This report presents electron microprobe data on glasses and selected crystalline phases from Kīlauea Iki lava lake and glasses from the 1959 summit eruption of Kīlauea Volcano, Hawaii. Some of these data have been published previously, but the complete set has not been published before. In addition, this report includes electron microprobe data for phases in melting experiments reported earlier, which form the basis for using many of the glass compositions reported here to estimate quenching temperatures of the samples. Finally, because of the latter application, this report includes all useful field determinations of temperature taken in Kīlauea Iki boreholes from 1967 to 1988. These field measurements have been merged with geothermometry based on glass and Fe-Ti oxide compositions to produce a comprehensive review of all available thermal information for Kīlauea Iki. Making these datasets available completes documentation of field and chemical information on Kīlauea Iki lava lake, supplementing six previous U.S. Geological Survey Open-File Reports listed in the References Cited.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201012","usgsCitation":"Helz, R.T., 2020, Major-element compositional data and thermal data for drill core from Kīlauea Iki lava lake, plus analyses of glasses from scoria of the 1959 summit eruption of Kīlauea Volcano, Hawaii (ver 1.1, December 2021): U.S. Geological Survey Open-File Report 2020–1012, 48 p., https://doi.org/10.3133/ofr20201012.","productDescription":"Report: v, 48 p.; Appendix 1-2","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-109981","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":374174,"rank":2,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1012/ofr20201012_appendix1.xlsx","text":"Appendix 1","size":"206 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Tables 1.1–1.13 as an Excel file"},{"id":374175,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1012/ofr20201012_appendix2.xlsx","text":"Appendix 2","size":"48.5 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Tables 2.1–2.4 as an Excel file"},{"id":374172,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1012/coverthb2.jpg"},{"id":374177,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1012/ofr20201021_appendix2_csv.zip","text":"Appendix 2","size":"5.50 KB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Tables 2.1–2.4 as CSV files in a zipped folder"},{"id":374205,"rank":6,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1012/ofr20201012.pdf","text":"Report","size":"3.00 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1012"},{"id":392665,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2020/1012/versionHist.txt","size":"691 B","linkFileType":{"id":2,"text":"txt"}},{"id":374176,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1012/ofr20201021_appendix1_csv.zip","text":"Appendix 1","size":"41.5 KB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Tables 1.1–1.13 as CSV files in a zipped folder"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.30410766601562,\n 19.38759093442151\n ],\n [\n -155.2306365966797,\n 19.38759093442151\n ],\n [\n -155.2306365966797,\n 19.44846418467642\n ],\n [\n -155.30410766601562,\n 19.44846418467642\n ],\n [\n -155.30410766601562,\n 19.38759093442151\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: April 23, 2020; Version 1.1: December 15, 2021","contact":"Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 21092
(Re)insurance companies rely on earthquake risk models to estimate the frequency and severity of their potential financial losses. To protect themselves, they sometimes use parametric risk transfer solutions, which are derivative-form agreements that provide compensation as a function of routine measurable earthquake characteristics. These mechanisms typically remain in force for one to three years and assume seismic conditions—and our estimates of them—remain unchanged during this period. However, seismic risk estimates evolve continuously due to changes in nearby seismicity, sudden ruptures, slower redistributions of stress, or improvements in our own understanding of these phenomena. As a consequence, the likelihood of some loss-causing events might decrease and make the protection superfluous (wasted money), or, more problematically, it might increase and render the protection insufficient (increased risk). This paper explores the construction of parametric earthquake risk transfer mechanisms that adapt efficiently (i.e., near real-time) to changes in seismicity throughout the lifetime of the transaction. The mechanism proposes the periodic adjustment of the payment conditions of the parametric agreement in harmony with the evolving probabilities of event occurrence. This, we hypothesize, may result in a more efficient allocation of premiums that reflects the changing nature of seismic risk. To build the proposed dynamic risk transfer mechanism, we first employ one of the earthquake models commonly used in the (re)insurance industry to assess the risk of a portfolio of assets. The modeling exercise yields the expected frequency distribution of loss, which a standard (re)insurance transaction would typically consider constant for the entire coverage period. Here, we use these results simply as a baseline for the initial time step of reference. Next, we construct a retrospective update loop, which consists of two parts: (1) we obtain the earthquake occurrence rate conditions at a previous time step taking into account the changes in seismicity observed in the interim period; and (2) we use the modeled losses and adjusted frequencies at the new time step to build a parametric risk transfer solution. This parametric solution remains in force until it is updated at the next iteration. We also track the effects on the efficiency of the risk transfer solution and its premium if these continuous updates were not implemented.
We apply the proposed mechanism to California and find that changes in seismicity can cause swings in the frequency of parametric payments (which is related to the premium paid for the cover) in average of 16% and up to 36% in any three-year period from 1986 to 2020. We also find that avoiding an update of the parametric solution on a yearly basis to match the new risk profile can decrease the efficiency of the cover (measured as the relative contribution to the average annual loss of the events covered) in the same time period by 13% on average and up to 35%.
","conferenceTitle":"17th World Conference on Earthquake Engineering, 17WCEE","conferenceDate":"September 13-18, 2020","conferenceLocation":"Sendai, Japan","language":"English","publisher":"Japan Association for Earthquake Engineering","usgsCitation":"Franco, G., Guidotti, R., Field, E., Milner, K., Lee, Y., and Stein, R.S., 2020, An exploration of parametric earthquake risk transfer solutions that dynamically adapt to seismicity changes, 17th World Conference on Earthquake Engineering, 17WCEE, Sendai, Japan, September 13-18, 2020, 12 p.","productDescription":"12 p.","ipdsId":"IP-117006","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":425797,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://wcee.nicee.org/wcee/seventeenth_conf_sendai_japan/","linkFileType":{"id":5,"text":"html"}},{"id":425798,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Franco, Guillermo","contributorId":194951,"corporation":false,"usgs":false,"family":"Franco","given":"Guillermo","email":"","affiliations":[],"preferred":false,"id":783436,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guidotti, R","contributorId":222891,"corporation":false,"usgs":false,"family":"Guidotti","given":"R","email":"","affiliations":[{"id":40620,"text":"Guy Carpenter","active":true,"usgs":false}],"preferred":false,"id":783437,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Field, Edward H. 0000-0001-8172-7882 field@usgs.gov","orcid":"https://orcid.org/0000-0001-8172-7882","contributorId":1165,"corporation":false,"usgs":true,"family":"Field","given":"Edward H.","email":"field@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":783435,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Milner, K.R.","contributorId":222892,"corporation":false,"usgs":false,"family":"Milner","given":"K.R.","email":"","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":783438,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lee, Y.J.","contributorId":222893,"corporation":false,"usgs":false,"family":"Lee","given":"Y.J.","affiliations":[{"id":40621,"text":"ImageCat Inc.","active":true,"usgs":false}],"preferred":false,"id":783439,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stein, R. S.","contributorId":222894,"corporation":false,"usgs":false,"family":"Stein","given":"R.","email":"","middleInitial":"S.","affiliations":[{"id":40622,"text":"Temblor","active":true,"usgs":false}],"preferred":false,"id":783440,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228603,"text":"70228603 - 2020 - Decision context as an essential component of population viability analysis","interactions":[],"lastModifiedDate":"2022-02-14T14:59:02.146641","indexId":"70228603","displayToPublicDate":"2021-09-30T08:41:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Decision context as an essential component of population viability analysis","docAbstract":"Population viability analysis (PVA) is a widely used tool that applies demographic data in simulation frameworks to assess extinction risk for species or populations. It is used in diverse conservation applications, including evaluating management effectiveness, relative risk of threats, and potential changes to protective status (Beissinger & McCullough, 2002), and can be a critical tool for making decisions with imperfect knowledge of the system state, often on limited timelines (Meine et al., 2006).
Chaudhary and Oli (2020) recently developed a framework to appraise the quality of PVAs based on the presence of essential background, model, and analysis components. They evaluated 160 published PVAs and reported a decline in the quality of PVAs over time (1990−2017). We agree PVA studies should report unambiguous descriptions of their essential components (Table 1 in Chaudhary and Oli) and explicitly state the model's biological and statistical assumptions. The need for increased transparency in PVAs is evident. Morrison et al. (2016) reported that only 50% of PVAs published in peer-reviewed and gray literature were both reproducible and repeatable. Further, in an examination of 67 studies that used matrix population models (widely used in PVAs), Kendall et al. (2019) reported that models frequently contained misspecification errors. Given the rapid advancement of simulation techniques, updated guidance for PVA construction is warranted.
However, we believe the essential PVA components identified by Chaudhary and Oli contain a critical omission: the decision context in which the PVA was created and its usefulness in that context. Quality and utility are not mutually exclusive; however, some models that do not meet idealized quality standards might still be valuable because they are useful and represent the best available science for a given decision context (hereafter, decision-support models). The definition of quality for decision-support models should be different than models developed for the purpose of learning (hereafter, heuristic models) and should incorporate how useful the model was, despite information gaps. We further argue that assessment questions should be used prospectively to guide modeling projects, rather than for retrospective comparison of model quality.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/cobi.13818","usgsCitation":"Lawson, A.J., Folt, B., Tucker, A.M., Erickson, F.T., and McGowan, C.P., 2020, Decision context as an essential component of population viability analysis: Conservation Biology, no. 5, p. 1683-1685, https://doi.org/10.1111/cobi.13818.","productDescription":"3 p.","startPage":"1683","endPage":"1685","ipdsId":"IP-118153","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":395881,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"issue":"5","noUsgsAuthors":false,"publicationDate":"2021-08-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Lawson, Abigail Jean 0000-0002-2799-8750","orcid":"https://orcid.org/0000-0002-2799-8750","contributorId":276319,"corporation":false,"usgs":true,"family":"Lawson","given":"Abigail","email":"","middleInitial":"Jean","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":834747,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Folt, Brian","contributorId":267702,"corporation":false,"usgs":false,"family":"Folt","given":"Brian","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":834748,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tucker, Anna Maureen 0000-0002-1473-2048 amtucker@usgs.gov","orcid":"https://orcid.org/0000-0002-1473-2048","contributorId":257906,"corporation":false,"usgs":true,"family":"Tucker","given":"Anna","email":"amtucker@usgs.gov","middleInitial":"Maureen","affiliations":[],"preferred":true,"id":834749,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Erickson, Francesca T.","contributorId":276320,"corporation":false,"usgs":false,"family":"Erickson","given":"Francesca","email":"","middleInitial":"T.","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":834750,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":167162,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor","email":"cmcgowan@usgs.gov","middleInitial":"P.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":834751,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70213131,"text":"70213131 - 2020 - Lampricide residues in sea lamprey larvae carcasses recovered after 3-trifluoromethyl-4- nitrophenol (TFM) or TFM/Bayluscide stream treatments","interactions":[],"lastModifiedDate":"2022-04-21T16:58:18.655836","indexId":"70213131","displayToPublicDate":"2021-09-01T11:51:06","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":7568,"text":"Project Completion Report","active":true,"publicationSubtype":{"id":3}},"title":"Lampricide residues in sea lamprey larvae carcasses recovered after 3-trifluoromethyl-4- nitrophenol (TFM) or TFM/Bayluscide stream treatments","docAbstract":"Lampricide concentrations in whole larval sea lamprey (Petromyzon marinus) carcasses collected after lampricide treatments were determined to support risk assessment for non-target organisms that may consume lampricide-laden carcasses. Dead larvae were collected by Sea Lamprey Control personnel following the Ford River (Delta County, Michigan) 4.1 mg·L-1 3-trifluoromethyl-4-nitrophenol (TFM) treatment, Sturgeon River (Baraga County, Michigan) 0.64 mg·L-1 /7.1 µg·L-1 TFM/niclosamide treatment, and two treatments on the Chippewa River (Isabella County, Michigan). The upper reach of the Chippewa River was treated with 3.1 mg·L-1 /33 µg·L-1 TFM/Bayluscide and the lower reach was treated with 4.1 mg·L-1 TFM. Carcasses were removed from each stream via scap nets immediately after treatment completion. To assess instream degradation, half of the collected carcasses from both Chippewa River treatments were placed in cages and returned to the river for 2 days before they were analyzed for lampricide residues. The estimated mean and standard error of the mean (SEM) TFM concentration in the fresh carcasses (n = 80) collected from all the TFM and TFM/Bayluscide treated rivers was 4.6 µg·g-1 (SEM =1.1 µg·g-1 ). The mean concentration of niclosamide (the active ingredient in Bayluscide) in the fresh carcasses (n = 40) from the two rivers treated with TFM/ Bayluscide was 0.49 µg·g-1 (SEM = 0.21 µg·g-1 ). The mean 2-day postmortem carcasses from the Chippewa River TFM/Bayluscide treatment contained 0.14 µg·g-1 (3%) of the TFM and 0.41 µg·g-1 (64%) of the niclosamide found in the fresh-carcass group (4.4 µg·g-1 TFM and 0.64 µg·g-1 niclosamide). The mean 2-day postmortem carcasses from the Chippewa River TFM treatment contained 0.72 µg·g-1 (12%) compared to the 6.1 µg·g-1 of TFM found in the fresh-carcass group.
","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Bernardy, J., and Schloesser, N., 2020, Lampricide residues in sea lamprey larvae carcasses recovered after 3-trifluoromethyl-4- nitrophenol (TFM) or TFM/Bayluscide stream treatments: Project Completion Report, 16 p.","productDescription":"16 p.","ipdsId":"IP-112331","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":399097,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":399096,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.glfc.org/"}],"country":"United States","state":"Michigan","otherGeospatial":"Chippewa river, Ford River, Harlow Creek, Sturgeon River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.6761474609375,\n 46.57774276255591\n ],\n [\n -88.3465576171875,\n 46.57774276255591\n ],\n [\n -88.3465576171875,\n 46.916503267244835\n ],\n [\n -88.6761474609375,\n 46.916503267244835\n ],\n [\n -88.6761474609375,\n 46.57774276255591\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.16312408447266,\n 45.66132705384569\n ],\n [\n -87.12089538574219,\n 45.66132705384569\n ],\n [\n -87.12089538574219,\n 45.69395042477016\n ],\n [\n -87.16312408447266,\n 45.69395042477016\n ],\n [\n -87.16312408447266,\n 45.66132705384569\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.220947265625,\n 43.51270490464819\n ],\n [\n -84.48486328124999,\n 43.51270490464819\n ],\n [\n -84.48486328124999,\n 43.96119063892024\n ],\n [\n -85.220947265625,\n 43.96119063892024\n ],\n [\n -85.220947265625,\n 43.51270490464819\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.48631954193115,\n 46.628663099851025\n ],\n [\n -87.4681234359741,\n 46.628663099851025\n ],\n [\n -87.4681234359741,\n 46.63880017254108\n ],\n [\n -87.48631954193115,\n 46.63880017254108\n ],\n [\n -87.48631954193115,\n 46.628663099851025\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bernardy, Jeffry 0000-0001-7443-1995","orcid":"https://orcid.org/0000-0001-7443-1995","contributorId":213528,"corporation":false,"usgs":true,"family":"Bernardy","given":"Jeffry","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":798336,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schloesser, Nicholas 0000-0002-3815-5302","orcid":"https://orcid.org/0000-0002-3815-5302","contributorId":237025,"corporation":false,"usgs":true,"family":"Schloesser","given":"Nicholas","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":798337,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70223674,"text":"70223674 - 2020 - Outside the box: Working with wildlife in biocontainment","interactions":[],"lastModifiedDate":"2022-01-25T16:48:58.603578","indexId":"70223674","displayToPublicDate":"2021-08-23T08:22:36","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5255,"text":"ILAR Journal","active":true,"publicationSubtype":{"id":10}},"title":"Outside the box: Working with wildlife in biocontainment","docAbstract":"Research with captive wildlife in Animal Biosafety Level 2 (ABSL2) and 3 (ABSL3) facilities is becoming increasingly necessary as emerging and re-emerging diseases involving wildlife have increasing impacts on human, animal, and environmental health. Utilizing wildlife species in a research facility often requires outside the box thinking with specialized knowledge, practices, facilities, and equipment. The USGS National Wildlife Health Center (NWHC) houses an ABSL3 facility dedicated to understanding wildlife diseases and developing tools to mitigate their impacts on animal and human health. This review presents considerations for utilizing captive wildlife for infectious disease studies, including, husbandry, animal welfare, veterinary care, and biosafety. Examples are drawn from primary literature review and collective 40-year experience of the NWHC. Working with wildlife in ABSL2 and ABSL3 facilities differs from laboratory animals in that typical laboratory housing systems, husbandry practices, and biosafety practices are not designed for work with wildlife. This requires thoughtful adaptation of standard equipment and practices, invention of customized solutions and development of appropriate enrichment plans using the natural history of the species and the microbiological characteristics of introduced and native pathogens. Ultimately, this task requires critical risk assessment, understanding of the physical and psychological needs of diverse species, creativity, innovation, and flexibility. Finally, continual reassessment and improvement are imperative in this constantly changing specialty area of infectious disease and environmental hazard research.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/ilar/ilab025","usgsCitation":"Falendysz, E., Calhoun, D.M., Smith, C.A., and Sleeman, J.M., 2020, Outside the box: Working with wildlife in biocontainment: ILAR Journal, v. 61, no. 1, p. 72-85, https://doi.org/10.1093/ilar/ilab025.","productDescription":"14 p.","startPage":"72","endPage":"85","ipdsId":"IP-123136","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":454587,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ilar/ilab025","text":"Publisher Index Page"},{"id":388725,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"61","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-08-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Falendysz, Elizabeth 0000-0003-2895-8918 efalendysz@usgs.gov","orcid":"https://orcid.org/0000-0003-2895-8918","contributorId":127751,"corporation":false,"usgs":true,"family":"Falendysz","given":"Elizabeth","email":"efalendysz@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":822284,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Calhoun, Dana Marie 0000-0002-9483-2064","orcid":"https://orcid.org/0000-0002-9483-2064","contributorId":245039,"corporation":false,"usgs":true,"family":"Calhoun","given":"Dana","email":"","middleInitial":"Marie","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":822285,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Carrie Alison 0000-0002-2684-3407","orcid":"https://orcid.org/0000-0002-2684-3407","contributorId":228816,"corporation":false,"usgs":true,"family":"Smith","given":"Carrie","email":"","middleInitial":"Alison","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":822286,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sleeman, Jonathan M. 0000-0002-9910-6125 jsleeman@usgs.gov","orcid":"https://orcid.org/0000-0002-9910-6125","contributorId":128,"corporation":false,"usgs":true,"family":"Sleeman","given":"Jonathan","email":"jsleeman@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":82110,"text":"Midcontinent Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":822287,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70221395,"text":"70221395 - 2020 - Asian carp population modeling to support an adaptive management framework","interactions":[],"lastModifiedDate":"2021-06-14T13:17:12.747593","indexId":"70221395","displayToPublicDate":"2021-06-01T08:15:31","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Asian carp population modeling to support an adaptive management framework","docAbstract":"This Monitoring and Response Plan provides the Asian Carp Regional Coordinating Committee (ACRCC) with updates on FWS and USGS modeling efforts for the Spatially Explicit Asian carp Population (SEAcarP) model. For FY2020, efforts are underway to parameterize and analyze the SEAcarP model. Themes: invasive species; Asian carp; Great Lakes.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Monitoring Response Plans, Asian Carp Regional Coordinating Committee","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Asian Carp Regional Coordinating Committee","collaboration":"U.S. Fish and Wildlife Service; ACRCC","usgsCitation":"Kallis, J.L., Erickson, R.A., and Fritts, M.W., 2020, Asian carp population modeling to support an adaptive management framework, 6 p.","productDescription":"6 p.","startPage":"95","endPage":"100","ipdsId":"IP-119007","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":386470,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":386461,"type":{"id":15,"text":"Index Page"},"url":"https://www.asiancarp.us/PlansReports.html"}],"country":"United States","state":"Illinois","otherGeospatial":"Illinois River Waterway system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.5830078125,\n 41.73852846935917\n ],\n [\n -87.62695312499999,\n 42.00032514831621\n ],\n [\n -87.8466796875,\n 42.00032514831621\n ],\n [\n -88.26416015625,\n 41.713930073371294\n ],\n [\n -88.857421875,\n 41.60722821271717\n ],\n [\n -89.439697265625,\n 41.44272637767212\n ],\n [\n -89.8681640625,\n 41.253032440653186\n ],\n [\n -90.32958984375,\n 40.64730356252251\n ],\n [\n -90.791015625,\n 39.93501296038254\n ],\n [\n -90.791015625,\n 39.257778150283364\n ],\n [\n -90.52734374999999,\n 38.659777730712534\n ],\n [\n -90.098876953125,\n 38.65119833229951\n ],\n [\n -90.087890625,\n 39.07037913108751\n ],\n [\n -90.4833984375,\n 39.45316112807394\n ],\n [\n -90.06591796875,\n 40.027614437486655\n ],\n [\n -89.088134765625,\n 40.94671366508002\n ],\n [\n -88.2861328125,\n 41.29431726315258\n ],\n [\n -87.5830078125,\n 41.73852846935917\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kallis, Jahn L.","contributorId":205603,"corporation":false,"usgs":false,"family":"Kallis","given":"Jahn","email":"","middleInitial":"L.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":817507,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":817508,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fritts, Mark W.","contributorId":139239,"corporation":false,"usgs":false,"family":"Fritts","given":"Mark","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":817509,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70221394,"text":"70221394 - 2020 - USGS Illinois River monitoring and evaluation","interactions":[],"lastModifiedDate":"2021-11-01T19:06:35.968534","indexId":"70221394","displayToPublicDate":"2021-06-01T08:08:15","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"displayTitle":"USGS Illinois River Monitoring and Evaluation","title":"USGS Illinois River monitoring and evaluation","docAbstract":"Asian carp monitoring and contract removal will continue throughout the Upper Illinois Waterway system as needed for adaptive management to mitigate, control, and contain Asian carp. Compiling data from monitoring and removal efforts into a centralized database (Illinois River Catch Database application) facilitates data standardization, quality, accessibility, sharing, and analysis to aid in Asian carp removal efforts, evaluations of management actions, and modeling efforts (e.g., SEACarP model). Data summarization, visualization, and modeling supports a better understanding of bigheaded carp life history, behavior, and habitat use. Integrating Asian carp-related data and analyses into decision support tools and products aids in applying control and containment methods in an informed and transparent manner (e.g., improved efficiencies in implementations of the Unified Method, inform targeted removal efforts or deterrent deployments in key locations based on preferential benthic characteristics and environmental conditions).","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"2020 Asian Carp Monitoring and Response Plan","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Invasive Species Regional Coordinating Committee","usgsCitation":"Harrison, T.J., Hop, K.D., Hlavacek, E., and Knights, B.C., 2020, USGS Illinois River monitoring and evaluation, 4 p.","productDescription":"4 p.","startPage":"87","endPage":"90","ipdsId":"IP-119472","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":386469,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":391210,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://invasivecarp.us/Documents/Monitoring-Response-Plan-2020.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Illinois","otherGeospatial":"Illinois River Waterway system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.5830078125,\n 41.73852846935917\n ],\n [\n -87.62695312499999,\n 42.00032514831621\n ],\n [\n -87.8466796875,\n 42.00032514831621\n ],\n [\n -88.26416015625,\n 41.713930073371294\n ],\n [\n -88.857421875,\n 41.60722821271717\n ],\n [\n -89.439697265625,\n 41.44272637767212\n ],\n [\n -89.8681640625,\n 41.253032440653186\n ],\n [\n -90.32958984375,\n 40.64730356252251\n ],\n [\n -90.791015625,\n 39.93501296038254\n ],\n [\n -90.791015625,\n 39.257778150283364\n ],\n [\n -90.52734374999999,\n 38.659777730712534\n ],\n [\n -90.098876953125,\n 38.65119833229951\n ],\n [\n -90.087890625,\n 39.07037913108751\n ],\n [\n -90.4833984375,\n 39.45316112807394\n ],\n [\n -90.06591796875,\n 40.027614437486655\n ],\n [\n -89.088134765625,\n 40.94671366508002\n ],\n [\n -88.2861328125,\n 41.29431726315258\n ],\n [\n -87.5830078125,\n 41.73852846935917\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Harrison, Travis J. 0000-0002-9195-738X","orcid":"https://orcid.org/0000-0002-9195-738X","contributorId":213966,"corporation":false,"usgs":true,"family":"Harrison","given":"Travis","email":"","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":817503,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":817504,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":817505,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knights, Brent C. 0000-0001-8526-8468 bknights@usgs.gov","orcid":"https://orcid.org/0000-0001-8526-8468","contributorId":2906,"corporation":false,"usgs":true,"family":"Knights","given":"Brent","email":"bknights@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":817506,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210044,"text":"ofr20201042 - 2020 - Systems-deposits-commodities-critical minerals table for the earth mapping resources initiative","interactions":[],"lastModifiedDate":"2021-05-28T19:26:40.9176","indexId":"ofr20201042","displayToPublicDate":"2021-05-28T11:40:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1042","displayTitle":"Systems-Deposits-Commodities-Critical Minerals Table for the Earth Mapping Resources Initiative","title":"Systems-deposits-commodities-critical minerals table for the earth mapping resources initiative","docAbstract":"To define and prioritize focus areas across the United States with resource potential for 35 critical minerals in a few years’ time, the U.S Geological Survey Earth Mapping Resources Initiative (Earth MRI) required an efficient approach to streamline workflow. A mineral systems approach based on current understanding of how ore deposits that contain critical minerals form and relate to broader geologic frameworks and the tectonic history of the Earth was used to satisfy this Earth MRI need. This report describes the rationale for, and structure of, a table developed for Earth MRI that relates critical minerals and principal commodities to the deposit types and mineral systems in which they are concentrated. The hierarchical relationship between systems, deposits, commodities, and critical minerals makes it possible to define and prioritize each system-based focus area once for all of the critical minerals that it may contain. This approach is advantageous because mineral systems are much larger than individual ore deposits and they generally have geologic features that can be “imaged” by the topographic, geologic, geochemical, and geophysical mapping techniques deployed by Earth MRI.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201042","usgsCitation":"Hofstra, A.H., and Kreiner, D.C., 2020, Systems-Deposits-Commodities-Critical Minerals Table for the Earth Mapping Resources Initiative (ver. 1.1, May 2021): U.S. Geological Survey Open-File Report 2020–1042, 26 p., \nhttps://doi.org/10.3133/ofr20201042.","productDescription":"Report: vii, 24 p.; Table","onlineOnly":"Y","ipdsId":"IP-115500","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":374652,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1042/coverthb2.jpg"},{"id":374653,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1042/ofr20201042.pdf","text":"Report","size":"3.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1042"},{"id":374654,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1042/ofr20201042_table1.pdf","text":"Table 1. Systems-Deposits-Commodities-Critical Minerals Table for the Earth Mapping Resources Initiative","size":"196 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1042 Table 1"},{"id":385684,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2020/1042/versionHist.txt","size":"4.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2020-1042 version history"}],"edition":"Version 1.0: May 28, 2020; Version 1.1: May 19, 2021","contact":"Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
MS 973, Box 25046
Denver, CO 80225
Anadromous Pacific salmon Oncorhynchus spp. are semelparous, and resource subsidies from spawning adult salmon (marine-derived nutrients [MDN]) benefit juvenile salmonids while they rear in freshwater. However, it is unclear if juvenile salmon populations respond predictably to the abundance of spawning salmon at the watershed scale. To address whether hypothesized benefits to rearing juveniles scale up to population and watershed scales, we examined juvenile Coho Salmon Oncorhynchus kisutch and Chinook Salmon O. tshawytscha growth, fork length, condition, and abundance as a function of MDN assimilation throughout the Unalakleet and North rivers in western Alaska. Additionally, a mark–recapture experiment provided abundance estimates of Coho Salmon smolts emigrating from these two rivers. Prior to spawning, residual MDN from past years offered little advantage to juvenile salmon. However, after the arrival of spawning adults, juveniles demonstrated a positive relationship between MDN and fish size, growth, and condition in fall and winter. Out-migrating smolts also benefitted from MDN resources via increased size and growth rates. Coho Salmon smolt abundance was unrelated to total spawner biomass, but a positive relationship between MDN assimilation and smolt abundance suggested a possible effect on overwinter survival. Furthermore, similar trends in spawner biomass and the abundance of age-1 smolts suggested that age at smolting was influenced by MDN. These relationships support the hypothesis that salmon spawner abundance during Coho and Chinook Salmon rearing is an important factor in the juvenile productivity of these species.
","language":"English","publisher":"Wiley","doi":"10.1002/tafs.10233","usgsCitation":"Joy, P.J., Stricker, C.A., Ivanoff, R., Wang, S.Y., Wipfli, M.S., Seitz, A., Huang, J., and Tyers, M.B., 2020, Juvenile Coho and Chinook salmon growth, size, and condition linked to watershed-scale salmon spawner abundance: Transactions of the American Fisheries Society, v. 150, no. 3, p. 307-326, https://doi.org/10.1002/tafs.10233.","productDescription":"20 p.","startPage":"307","endPage":"326","ipdsId":"IP-109480","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":395641,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Chirosky River, North River, Unalakleet River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -160.8185577392578,\n 63.82825415987884\n ],\n [\n -160.5370330810547,\n 63.82825415987884\n ],\n [\n -160.5370330810547,\n 63.91352961251089\n ],\n [\n -160.8185577392578,\n 63.91352961251089\n ],\n [\n -160.8185577392578,\n 63.82825415987884\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"150","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Joy, Philip J.","contributorId":274930,"corporation":false,"usgs":false,"family":"Joy","given":"Philip","email":"","middleInitial":"J.","affiliations":[{"id":56688,"text":"adfg","active":true,"usgs":false}],"preferred":false,"id":833517,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stricker, Craig A. 0000-0002-5031-9437 cstricker@usgs.gov","orcid":"https://orcid.org/0000-0002-5031-9437","contributorId":1097,"corporation":false,"usgs":true,"family":"Stricker","given":"Craig","email":"cstricker@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":833516,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ivanoff, Renae","contributorId":274931,"corporation":false,"usgs":false,"family":"Ivanoff","given":"Renae","email":"","affiliations":[{"id":56689,"text":"nsedc","active":true,"usgs":false}],"preferred":false,"id":833518,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Shiao Y.","contributorId":274932,"corporation":false,"usgs":false,"family":"Wang","given":"Shiao","email":"","middleInitial":"Y.","affiliations":[{"id":56690,"text":"usm","active":true,"usgs":false}],"preferred":false,"id":833519,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wipfli, Mark S. 0000-0002-4856-6068 mwipfli@usgs.gov","orcid":"https://orcid.org/0000-0002-4856-6068","contributorId":1425,"corporation":false,"usgs":true,"family":"Wipfli","given":"Mark","email":"mwipfli@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":833515,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Seitz, Andrew C.","contributorId":274933,"corporation":false,"usgs":false,"family":"Seitz","given":"Andrew C.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":833520,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huang, Jiaqi","contributorId":274934,"corporation":false,"usgs":false,"family":"Huang","given":"Jiaqi","email":"","affiliations":[{"id":56688,"text":"adfg","active":true,"usgs":false}],"preferred":false,"id":833521,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tyers, Mathew B.","contributorId":274935,"corporation":false,"usgs":false,"family":"Tyers","given":"Mathew","email":"","middleInitial":"B.","affiliations":[{"id":56688,"text":"adfg","active":true,"usgs":false}],"preferred":false,"id":833522,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70216163,"text":"sir20205090 - 2020 - Analysis of remedial scenarios affecting plume movement through a sole-source aquifer system, southeastern Nassau County, New York","interactions":[],"lastModifiedDate":"2021-04-27T17:33:12.761031","indexId":"sir20205090","displayToPublicDate":"2021-04-27T13:40:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5090","displayTitle":"Analysis of Remedial Scenarios Affecting Plume Movement Through a Sole-Source Aquifer System, Southeastern Nassau County, New York","title":"Analysis of remedial scenarios affecting plume movement through a sole-source aquifer system, southeastern Nassau County, New York","docAbstract":"A steady-state three-dimensional groundwater-flow model based on present conditions is coupled with the particle-tracking program MODPATH to assess the fate and transport of volatile organic-compound plumes within the Magothy and upper glacial aquifers in southeastern Nassau County, New York. Particles are forward tracked from locations within plumes defined by surfaces of equal concentration. Particles move toward ultimate well capture and discharge to the general head and drain boundaries representing natural receptors in the models. Because rates of advection within coarse-grained sediments typically exceed 0.1 foot per day, mechanisms of dispersion and diffusion were assumed to be negligible. Resulting particle pathlines are influenced by hydrogeologic framework features and the interplay of nearby hydrologic stresses. Simulated hydrologic effects include cones of depression near pumping wells and water-table mounding near points of treated water recharge; however, remedial pumping amounts are balanced by treated-water return, and net effects at distant regional boundaries, including freshwater/saltwater interfaces, are minor.
Once a steady-state model was developed and calibrated, eight hypothetical remedial scenarios were evaluated to hydraulically contain the volatile organic-compound plumes. Specifically, the remedial scenarios were optimized to achieve full containment by altering the pumping-well locations, adjusting the pumping rates, and adjusting the discharge locations and rates. Based on the results, total hypothetical extraction rates varied from about 5,462 gallons per minute during an anticipated near-future condition to about 13,340 gallons per minute during full hydraulic containment of all site-related compounds identified by the New York State standards, criteria, and guidance for environmental investigations and cleanup. Targeting of high-concentration zones of the plume increases the total amount of remedial pumpage necessary to capture all parts of the plume but may decrease the total amount of time necessary to operate a remedial system. Simulated time frames of advective transport ranged from about 12 years to capture zones with elevated concentrations of volatile organic compounds (mean particle travel time plus the standard deviation of travel time) to more than 100 years to capture all zones.
Groundwater-flow model analysis indicates that all the optimal plume-containment scenarios would have negligible effects on streams and the saltwater-freshwater interface along the south shore of Long Island. Massapequa, Bellmore, Seaman, and Seaford Creeks are represented by using MODFLOW drain-boundary conditions. Saltwater-freshwater interfaces are represented by using MODFLOW general head-boundary conditions where the Magothy aquifer discharges upward into saline groundwater across the Gardiners clay confining unit and the Lloyd aquifer discharges upward into saline groundwater across the Raritan confining unit.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205090","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Misut, P.E., Walter, D., Schubert, C., and Dressler, S., 2020, Analysis of remedial scenarios affecting plume movement through a sole-source aquifer system, southeastern Nassau County, New York: U.S. Geological Survey Scientific Investigations Report 2020–5090, 83 p., https://doi.org/10.3133/sir20205090.","productDescription":"Report: vi, 83 p.; Data Release; 5 Figures","numberOfPages":"83","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-105143","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":380266,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5090/sir20205090_figures.zip","text":"High-resolution figures","size":"159 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Figures 16, 18, 20, 22, and 24"},{"id":380264,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DOBQ8N","text":"USGS data release","linkHelpText":"MODFLOW–NWT and MODPATH6 model use to analyze remedial scenarios affecting plume movement through a sole-source aquifer system, southeastern Nassau County, New York"},{"id":380262,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5090/coverthb.jpg"},{"id":380263,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5090/sir20205090.pdf","text":"Report","size":"18.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5090"}],"country":"United States","state":"New York","county":"Nassau County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.87619018554688,\n 40.482470524589516\n ],\n [\n -73.289794921875,\n 40.482470524589516\n ],\n [\n -73.289794921875,\n 40.81796653313175\n ],\n [\n -73.87619018554688,\n 40.81796653313175\n ],\n [\n -73.87619018554688,\n 40.482470524589516\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
We applied compound-specific sulfur isotope analysis (CSSIA) to organic matter (OM) extracted from ancient and immature organic-rich rocks from the Cretaceous Ghareb (Shefela Basin locality, Israel) and Miocene Monterey (Naples Beach locality, California, USA) Formations. Large variations in the δ34S values of different organosulfur compounds (OSCs), that reach up to 28‰ and 36‰, were observed in the Ghareb and Monterey samples, respectively. Additionally, some common OSCs in both locations showed consistent 34S trends relative to each other. The consistent enrichment in 34S of C35 hopane thiophene relative to iC20 thiophene in the studied sections probably resulted from differences in the timing of OM sulfurization. Reactive organic precursors quickly consume the most 34S-depleted reduced S, while less reactive species incorporate the heavier residual S at a later time. Despite the differences in the depositional environments, ages, and the initial δ34S values of the reduced S (represented by the δ34S of pyrite) between the Ghareb and the Monterey Formations, the sulfurization order of common organic compounds seems to be similar. All of the δ34S values of OSCs are 34S enriched relative to that of the coexisting pyrite with the exception of the C25 highly branched isoprenoid (HBI) thiophene in several samples from the Monterey Formation. The existence of 34S-depleted sulfurized HBI may point to OM sulfurization that occurred at or near the sediment-water interface during the deposition of the Monterey. Moreover, the δ34S of steroid sulfides shows an inverse trend with the pristane/phytane ratio, which may indicate that the sulfurization mechanism of these OSCs are affected by redox conditions. Further investigation of CSSI values in immature rocks from other basins may help constrain the OM sulfurization process, timescale, and depositional conditions and their possible use as paleoenvironmental proxies.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2020.01.034","usgsCitation":"Shawar, L., Said-Ahmad, W., Ellis, G.S., and Amrani, A., 2020, Sulfur isotope composition of individual compounds in immature organic-rich rocks and possible geochemical implications: Geochimica et Cosmochimica Acta, v. 274, p. 20-44, https://doi.org/10.1016/j.gca.2020.01.034.","productDescription":"25 p.","startPage":"20","endPage":"44","ipdsId":"IP-111549","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":385460,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Santa Barbara, Oxnard","otherGeospatial":"Naples beach","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.14648437499999,\n 34.14363482031264\n ],\n [\n -119.036865234375,\n 34.14363482031264\n ],\n [\n -119.036865234375,\n 34.67839374011646\n ],\n [\n -120.14648437499999,\n 34.67839374011646\n ],\n [\n -120.14648437499999,\n 34.14363482031264\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"274","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shawar, Lubna","contributorId":177555,"corporation":false,"usgs":false,"family":"Shawar","given":"Lubna","email":"","affiliations":[],"preferred":false,"id":815173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Said-Ahmad, Ward","contributorId":257863,"corporation":false,"usgs":false,"family":"Said-Ahmad","given":"Ward","affiliations":[{"id":52141,"text":"Hebrew University of Jerusalem","active":true,"usgs":false}],"preferred":false,"id":815174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ellis, Geoffrey S. 0000-0003-4519-3320 gsellis@usgs.gov","orcid":"https://orcid.org/0000-0003-4519-3320","contributorId":1058,"corporation":false,"usgs":true,"family":"Ellis","given":"Geoffrey","email":"gsellis@usgs.gov","middleInitial":"S.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":815175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Amrani, Alon","contributorId":225213,"corporation":false,"usgs":false,"family":"Amrani","given":"Alon","affiliations":[{"id":41077,"text":"Research Center","active":true,"usgs":false}],"preferred":false,"id":815176,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70218269,"text":"70218269 - 2020 - Debris-flow growth in Puerto Rico during Hurricane Maria: Preliminary results from analyses of pre- and post-event lidar data","interactions":[],"lastModifiedDate":"2021-04-20T11:52:56.654517","indexId":"70218269","displayToPublicDate":"2021-02-28T09:19:43","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Debris-flow growth in Puerto Rico during Hurricane Maria: Preliminary results from analyses of pre- and post-event lidar data","docAbstract":"On September 20, 2017, Hurricane Maria triggered widespread debris flows in Puerto Rico. We used field observations and pre- and post-Maria lidar to study the volumetric growth of long-travelled (>400 m) debris flows in four basins. We found overall growth rates that ranged from 0.7 to 30.4 m3 per meter of channel length. We partitioned the rates into two growth mechanisms, aggregation of multiple landslides, or erosion and entrainment of channel sediment. In three basins, landslides accounted for more than 80% of the total debris-flow volumes. In one basin, entrainment accounted for 96% of the volume. These results indicate that forecasting volumes for regional debris-flow inundation modeling is more complicated than estimating the number and volume of contributing landslide source areas, although this task is difficult by itself. In this preliminary analysis, we did not find geologic, topographic, or morphometric variables that correlated with the growth observations. We suspect that the observed growth rates were heavily influenced by local variations in environmental conditions, including antecedent soil moisture conditions, duration and intensity of rainfall, and availability of channel material. Given these considerations, regional debris-flow inundation modeling may be best achieved by using a suite of scenarios that capture possible mechanisms of debris-flow growth.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 13th International Symposium on Landslides","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"13th International Symposium on Landslides","conferenceDate":"February 22-26, 2021","language":"English","publisher":"International Society for Soil Mechanics and Geotechnical Engineering","usgsCitation":"Coe, J.A., Bessette-Kirton, E., Brien, D.L., and Reid, M.E., 2020, Debris-flow growth in Puerto Rico during Hurricane Maria: Preliminary results from analyses of pre- and post-event lidar data, in Proceedings of the 13th International Symposium on Landslides, February 22-26, 2021, 9 p.","productDescription":"9 p.","ipdsId":"IP-113885","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":385190,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":383574,"type":{"id":15,"text":"Index Page"},"url":"https://www.issmge.org/uploads/publications/105/106/ISL2020-7.pdf"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.30499267578125,\n 17.85590441431915\n ],\n [\n -65.5828857421875,\n 17.85590441431915\n ],\n [\n -65.5828857421875,\n 18.578568865536027\n ],\n [\n -67.30499267578125,\n 18.578568865536027\n ],\n [\n -67.30499267578125,\n 17.85590441431915\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Coe, Jeffrey A. 0000-0002-0842-9608 jcoe@usgs.gov","orcid":"https://orcid.org/0000-0002-0842-9608","contributorId":1333,"corporation":false,"usgs":true,"family":"Coe","given":"Jeffrey","email":"jcoe@usgs.gov","middleInitial":"A.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":810789,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bessette-Kirton, Erin K. 0000-0002-2797-0694","orcid":"https://orcid.org/0000-0002-2797-0694","contributorId":225097,"corporation":false,"usgs":false,"family":"Bessette-Kirton","given":"Erin K.","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":810790,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brien, Dianne L. 0000-0003-3227-7963 dbrien@usgs.gov","orcid":"https://orcid.org/0000-0003-3227-7963","contributorId":229851,"corporation":false,"usgs":true,"family":"Brien","given":"Dianne","email":"dbrien@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":810791,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reid, Mark E. 0000-0002-5595-1503 mreid@usgs.gov","orcid":"https://orcid.org/0000-0002-5595-1503","contributorId":1167,"corporation":false,"usgs":true,"family":"Reid","given":"Mark","email":"mreid@usgs.gov","middleInitial":"E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":810792,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70218314,"text":"70218314 - 2020 - Geothermal play fairway analysis of the Sou Hills, northern Nevada: A major quaternary accommodation zone in the Great Basin region","interactions":[],"lastModifiedDate":"2021-04-19T14:04:45.076424","indexId":"70218314","displayToPublicDate":"2021-02-24T07:45:11","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Geothermal play fairway analysis of the Sou Hills, northern Nevada: A major quaternary accommodation zone in the Great Basin region","docAbstract":"No abstract available.
","language":"English","publisher":"Bureau of Ocean Energy Management","collaboration":"Clemson University, BOEM","usgsCitation":"Lamb, J., Satge, Y., Streker, R., and Jodice, P.G., 2020, Ecological drivers of brown pelican movement patterns, health, and reproductive success in the Gulf of Mexico, xii, 232 p.","productDescription":"xii, 232 p.","ipdsId":"IP-113331","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":396234,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396233,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://espis.boem.gov/final%20reports/BOEM_2020-036.pdf"}],"country":"Mexico, United States","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.337890625,\n 16.88865978738161\n ],\n [\n -78.92578124999999,\n 16.88865978738161\n ],\n [\n -78.92578124999999,\n 32.69486597787505\n ],\n [\n -101.337890625,\n 32.69486597787505\n ],\n [\n -101.337890625,\n 16.88865978738161\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lamb, J.S.","contributorId":279814,"corporation":false,"usgs":false,"family":"Lamb","given":"J.S.","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":835496,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Satge, Y.G.","contributorId":279816,"corporation":false,"usgs":false,"family":"Satge","given":"Y.G.","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":835497,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Streker, R.A.","contributorId":279819,"corporation":false,"usgs":false,"family":"Streker","given":"R.A.","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":835498,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":835499,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70218222,"text":"70218222 - 2020 - Testing hypotheses on signatures of precipitation variability in the river and floodplain deposits of the Paleogene San Juan Basin, New Mexico, USA","interactions":[],"lastModifiedDate":"2021-02-22T12:54:08.541165","indexId":"70218222","displayToPublicDate":"2021-02-18T13:16:19","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2451,"text":"Journal of Sedimentary Research","onlineIssn":"1938-3681","printIssn":"1527-1404","active":true,"publicationSubtype":{"id":10}},"title":"Testing hypotheses on signatures of precipitation variability in the river and floodplain deposits of the Paleogene San Juan Basin, New Mexico, USA","docAbstract":"Much progress has been made in recent years towards a set of recognition criteria for river discharge variability in river channel deposits, and thus sedimentary proxies for precipitation variability. Despite this progress, there is currently no consensus on how different styles of discharge variability are reflected in river sedimentary records, and whether variable-discharge river records from different climate types can be distinguished. Herein, river discharge and precipitation variability in the Paleogene is investigated using associations between river channel and floodplain deposits across the Paleocene-Eocene boundary from the Paleocene upper Nacimiento Formation and the early Eocene San Jose Formation in the San Juan Basin, New Mexico, USA. \n\nThe succession is identified as deposits of variable-discharge river systems based on shared channel-deposit characteristics with modern and ancient variable-discharge river systems and the proposed facies models, in addition to alternations of poorly drained and well-drained floodplain deposits and/or slickensides indicating alternating wet-dry cycles. A long-term stratigraphic trend toward increasingly well-drained floodplain deposits is also observed and hypothesized to indicate successively more arid conditions from the Paleocene into the early Eocene. Comparisons with modern rivers from various climate zones suggest a long-term shift from a monsoonal climate in the Paleocene, to a fluctuating subhumid climate, ultimately leading to semiarid to arid conditions in the early Eocene. These observations suggest that floodplain deposits may be a better indicator of ambient climate, whereas channel deposits are records for frequency and magnitude of high-intensity precipitation events. Therefore, the existing facies models for variable-discharge rivers that consider only channel facies may not capture critical information needed to make accurate interpretations of paleoclimatic conditions. This study also adds to a growing body of evidence from geologic records of mid-latitude Paleogene river systems suggesting increases in the magnitude or variability of river discharge coinciding with established climate perturbations.","language":"English","publisher":"SEPM (Society for Sedimentary Geology)","doi":"10.2110/jsr.2020.75","usgsCitation":"Zellman, K.L., Plink-Bjorklund, P., and Fricke, H., 2020, Testing hypotheses on signatures of precipitation variability in the river and floodplain deposits of the Paleogene San Juan Basin, New Mexico, USA: Journal of Sedimentary Research, v. 90, no. 12, p. 1770-1801, https://doi.org/10.2110/jsr.2020.75.","productDescription":"32 p.","startPage":"1770","endPage":"1801","ipdsId":"IP-107514","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":383385,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"San Juan basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.907470703125,\n 36.23762751669998\n ],\n [\n -107.82806396484375,\n 36.23762751669998\n ],\n [\n -107.82806396484375,\n 37.00035919622158\n ],\n [\n -108.907470703125,\n 37.00035919622158\n ],\n [\n -108.907470703125,\n 36.23762751669998\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"90","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-02-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Zellman, Kristine L. 0000-0002-7088-429X kzellman@usgs.gov","orcid":"https://orcid.org/0000-0002-7088-429X","contributorId":4849,"corporation":false,"usgs":true,"family":"Zellman","given":"Kristine","email":"kzellman@usgs.gov","middleInitial":"L.","affiliations":[],"preferred":true,"id":810474,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Plink-Bjorklund, Piret","contributorId":251748,"corporation":false,"usgs":false,"family":"Plink-Bjorklund","given":"Piret","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":810475,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fricke, Henry","contributorId":251749,"corporation":false,"usgs":false,"family":"Fricke","given":"Henry","email":"","affiliations":[{"id":37163,"text":"Colorado College","active":true,"usgs":false}],"preferred":false,"id":810476,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229515,"text":"70229515 - 2020 - Landsat surface reflectance validation site selection","interactions":[],"lastModifiedDate":"2022-03-11T15:35:55.452537","indexId":"70229515","displayToPublicDate":"2021-02-17T09:31:32","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Landsat surface reflectance validation site selection","docAbstract":"An investigation was conducted to determine optimal locations within the continental United States for insitu measurements to validate the U.S. Landsat Analysis Ready Data (ARD) Surface Reflectance product. Site assessment involved analysis of aerosol optical depth, precipitable water vapor, land cover, cloud cover, and elevation models. Nineteen sites were selected for further month-by-month ranking to identify those sites most likely to capture simple or complex atmosphere.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"IGARSS 2020 - 2020 IEEE international geoscience and remote sensing symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"IEEE","doi":"10.1109/IGARSS39084.2020.9323374","usgsCitation":"Maddox, E.M., and Zavesky, L.D., 2020, Landsat surface reflectance validation site selection, in IGARSS 2020 - 2020 IEEE international geoscience and remote sensing symposium, p. 6133-6136, https://doi.org/10.1109/IGARSS39084.2020.9323374.","productDescription":"4 p.","startPage":"6133","endPage":"6136","ipdsId":"IP-115451","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":397023,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Maddox, Emily M. 0000-0001-5649-1193","orcid":"https://orcid.org/0000-0001-5649-1193","contributorId":288315,"corporation":false,"usgs":true,"family":"Maddox","given":"Emily","email":"","middleInitial":"M.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":837721,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zavesky, Landon Douglas 0000-0003-1109-8149","orcid":"https://orcid.org/0000-0003-1109-8149","contributorId":288316,"corporation":false,"usgs":true,"family":"Zavesky","given":"Landon","email":"","middleInitial":"Douglas","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":837722,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70216974,"text":"ofr20201148 - 2020 - 2020 drought in New England","interactions":[],"lastModifiedDate":"2021-02-11T19:15:14.115573","indexId":"ofr20201148","displayToPublicDate":"2021-02-11T13:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1148","displayTitle":"2020 Drought in New England","title":"2020 drought in New England","docAbstract":"Below average and infrequent rainfall from May through September 2020 led to an extreme hydrologic drought across much of New England, with some areas experiencing a flash drought, reflecting its quick onset. The U.S. Geological Survey (USGS) recorded record-low streamflow and groundwater levels throughout the region. In September, the U.S. Department of Agriculture (2020) declared Aroostook County in Maine and Hillsborough and Merrimack Counties in New Hampshire as crop disaster areas. By the beginning of October, 166 community water systems and 5 municipalities in New Hampshire, more than 100 municipalities in Massachusetts, and several community water supplies in Connecticut, Maine, and Rhode Island had mandatory water restrictions in place.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201148","usgsCitation":"Lombard, P.J., Barclay, J.R., and McCarthy, D.E., 2020, 2020 drought in New England (ver. 1.1, February 2021): U.S. Geological Survey Open-File Report 2020–1148, 12 p., https://doi.org/10.3133/ofr20201148.","productDescription":"Report: 12 p.; 3 Figures; 2 Tables","numberOfPages":"12","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-124096","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":383216,"rank":9,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1148/ofr20201148.pdf","text":"Report","size":"21.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1148"},{"id":383215,"rank":8,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2020/1148/ofr20201148_versionHist.txt","size":"2.33 KB","linkFileType":{"id":2,"text":"txt"}},{"id":381557,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2020/1148/ofr20201148_fig06.pdf","text":"Figure 6, full size","size":"270 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Groundwater levels"},{"id":381556,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2020/1148/ofr20201148_fig05.pdf","text":"Figure 5, full size","size":"71.9 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- 30- and 25-year percentiles"},{"id":381555,"rank":2,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2020/1148/ofr20201148_fig04.pdf","text":"Figure 4, full size","size":"573 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Moving average 7-day flows"},{"id":381653,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"National Water Information System"},{"id":381559,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1148/ofr20201148_table1.2.csv","text":"Table 1.2","size":"10.2 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Groundwater observation wells"},{"id":381544,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1148/coverthb4.jpg"},{"id":381558,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1148/ofr20201148_table1.1.csv","text":"Table 1.1","size":"7.53 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Streamgages"}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont","otherGeospatial":"New England","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.63037109375,\n 40.9964840143779\n ],\n [\n -72.94921875,\n 41.19518982948959\n ],\n [\n -71.3836669921875,\n 41.31494988250965\n ],\n [\n -70.63110351562499,\n 41.20758898181025\n ],\n [\n -69.8291015625,\n 41.17451935556443\n ],\n [\n -69.9884033203125,\n 42.147114459220994\n ],\n [\n -70.57617187499999,\n 42.70262285884388\n ],\n [\n -70.4168701171875,\n 43.20917969039356\n ],\n [\n -69.1314697265625,\n 43.957236472025635\n ],\n [\n -68.4942626953125,\n 43.98491011404692\n ],\n [\n -68.1756591796875,\n 44.13885576756881\n ],\n [\n -66.9232177734375,\n 44.75453548416007\n ],\n [\n -66.9012451171875,\n 44.80522439622254\n ],\n [\n -67.2308349609375,\n 45.1936513315257\n ],\n [\n -67.34619140625,\n 45.154927133618465\n ],\n [\n -67.47802734375,\n 45.27488643704891\n ],\n [\n -67.5,\n 45.4986468234261\n ],\n [\n -67.43408203124999,\n 45.606352077118316\n ],\n [\n -67.8350830078125,\n 45.69083283645816\n ],\n [\n -67.774658203125,\n 45.9511496866914\n ],\n [\n -67.7911376953125,\n 47.07386310181414\n ],\n [\n -68.2855224609375,\n 47.364873807434094\n ],\n [\n -68.40087890624999,\n 47.29413372501023\n ],\n [\n -68.92822265625,\n 47.19344533938292\n ],\n [\n -69.027099609375,\n 47.253135632244216\n ],\n [\n -69.005126953125,\n 47.431803338643334\n ],\n [\n -69.2413330078125,\n 47.46152250874388\n ],\n [\n -70.02685546875,\n 46.69843486113957\n ],\n [\n -70.0543212890625,\n 46.426499019253\n ],\n [\n -70.3070068359375,\n 46.17983040759436\n ],\n [\n -70.29052734375,\n 45.9587876403564\n ],\n [\n -70.2740478515625,\n 45.874712248904764\n ],\n [\n -70.3948974609375,\n 45.786679041363726\n ],\n [\n -70.3948974609375,\n 45.71385093029221\n ],\n [\n -70.4937744140625,\n 45.686995566120395\n ],\n [\n -70.7025146484375,\n 45.51789504294005\n ],\n [\n -70.6146240234375,\n 45.394592696926615\n ],\n [\n -70.6256103515625,\n 45.359865333959746\n ],\n [\n -70.7794189453125,\n 45.44471679159555\n ],\n [\n -70.8233642578125,\n 45.37530235052552\n ],\n [\n -70.8343505859375,\n 45.25555527789205\n ],\n [\n -70.86181640625,\n 45.213003555993964\n ],\n [\n -70.9552001953125,\n 45.31352900692258\n ],\n [\n -71.004638671875,\n 45.33284041773058\n ],\n [\n -71.048583984375,\n 45.31739181570158\n ],\n [\n -71.1419677734375,\n 45.263288531496876\n ],\n [\n -71.2957763671875,\n 45.298075138707965\n ],\n [\n -71.43310546875,\n 45.232349197513095\n ],\n [\n -71.5155029296875,\n 45.023067895446175\n ],\n [\n -73.355712890625,\n 45.00753503123719\n ],\n [\n -73.36669921875,\n 43.88997537383687\n ],\n [\n -73.38317871093749,\n 43.59630591596548\n ],\n [\n -73.267822265625,\n 43.59232754538541\n ],\n [\n -73.2843017578125,\n 43.08092540794885\n ],\n [\n -73.2733154296875,\n 42.755079545072135\n ],\n [\n -73.531494140625,\n 42.08599350447723\n ],\n [\n -73.49853515625,\n 42.004407212963585\n ],\n [\n -73.5479736328125,\n 41.430371882652814\n ],\n [\n -73.5369873046875,\n 41.306697618181865\n ],\n [\n -73.487548828125,\n 41.20758898181025\n ],\n [\n -73.6578369140625,\n 41.1455697310095\n ],\n [\n -73.685302734375,\n 41.075210270566636\n ],\n [\n -73.63037109375,\n 40.9964840143779\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: December 22, 2020; Version 1.1: February 11, 2021","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
In response to a co-occurring non-native scale infestation and Phragmites australis dieback in southeast Louisiana, normalized difference vegetation index (NDVI) satellite mapping was implemented to track P. australis condition in the lower Mississippi River Delta. While the NDVI mapping successfully documented relative condition changes, identification of cause required a quantitative-biophysical metric directly related to P. australis marsh live vegetation proportion. During this study, a satellite mapping tool that quantified P. australis live fraction cover (LFC) magnitude was designed and implemented. The key to development of the quantitative LFC mapping was the field to satellite calibration design. The calibration of P. australis marsh LFC to optical satellite image data combined field and near-in-time satellite data collections in the fall of 2018 and summer of 2019. Basing the field-NDVI to field-LFC calibrations and the satellite-NDVI to field-NDVI calibrations on combined pre-senescence and peak-growth period data offers nearly year-round LFC mapping. The utility of the developed P. australis marsh LFC mapping tool was demonstrated by the creation of a yearly suite of Mississippi River Delta LFC status and change maps extending from 2009 to 2019. P. australis marsh LFC mapping relies on Sentinel-2 for current to future mapping and relies on Landsat for historical mapping.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201131","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Rangoonwala, A., Howard, R.J., and Ramsey, E.W., III, 2020, Mapping Phragmites australis live fractional cover in the lower Mississippi River Delta, Louisiana (ver. 1.1, January 2021): U.S. Geological Survey Open-File Report 2020–1131, 24 p., https://doi.org/10.3133/ofr20201131.","productDescription":"Report: vii, 24 p.; Data Release","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-119555","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":381145,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ASPB4E","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Phragmites australis live fractional cover yearly map from 2009 to 2019 of the lower Mississippi River Delta using Landsat and Sentinel-2 satellite data"},{"id":381143,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1131/coverthb2.jpg"},{"id":381144,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1131/ofr20201131.pdf","text":"Report","size":"6.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1131"},{"id":382663,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2020/1131/versionHist.txt","text":"Version History","size":"4.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2020–1131 version history"}],"country":"United States","state":"Louisiana","otherGeospatial":"Lower Mississippi River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.54544067382812,\n 28.930045059458923\n ],\n [\n -89.000244140625,\n 28.930045059458923\n ],\n [\n -89.000244140625,\n 29.40371231103247\n ],\n [\n -89.54544067382812,\n 29.40371231103247\n ],\n [\n -89.54544067382812,\n 28.930045059458923\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: December 14, 2020; Version 1.1: January 27, 2021","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, Louisiana 70506
In conservation paradigms, management actions for umbrella species also benefit co‐occurring species because of overlapping ranges and similar habitat associations. The greater sage‐grouse (Centrocercus urophasianus) is an umbrella species because it occurs across vast sagebrush ecosystems of western North America and is the recipient of extensive habitat conservation and restoration efforts that might benefit sympatric species. Biologists' understanding of how non‐target species might benefit from sage‐grouse conservation is, however, limited. Reptiles, in particular, are of interest in this regard because of their relatively high diversity in shrublands and grasslands where sage‐grouse are found. Using spatial overlap of species distributions, land cover similarity statistics, and a literature review, we quantified which reptile species may benefit from the protection of intact sage‐grouse habitat and which may be affected by recent (since about 1990) habitat restoration actions targeting sage‐grouse. Of 190 reptile species in the United States and Canadian provinces where greater sage‐grouse occur, 70 (37%) occur within the range of the bird. Of these 70 species, about a third (11 snake and 11 lizard species) have >10% of their distribution area within the sage‐grouse range. Land cover similarity indices revealed that 14 of the 22 species (8 snake and 6 lizard species) had relatively similar land cover associations to those of sage‐grouse, suggesting greater potential to be protected under the sage‐grouse conservation umbrella and greater potential to be affected, either positively or negatively, by habitat management actions intended for sage‐grouse. Conversely, the remaining 8 species are less likely to be protected because of less overlap with sage‐grouse habitat and thus uncertain effects of sage‐grouse habitat management actions. Our analyses of treatment databases indicated that from 1990 to 2014 there were at least 6,400 treatments implemented on public land that covered approximately 4 million ha within the range of the sage‐grouse and, of that, >1.5 million ha were intended to at least partially benefit sage‐grouse. Whereas our results suggest that conservation of intact sagebrush vegetation communities could benefit ≥14 reptiles, a greater number than previously estimated, additional research on each species' response to habitat restoration actions is needed to assess broader claims of multi‐taxa benefits when it comes to manipulative sage‐grouse habitat management. Published 2020. This article is a U.S. Government work and is in the public domain in the USA.
The brown-headed cowbird (Molothrus ater; cowbird) is unique among North American blackbirds (Icteridae) because it is managed to mitigate the negative effects on endangered songbirds and economic losses in agricultural crops. Cowbird brood parasitism can further affect species that are considered threatened or endangered due to anthropogenic land uses. Historically, cowbirds have often been culled without addressing ultimate causes of songbird population declines. Similar to other North American blackbirds, cowbirds depredate agricultural crops, albeit at a lower rate reported for other blackbird species. Conflicting information exists on the extent of agricultural damage caused by cowbirds and the effectiveness of mitigation measures for application to management. In this paper, we reviewed the progress that has been made in cowbird management from approximately 2005 to 2020 in relation to endangered species. We also reviewed losses to the rice (Oryza sativa) crop attributed to cowbirds and the programs designed to reduce depredation. Of the 4 songbird species in which cowbirds have been managed, both the Kirtland’s warbler (Dendroica kirtlandii) and black-capped vireo (Vireo atricapilla) have been removed from the endangered species list following population increases in response to habitat expansion. Cowbird trapping has ceased for Kirtland’s warbler but continues for the vireo. In contrast, least Bell’s vireo (V. bellii pusillus) and southwestern willow flycatcher (Empidonax traillii extimus) still require cowbird control after modest increases in suitable habitat. Our review of rice depredation by cowbirds revealed models that have been created to determine the number of cowbirds that can be taken to decrease rice loss have been useful but require refinement with new data that incorporate cowbird population changes in the rice growing region, dietary preference studies, and current information on population sex ratios and female cowbird egg laying. Once this information has been gathered, bioenergetic and economic models would increase our understanding of the damage caused by cowbirds.
DGMETA (Dissolved Gas Modeling and Environmental Tracer Analysis) is a Microsoft Excel-based computer program that is used for modeling air-water equilibrium conditions from measurements of dissolved gases and for computing concentrations of environmental tracers that rely on air-water equilibrium model results. DGMETA can solve for the temperature, salinity, excess air, fractionation of gases, or pressure/elevation of water when it is equilibrated with the atmosphere. Models are calibrated inversely using one or more measurements of dissolved gases such as helium, neon, argon, krypton, xenon, and nitrogen. Excess nitrogen gas, originating from denitrification or other sources, also can be included as a fitted parameter or as a separate calculation from the dissolved gas modeling results. DGMETA uses the air-water equilibrium models to separate measured concentrations of gases and isotopes of gases into components that are used for tracing water in the environment. DGMETA calculates atmospheric dry-air mole fractions (mixing ratios) for transient atmospheric gas tracers such as chlorofluorocarbons, sulfur hexafluoride, and bromotrifluoromethane (Halon-1301); and concentrations of tritiogenic helium-3 and radiogenic helium-4, which accumulate from the decay of tritium in water and the decay of uranium and thorium in rocks, respectively.
Sample data can be graphed to identify applicable models of excess air, samples that contain excess nitrogen gas, or samples that have partially degassed, for example. Monte Carlo analysis of errors associated with dissolved gas equilibrium model results can be carried through computations of environmental tracer concentrations to provide robust estimates of error. In addition, graphical routines for separating helium sources using helium isotopes are included to refine estimates of tritiogenic helium-3 when terrigenic helium from mantle or crustal sources is present in samples. Environmental tracer concentrations and their errors computed from DGMETA can be used with other programs, such as TracerLPM (Jurgens and others, 2012), to determine groundwater ages and biogeochemical reaction rates. DGMETA also produces output files in a format that meets the U.S. Geological Survey open data requirements for documentation of model inputs and outputs.
DGMETA is a versatile and adaptable program that allows users to add solubility data for new gases, modify the existing set of gas solubility data, modify the default set of gases used for modeling, choose calculations based on real (non-ideal) gas behavior, and select various concentration units for data entry and results to match laboratory reports and study objectives. DGMETA comes with a set of gases widely used in hydrology and oceanography and many gases include multiple solubilities from previous work. Seventeen dissolved gases are included in the default version of the program: noble gases (helium, neon, argon, krypton, and xenon), reactive gases (nitrogen, oxygen, methane, carbon dioxide, carbon monoxide, hydrogen, and nitrous oxide), and environmental tracers (chlorofluorocarbon-11, chlorofluorocarbon-12, chlorofluorocarbon-113, sulfur hexafluoride, and Halon-1301).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm4F5","collaboration":"National Water Quality Assessment Project","usgsCitation":"Jurgens, B.C., Böhlke, J., Haase, K., Busenberg, E., Hunt, A.G., and Hansen, J.A., 2020, DGMETA (version 1)—Dissolved gas modeling and environmental tracer analysis computer program: U.S. Geological Survey Techniques and Methods 4-F5, 50 p., https://doi.org/10.3133/tm4F5.","productDescription":"Report: viii, 50 p.; Software Release","onlineOnly":"Y","ipdsId":"IP-100912","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":436689,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NQ1RFY","text":"USGS data release","linkHelpText":"DGMETA (Version 1): Dissolved Gas Modeling and Environmental Tracer Analysis Computer Program"},{"id":382045,"rank":3,"type":{"id":35,"text":"Software Release"},"url":"https://code.usgs.gov/cawsc/DGMETA","text":"DGMETA","linkHelpText":"- DGMETA (Dissolved Gas Modeling and Environmental Tracer Analysis) is a Microsoft Excel-based computer program that is used for modeling air-water equilibrium conditions from measurements of dissolved gases and for computing concentrations of environmental tracers that rely on air-water equilibrium model results."},{"id":382038,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/04/f05/coverthb.jpg"},{"id":382039,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/f05/tm4f5.pdf","text":"Report","size":"8.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 4-F5"}],"contact":"NAWQA Science Team
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192–0002
Bathymetric geoprocessing analyses of the Florida Reef Tract have provided insights into trends of seafloor accretion and seafloor erosion over time and following major storm events. However, bathymetric surveys sometimes capture manmade structures and vegetation, which do not represent the desired bare-earth data. Therefore, ground truthing is essential to maintain the most accurate bathymetric data possible. Field procedures were developed in the Florida Reef Tract in order to quickly and accurately collect consistent imagery and habitat data across variable sites. Areas of significant elevation change were determined through elevation change analyses; these areas were targeted for ground truthing in order to check the reliability of the surveys. This report outlines the standard operating procedures for underwater photographic imagery and habitat data collection, as well as procedures for the storage of these photographs and associated metadata. These standard operating procedures ensure the reproducibility of photographic operations and habitat data collection in future field excursions, enable longitudinal visual comparisons alongside seafloor elevation change analyses, and also have the potential to be applied to similar studies in different coastal environments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201118","usgsCitation":"Fehr, Z.W., and Yates, K.K., 2020, Underwater photographic reconnaissance and habitat data collection in the Florida Keys—A procedure for ground truthing remotely sensed bathymetric data: U.S. Geological Survey Open-File Report 2020–1118, 13 p., https://doi.org/10.3133/ofr20201118.","productDescription":"vii, 13 p.","numberOfPages":"13","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-114891","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":381619,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1118/ofr20201118.pdf","text":"Report","size":"5.71 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1118"},{"id":381618,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1118/coverthb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Florida Keys","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.53665161132812,\n 25.022150920405707\n ],\n [\n -80.1177978515625,\n 25.022150920405707\n ],\n [\n -80.1177978515625,\n 25.336579097268118\n ],\n [\n -80.53665161132812,\n 25.336579097268118\n ],\n [\n -80.53665161132812,\n 25.022150920405707\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701