flux and nutrient loading in the northeast section of Bear Lake, Muskegon County, Michigan, 2015: U.S. Geological Survey Scientific Investigations Report 2017–5092, 16 p., https://doi.org/10.3133/sir20175092.","productDescription":"v, 16 p.","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-074168","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"links":[{"id":349260,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F73J3BVJ","text":"USGS data release","description":"USGS data release","linkHelpText":"Groundwater Seepage Measurements in Northeast Section of Bear Lake, Muskegon County, Michigan, October 2015"},{"id":349259,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5092/sir20175092.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5092"},{"id":349258,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5092/coverthb.jpg"}],"country":"United States","state":"Michigan","county":"Muskegon County","otherGeospatial":"Bear Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.27194404602051,\n 43.25970598443754\n ],\n [\n -86.25323295593262,\n 43.25970598443754\n ],\n [\n -86.25323295593262,\n 43.27145609469072\n ],\n [\n -86.27194404602051,\n 43.27145609469072\n ],\n [\n -86.27194404602051,\n 43.25970598443754\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
6520 Mercantile Way
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Lansing, MI 48911
The exchange of groundwater and surface water (GW-SW), including dissolved constituents and energy, represents a critical yet challenging characterization problem for hydrogeologists and stream ecologists. Here, we describe the use of a suite of high spatial-resolution remote-sensing techniques, collected using a small unmanned aircraft system (sUAS), to provide novel and complementary data to analyze GW-SW exchange. sUAS provided centimeter-scale resolution topography and water surface elevations, which are often drivers of exchange along the river corridor. Additionally, sUAS-based vegetation imagery, vegetation-top elevation, and normalized difference vegetation index (NDVI) mapping indicated GW-SW exchange patterns that are difficult to characterize from the land surface and may not be resolved from coarser satellite-based imagery. We combined these data with estimates of sediment hydraulic conductivity to provide a direct estimate of GW “shortcutting” through meander necks, which was corroborated by temperature data at the riverbed interface.
","language":"English","publisher":"AGU","doi":"10.1002/2017GL075836","usgsCitation":"Pai, H., Malenda, H., Briggs, M.A., Singha, K., González-Pinzón, R., Gooseff, M., Tyler, S., and AirCTEMPS Team, 2017, Potential for Small Unmanned Aircraft Systems applications for identifying groundwater-surface water exchange in a meandering river reach: Geophysical Research Letters, v. 44, no. 23, p. 11868-11877, https://doi.org/10.1002/2017GL075836.","productDescription":"10 p.","startPage":"11868","endPage":"11877","ipdsId":"IP-092215","costCenters":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"links":[{"id":469289,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017gl075836","text":"Publisher Index Page"},{"id":438142,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7J9658M","text":"USGS data release","linkHelpText":"Fiber-optic distributed temperature data collected along the streambed of the East River, Crested Butte, CO, USA"},{"id":349585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"23","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-11","publicationStatus":"PW","scienceBaseUri":"5a60fafbe4b06e28e9c22a78","contributors":{"authors":[{"text":"Pai, H.","contributorId":201023,"corporation":false,"usgs":false,"family":"Pai","given":"H.","email":"","affiliations":[],"preferred":false,"id":724125,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Malenda, H.","contributorId":201024,"corporation":false,"usgs":false,"family":"Malenda","given":"H.","affiliations":[],"preferred":false,"id":724126,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":724124,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Singha, K.","contributorId":201025,"corporation":false,"usgs":false,"family":"Singha","given":"K.","email":"","affiliations":[],"preferred":false,"id":724127,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"González-Pinzón, R.","contributorId":198635,"corporation":false,"usgs":false,"family":"González-Pinzón","given":"R.","affiliations":[],"preferred":false,"id":724128,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gooseff, M.","contributorId":201026,"corporation":false,"usgs":false,"family":"Gooseff","given":"M.","email":"","affiliations":[],"preferred":false,"id":724129,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tyler, S.W.","contributorId":85740,"corporation":false,"usgs":true,"family":"Tyler","given":"S.W.","email":"","affiliations":[],"preferred":false,"id":724130,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"AirCTEMPS Team","contributorId":201028,"corporation":true,"usgs":false,"organization":"AirCTEMPS Team","id":724134,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70195700,"text":"70195700 - 2017 - North American Breeding Bird Survey in Mississippi","interactions":[],"lastModifiedDate":"2018-03-01T10:57:00","indexId":"70195700","displayToPublicDate":"2017-11-30T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2761,"text":"Mississippi Kite","active":true,"publicationSubtype":{"id":10}},"title":"North American Breeding Bird Survey in Mississippi","docAbstract":"Does it seem like you are hearing fewer Northern Bobwhite (Colinus virginianus) calls in recent years than you remember when you were younger? Conversely, have you also noticed hearing more “cooing” of Eurasian Collared-Doves (Streptopelia decaocto)? Do such experiences reflect changes in bird populations or are they false impressions? Well, fortunately for us, we have one of the most powerful wildlife data sets freely available, only a few mouse clicks away, for divining these answers. For most of our songbirds, their population trends are estimated from data gathered during the North American Breeding Bird Survey (BBS).","language":"English","publisher":"Mississippi Ornithological Society","usgsCitation":"Twedt, D.J., and Pardieck, K.L., 2017, North American Breeding Bird Survey in Mississippi: Mississippi Kite, v. 47, no. 2, p. 70-83.","productDescription":"14 p.","startPage":"70","endPage":"83","ipdsId":"IP-092988","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":352116,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":352087,"type":{"id":15,"text":"Index Page"},"url":"https://sora.unm.edu/node/140488"}],"country":"United States","state":"Mississippi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.22021484375,\n 35.0120020431607\n ],\n [\n -90.318603515625,\n 35.0120020431607\n ],\n [\n -90.5712890625,\n 34.79576153473033\n ],\n [\n -90.7470703125,\n 34.46127728843705\n ],\n [\n -90.9228515625,\n 34.243594729697406\n ],\n [\n -91.241455078125,\n 33.8247936182649\n ],\n [\n -91.263427734375,\n 33.61461929233378\n ],\n [\n -91.23046875,\n 33.137551192346145\n ],\n [\n -91.14257812499999,\n 32.759562025650126\n ],\n [\n -91.131591796875,\n 32.509761735919426\n ],\n [\n -90.999755859375,\n 32.32427558887655\n ],\n [\n -91.131591796875,\n 32.091882620021806\n ],\n [\n -91.25244140624999,\n 31.924192605327708\n ],\n [\n -91.373291015625,\n 31.82156451492074\n ],\n [\n -91.47216796875,\n 31.615965936476076\n ],\n [\n -91.5380859375,\n 31.44741029142872\n ],\n [\n -91.593017578125,\n 31.287939892641734\n ],\n [\n -91.669921875,\n 31.21280145833882\n ],\n [\n -91.636962890625,\n 31.109388560814963\n ],\n [\n -91.702880859375,\n 31.024694128525137\n ],\n [\n -89.7308349609375,\n 30.996445897426373\n ],\n [\n -89.813232421875,\n 30.845647420182598\n ],\n [\n -89.82421875,\n 30.581179257386985\n ],\n [\n -89.769287109375,\n 30.50548389892728\n ],\n [\n -89.6484375,\n 30.344435586368462\n ],\n [\n -89.615478515625,\n 30.183121842195515\n ],\n [\n -89.50561523437499,\n 30.15462722077597\n ],\n [\n -89.3408203125,\n 30.287531589298727\n ],\n [\n -89.1595458984375,\n 30.344435586368462\n ],\n [\n -88.9013671875,\n 30.391830328088137\n ],\n [\n -88.7310791015625,\n 30.330212685432734\n ],\n [\n -88.63220214843749,\n 30.358656420788783\n ],\n [\n -88.472900390625,\n 30.306503259848835\n ],\n [\n -88.39599609375,\n 30.358656420788783\n ],\n [\n -88.472900390625,\n 31.952162238024975\n ],\n [\n -88.11035156249999,\n 34.88593094075317\n ],\n [\n -88.22021484375,\n 35.0120020431607\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee7ace4b0da30c1bfc355","contributors":{"authors":[{"text":"Twedt, Daniel J. 0000-0003-1223-5045 dtwedt@usgs.gov","orcid":"https://orcid.org/0000-0003-1223-5045","contributorId":398,"corporation":false,"usgs":true,"family":"Twedt","given":"Daniel","email":"dtwedt@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":729753,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pardieck, Keith L. 0000-0003-2779-4392 kpardieck@usgs.gov","orcid":"https://orcid.org/0000-0003-2779-4392","contributorId":4104,"corporation":false,"usgs":true,"family":"Pardieck","given":"Keith","email":"kpardieck@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":729754,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70194474,"text":"70194474 - 2017 - EROD activity, chromosomal damage, and oxidative stress in response to contaminants exposure in tree swallow (Tachycineta bicolor) nestlings from Great Lakes Areas of Concern","interactions":[],"lastModifiedDate":"2017-11-29T10:29:20","indexId":"70194474","displayToPublicDate":"2017-11-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1479,"text":"Ecotoxicology","active":true,"publicationSubtype":{"id":10}},"title":"EROD activity, chromosomal damage, and oxidative stress in response to contaminants exposure in tree swallow (Tachycineta bicolor) nestlings from Great Lakes Areas of Concern","docAbstract":"Tree swallow, Tachycineta bicolor, nestlings were collected from 60 sites in the Great Lakes, which included multiple sites within 27 Areas of Concern (AOCs) and six sites not listed as AOCs from 2010 to 2014. Nestlings, approximately 12 days-of-age, were evaluated for ethoxyresorufin-O-dealkylase (EROD) activity, chromosomal damage, and six measures of oxidative stress. Data on each of these biomarkers were divided into four equal numbered groups from the highest to lowest values and the groups were compared to contaminant concentrations using multivariate analysis. Contaminant concentrations, from the same nestlings, included polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), perfluorinated compounds (PFCs), and 17 elements. Alkylated polycyclic aromatic hydrocarbons (aPAHs) and parent PAHs (pPAHs) were measured in pooled nestling dietary samples. Polychlorinated dibenzo-p-dioxins, polychlorinated dibenzofurans, and pesticides were measured in sibling eggs. Concentrations of aPAHs, pPAHs, chlordane, dieldrin, heptachlor, and PCBs, in that order, were the major contributors to the significant differences between the lowest and highest EROD activities; PFCs, PBDEs, the remaining pesticides, and all elements were of secondary importance. The four categories of chromosomal damage did not separate out well based on the contaminants measured. Concentrations of aPAHs, pPAHs, heptachlor, PCBs, chlordane, and dieldrin were the major contributors to the significant differences between the lowest and highest activities of two oxidative stress measures, total sulfhydryl (TSH) activity and protein bound sulfhydryl (PBSH) activity. The four categories of thiobarbituric acid reacting substances (TBARS), oxidized glutathione (GSSG), reduced glutathione (GSH), and the ratio of GSSG/GSH did not separate well based on the contaminants measured.","language":"English","publisher":"Springer","doi":"10.1007/s10646-017-1863-7","usgsCitation":"Custer, T.W., Custer, C.M., Dummer, P.M., Bigorgne, E., Oziolor, E., Karouna-Renier, N., Schultz, S.L., Erickson, R.A., Aagaard, K., and Matson, C., 2017, EROD activity, chromosomal damage, and oxidative stress in response to contaminants exposure in tree swallow (Tachycineta bicolor) nestlings from Great Lakes Areas of Concern: Ecotoxicology, v. 26, no. 10, p. 1392-1407, https://doi.org/10.1007/s10646-017-1863-7.","productDescription":"16 p.","startPage":"1392","endPage":"1407","ipdsId":"IP-085851","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences 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University","active":true,"usgs":false}],"preferred":false,"id":723997,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70194473,"text":"70194473 - 2017 - Constraining the magmatic system at Mount St. Helens (2004–2008) using Bayesian inversion with physics-based models including gas escape and crystallization","interactions":[],"lastModifiedDate":"2017-11-29T10:34:41","indexId":"70194473","displayToPublicDate":"2017-11-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Constraining the magmatic system at Mount St. Helens (2004–2008) using Bayesian inversion with physics-based models including gas escape and crystallization","docAbstract":"Physics-based models of volcanic eruptions track conduit processes as functions of depth and time. When used in inversions, these models permit integration of diverse geological and geophysical data sets to constrain important parameters of magmatic systems. We develop a 1-D steady state conduit model for effusive eruptions including equilibrium crystallization and gas transport through the conduit and compare with the quasi-steady dome growth phase of Mount St. Helens in 2005. Viscosity increase resulting from pressure-dependent crystallization leads to a natural transition from viscous flow to frictional sliding on the conduit margin. Erupted mass flux depends strongly on wall rock and magma permeabilities due to their impact on magma density. Including both lateral and vertical gas transport reveals competing effects that produce nonmonotonic behavior in the mass flux when increasing magma permeability. Using this physics-based model in a Bayesian inversion, we link data sets from Mount St. Helens such as extrusion flux and earthquake depths with petrological data to estimate unknown model parameters, including magma chamber pressure and water content, magma permeability constants, conduit radius, and friction along the conduit walls. Even with this relatively simple model and limited data, we obtain improved constraints on important model parameters. We find that the magma chamber had low (<5wt%) total volatiles and that the magma permeability scale is well constrained at ~10-11.4 m2 to reproduce observed dome rock porosities. Compared with previous results, higher magma overpressure and lower wall friction are required to compensate for increased viscous resistance while keeping extrusion rate at the observed value.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017JB014343","usgsCitation":"Wong, Y., Segall, P., Bradley, A., and Anderson, K.R., 2017, Constraining the magmatic system at Mount St. Helens (2004–2008) using Bayesian inversion with physics-based models including gas escape and crystallization: Journal of Geophysical Research B: Solid Earth, v. 122, no. 10, p. 7789-7812, https://doi.org/10.1002/2017JB014343.","productDescription":"34 p.","startPage":"7789","endPage":"7812","ipdsId":"IP-086340","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":469293,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1411224","text":"External Repository"},{"id":349506,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mount St. Helens","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.63214111328125,\n 45.94160076422081\n ],\n [\n -121.77246093750001,\n 45.94160076422081\n ],\n [\n -121.77246093750001,\n 46.494610770689384\n ],\n [\n -122.63214111328125,\n 46.494610770689384\n ],\n [\n -122.63214111328125,\n 45.94160076422081\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"122","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-30","publicationStatus":"PW","scienceBaseUri":"5a60fafce4b06e28e9c22a90","contributors":{"authors":[{"text":"Wong, Ying-Qi","contributorId":200978,"corporation":false,"usgs":false,"family":"Wong","given":"Ying-Qi","email":"","affiliations":[],"preferred":false,"id":723991,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Segall, Paul","contributorId":75942,"corporation":false,"usgs":true,"family":"Segall","given":"Paul","affiliations":[],"preferred":false,"id":723992,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradley, Andrew","contributorId":200980,"corporation":false,"usgs":false,"family":"Bradley","given":"Andrew","affiliations":[],"preferred":false,"id":723993,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":723990,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70194483,"text":"70194483 - 2017 - Combining remote sensing and water-balance evapotranspiration estimates for the conterminous United States","interactions":[],"lastModifiedDate":"2022-04-22T16:02:15.153901","indexId":"70194483","displayToPublicDate":"2017-11-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Combining remote sensing and water-balance evapotranspiration estimates for the conterminous United States","docAbstract":"Evapotranspiration (ET) is a key component of the hydrologic cycle, accounting for ~70% of precipitation in the conterminous U.S. (CONUS), but it has been a challenge to predict accurately across different spatio-temporal scales. The increasing availability of remotely sensed data has led to significant advances in the frequency and spatial resolution of ET estimates, derived from energy balance principles with variables such as temperature used to estimate surface latent heat flux. Although remote sensing methods excel at depicting spatial and temporal variability, estimation of ET independently of other water budget components can lead to inconsistency with other budget terms. Methods that rely on ground-based data better constrain long-term ET, but are unable to provide the same temporal resolution. Here we combine long-term ET estimates from a water-balance approach with the SSEBop (operational Simplified Surface Energy Balance) remote sensing-based ET product for 2000–2015. We test the new combined method, the original SSEBop product, and another remote sensing ET product (MOD16) against monthly measurements from 119 flux towers. The new product showed advantages especially in non-irrigated areas where the new method showed a coefficient of determination R2 of 0.44, compared to 0.41 for SSEBop or 0.35 for MOD16. The resulting monthly data set will be a useful, unique contribution to ET estimation, due to its combination of remote sensing-based variability and ground-based long-term water balance constraints.
","language":"English","publisher":"MDPI","doi":"10.3390/rs9121181","usgsCitation":"Reitz, M., Senay, G., and Sanford, W.E., 2017, Combining remote sensing and water-balance evapotranspiration estimates for the conterminous United States: Remote Sensing, v. 9, no. 12, 1181, 17 p.; Data release, https://doi.org/10.3390/rs9121181.","productDescription":"1181, 17 p.; Data release","ipdsId":"IP-090961","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":469292,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs9121181","text":"Publisher Index Page"},{"id":349568,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":397955,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QC02FK","text":"USGS data 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Information about volcanic activity comes from a variety of sources: news organisations, emergency management personnel, individuals (both public and official) and volcano monitoring agencies. Once posted, this information is easily shared, increasing the reach to a much broader population than the original audience. The onset and popularity of social media as a vehicle for eruption information dissemination has presented many benefits as well as challenges, and points towards a need for a more unified system for information. This includes volcano observatories using social media as an official channels to distribute activity statements, forecasts and predictions on social media, in addition to the archiving of images and data activity. This chapter looks at two examples of projects that collect / disseminate information regarding volcanic crises and eruptive activity utilizing social media sources. Based on those examples, recommendations are made to volcanic observatories in relation to the use of social media as a two-way communication tool. These recommendations include: using social media as a two-way dialogue to communicate and receive information directly from the public and other sources; stating that the social media account is from an official source; and, posting types of information that the public are seeking such as images, videos and figures.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Advances in Volcanology","language":"English","publisher":"Springer","doi":"10.1007/11157_2015_13","usgsCitation":"Sennert, S.K., Klemetti, E.W., and Bird, D., 2017, Role of social media and networking in volcanic crises and communication, chap. of Advances in Volcanology, p. 1-12, https://doi.org/10.1007/11157_2015_13.","productDescription":"12 p.","startPage":"1","endPage":"12","ipdsId":"IP-066483","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":487306,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/11157_2015_13","text":"Publisher Index Page"},{"id":349555,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-26","publicationStatus":"PW","scienceBaseUri":"5a60fafde4b06e28e9c22aa1","contributors":{"authors":[{"text":"Sennert, Sally K. ssennert@usgs.gov","contributorId":5459,"corporation":false,"usgs":true,"family":"Sennert","given":"Sally","email":"ssennert@usgs.gov","middleInitial":"K.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":719758,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Klemetti, Erik W.","contributorId":139092,"corporation":false,"usgs":false,"family":"Klemetti","given":"Erik","email":"","middleInitial":"W.","affiliations":[{"id":12650,"text":"Denison University","active":true,"usgs":false}],"preferred":false,"id":719759,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bird, Deanne","contributorId":199688,"corporation":false,"usgs":false,"family":"Bird","given":"Deanne","email":"","affiliations":[],"preferred":false,"id":719760,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70190686,"text":"sir20175102 - 2017 - Procedure for calculating estimated ultimate recoveries of wells in the Mississippian Barnett Shale, Bend Arch–Fort Worth Basin Province of north-central Texas","interactions":[],"lastModifiedDate":"2017-11-29T10:11:21","indexId":"sir20175102","displayToPublicDate":"2017-11-28T17:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5102","title":"Procedure for calculating estimated ultimate recoveries of wells in the Mississippian Barnett Shale, Bend Arch–Fort Worth Basin Province of north-central Texas","docAbstract":"In 2015, the U.S. Geological Survey published an assessment of technically recoverable continuous oil and gas resources of the Mississippian Barnett Shale in the Bend Arch–Fort Worth Basin Province of north-central Texas. Of the two assessment units involved in the overall assessment, one included a roughly equal number of oil wells and gas wells as classified by the U.S. Geological Survey’s standard of gas wells having production greater than or equal to 20,000 cubic feet of gas per barrel of oil and oil wells having production less than 20,000 cubic feet of gas per barrel of oil. As a result, estimated ultimate recoveries (EURs) were calculated for both oil wells and gas wells in one of the assessment units. Generally, only gas EURs or only oil EURs are calculated for an assessment unit. These EURs were calculated with data from IHS MarkitTM using DeclinePlus software in the Harmony interface and were a major component of the quantitative resource assessment. The calculated mean EURs ranged from 235 to 2,078 million cubic feet of gas and 21 to 39 thousand barrels of oil for various subsets of wells.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175102","usgsCitation":"Leathers-Miller, H.M., 2017, Procedure for calculating estimated ultimate recoveries of wells in the Mississippian Barnett Shale, Bend Arch–Fort Worth Basin Province of north-central Texas: U.S. Geological Survey Scientific Investigations Report 2017–5102, 6 p., https://doi.org/10.3133/sir20175102.","productDescription":"iii, 6 p.","numberOfPages":"14","onlineOnly":"Y","ipdsId":"IP-079909","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":349345,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5102/sir20175102.pdf","text":"Report","size":"4.53 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5102"},{"id":349344,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5102/coverthb.jpg"},{"id":349347,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20153078","text":"Fact Sheet 2015–3078 ","linkHelpText":"Assessment of undiscovered shale gas and shale oil resources in the Mississippian Barnett Shale, Bend Arch–Fort Worth Basin Province, north-central Texas"}],"country":"United States","state":"Texas","otherGeospatial":"Bend Arch-Fort Worth Basin Province, Mississippian Barnett Shale","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100,\n 30\n ],\n [\n -96,\n 30\n ],\n [\n -96,\n 35\n ],\n [\n -100,\n 35\n ],\n [\n -100,\n 30\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
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Scientists from the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center conducted a seasonal collection of estuarine, marsh, and sandy washover surface sediments from Chincoteague Bay, Tom’s Cove, and the surrounding Assateague Island and Delmarva Peninsula in March–April and October 2014, after Hurricane Sandy. Micropaleontology samples were collected as part of a complementary USGS Coastal and Marine Geology Program Sea-level and Storm Impacts on Estuarine Environments and Shorelines project study. For comparison with estuarine and overwash deposited foraminifera, a group of scientists from the USGS Woods Hole Coastal and Marine Science Center in Massachusetts collected samples offshore of Assateague Island on the inner continental shelf during a seafloor mapping study in the summer of 2014 and shipped select samples to the St. Petersburg Coastal and Marine Science Center. The micropaleontological subsamples analyzed for foraminifera at each site can be used to establish a foraminiferal baseline assemblage that takes into consideration the seasonal variability of the various species, regarding density and geographic extent, which are influenced by transient and stable environmental parameters. By understanding what parameters affect the various foraminiferal assemblages, researchers can delineate how alterations in salinity, temperature, or marsh-to-bay interactions, such as marsh erosion, might affect that assemblage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1060","usgsCitation":"Ellis, A.M., Shaw, J.E., Osterman, L.E., and Smith, C.G., 2017, Distribution of foraminifera in Chincoteague Bay and the marshes of Assateague Island and the adjacent vicinity, Maryland and Virginia: U.S. Geological Survey Data Series 1060, available at https://doi.org/10.3133/ds1060.","productDescription":"HTML Document; Data Downloads","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-084010","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":347970,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1060","text":"Report HTML","linkFileType":{"id":5,"text":"html"},"description":"DS 1060"},{"id":347969,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1060/coverthb.jpg"},{"id":347971,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ds1059","text":"Data Series 1059","linkHelpText":"- A seasonal and spatial comparison of metals, and stable carbon and nitrogen isotopes, in Chincoteague Bay and the marsh deposits of Assateague Island and the adjacent vicinity, Maryland and Virginia"},{"id":438143,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YCK857","text":"USGS data release","linkHelpText":"Benthic Foraminiferal Data from Surface Samples and Sedimentary Cores in the Grand Bay Estuary, Mississippi and Alabama"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Assateague Island, Chincoteague Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.42388916015625,\n 37.82931081282506\n ],\n [\n -75.0311279296875,\n 37.82931081282506\n ],\n [\n -75.0311279296875,\n 38.43422817624596\n ],\n [\n -75.42388916015625,\n 38.43422817624596\n ],\n [\n -75.42388916015625,\n 37.82931081282506\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
After Hurricane Sandy, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center conducted a seasonal collection of estuarine, marsh, and sandy overwash surface sediments from Chincoteague Bay, Tom’s Cove, and the surrounding Assateague Island and Delmarva Peninsula in March–April and October 2014. Surplus surface sediment was analyzed for metals, percent carbon and nitrogen, δ13C, and δ15N as part of a complementary U.S. Geological Survey Coastal and Marine Geology Program Sea-level and Storm Impacts on Estuarine Environments and Shorelines project study. The geochemical subsample analyzed for metals and stable isotopes at each site may be used for comparison with past data sets, to create a modern baseline of the natural distribution of the area, to understand seasonal variability as it relates to the health of the local environment, and to assess marsh-to-bay interactions. The use of metals, stable carbon, and stable nitrogen isotopes allows for a more cohesive snapshot of factors influencing the environment and could aid in tracking environmental change.
This report serves as an archive for chemical data derived from the surface sediment. Data are available for a seasonal comparison between the March–April 2014 and October 2014 sampling trips. Downloadable data are available as Microsoft Excel spreadsheets. These additional files include formal Federal Geographic Data Committee metadata (data downloads).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1059","usgsCitation":"Ellis, A.M., and Smith, C.G., 2017, A seasonal and spatial comparison of metals, and stable carbon and nitrogen isotopes, in Chincoteague Bay and the marsh deposits of Assateague Island and the adjacent vicinity, Maryland and Virginia: U.S. Geological Survey Data Series 1059, https://doi.org/10.3133/ds1059.","productDescription":"HMTL Document; Data Downloads","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-077432","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":347967,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ds1060","text":"Data Series 1060","linkHelpText":"- Distribution of foraminifera in Chincoteague Bay and the marshes of Assateague Island and the adjacent vicinity, Maryland and Virginia"},{"id":347964,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1059/","text":"Report HTML","linkFileType":{"id":5,"text":"html"},"description":"DS 1059"},{"id":347963,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1059/coverthb.jpg"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Assateague Island, Chincoteague Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.42388916015625,\n 37.82931081282506\n ],\n [\n -75.0311279296875,\n 37.82931081282506\n ],\n [\n -75.0311279296875,\n 38.43422817624596\n ],\n [\n -75.42388916015625,\n 38.43422817624596\n ],\n [\n -75.42388916015625,\n 37.82931081282506\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Director,
USGS Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road
Mail Stop 977
Menlo Park, CA 94025