We used data collected during surveys of seven North American Breeding Bird Survey routes in eastern Texas to estimate avian populations within Big Thicket National Preserve. On only 61 of the 350 count locations located along these routes did observers monitor birds within the boundaries of this preserve. On selected routes, we recorded initial bird detections during the 3-min bird count within 1-min time intervals and within two distance classes (≤50 or >50 m). We used these data, combined with data collected using standard Breeding Bird Survey protocols during 2009–2016, to estimate detection probabilities and effective detection radii for commonly detected species. For species often detected in flocks, we estimated these parameters for group detections. From these parameters, we estimated regional densities for 60 species. Because habitat within Big Thicket National Preserve differed from habitat along surveyed routes, for each species we adjusted the projected population estimate to account for the relationship between density of detected birds and habitat descriptors from the National Land Cover database. On the basis of our estimates of regional density of each species, and accounting for differences in habitat availability, we estimated that commonly detected avian species comprises a population of 192,201 breeding birds (95% confidence interval = 144,269–340,790) within Big Thicket National Preserve.
","language":"English","publisher":"BioOne","doi":"10.1894/0038-4909-66.3.240","usgsCitation":"Twedt, D.J., and Shackelford, C.E., 2022, Use of regional breeding bird surveys to estimate bird populations in Big Thicket National Preserve: The Southwestern Naturalist, v. 66, no. 3, p. 240-249, https://doi.org/10.1894/0038-4909-66.3.240.","productDescription":"10 p.","startPage":"240","endPage":"249","ipdsId":"IP-065568","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":418650,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Big Thicket National Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.4910453710765,\n 30.60117874378041\n ],\n [\n -94.4910453710765,\n 30.35392517388506\n ],\n [\n -94.20415064179676,\n 30.35392517388506\n ],\n [\n -94.20415064179676,\n 30.60117874378041\n ],\n [\n -94.4910453710765,\n 30.60117874378041\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"66","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Twedt, Daniel J. 0000-0003-1223-5045 dtwedt@usgs.gov","orcid":"https://orcid.org/0000-0003-1223-5045","contributorId":398,"corporation":false,"usgs":true,"family":"Twedt","given":"Daniel","email":"dtwedt@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":876669,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shackelford, Clifford E.","contributorId":315488,"corporation":false,"usgs":false,"family":"Shackelford","given":"Clifford","email":"","middleInitial":"E.","affiliations":[{"id":68340,"text":"Texas Parks and Wildlife Department, 506 Hayter St., Nacogdoches, Texas 75965","active":true,"usgs":false}],"preferred":false,"id":876670,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70237176,"text":"cir1500 - 2022 - 2022 Joint Agency Commercial Imagery Evaluation—Remote sensing satellite compendium","interactions":[{"subject":{"id":70212975,"text":"cir1468 - 2020 - 2020 Joint Agency Commercial Imagery Evaluation—Remote sensing satellite compendium","indexId":"cir1468","publicationYear":"2020","noYear":false,"displayTitle":"2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium","title":"2020 Joint Agency Commercial Imagery Evaluation—Remote sensing satellite compendium"},"predicate":"SUPERSEDED_BY","object":{"id":70237176,"text":"cir1500 - 2022 - 2022 Joint Agency Commercial Imagery Evaluation—Remote sensing satellite compendium","indexId":"cir1500","publicationYear":"2022","noYear":false,"title":"2022 Joint Agency Commercial Imagery Evaluation—Remote sensing satellite compendium"},"id":1}],"lastModifiedDate":"2022-10-04T19:55:21.162292","indexId":"cir1500","displayToPublicDate":"2022-10-03T17:23:03","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1500","displayTitle":"2022 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium","title":"2022 Joint Agency Commercial Imagery Evaluation—Remote sensing satellite compendium","docAbstract":"The Joint Agency Commercial Imagery Evaluation (JACIE) is a collaboration between six Federal agencies that are major users and producers of satellite land remote sensing data. In recent years, the JACIE group has observed ever-increasing numbers of remote sensing satellites being launched. This rapidly growing wave of new systems creates a need for a single reference for land remote sensing satellites that provides basic system specifications and linkage to any JACIE assessment that may have been completed on existing systems. This volume has been assembled by the Requirements, Capabilities, and Analysis for Earth Observation Project under the U.S. Geological Survey National Land Imaging Program as a contribution to the JACIE community. This is the third edition of the JACIE compendium, which is planned to be updated and released annually.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1500","usgsCitation":"Ramaseri Chandra, S.N., Christopherson, J.B., Casey, K.A., Lawson, J., and Sampath, A., 2022, 2022 Joint Agency Commercial Imagery Evaluation—Remote sensing satellite compendium: U.S. Geological Survey Circular 1500, 279 p., https://doi.org/10.3133/cir1500. [Supersedes USGS Circular 1468.]","productDescription":"xiii, 279 p.","numberOfPages":"298","onlineOnly":"N","ipdsId":"IP-139076","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":407832,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1500/circ1500.pdf","text":"Report","size":"21.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1500"},{"id":407831,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1500/coverthb.jpg"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Formally protected areas are an important component of wildlife conservation, but face limitations in their effectiveness for migratory species. Improved stewardship of working lands around protected areas is one solution for conservation planning, but private working lands are vulnerable to development. In the Greater Yellowstone Ecosystem (GYE), ungulates such as elk (Cervus canadensis) use both protected areas and private lands throughout their annual migrations. We studied patterns of landownership, protection, and conservation challenges within the ranges of migratory elk in the GYE. We used GPS data from 1088 elk in 26 herds to define herd-level seasonal ranges, and extracted covariates related to landownership and protection, land use, and human infrastructure and development. All elk herds used land encompassing >1 ownership type. Most elk herds (92.3 % of herds, n = 24) used the highest proportion of private land in the winter (mean = 36.2 % private land). Most elk herds' winter ranges contained the highest building densities (mean = 1.24 buildings/km2), fence densities (mean = 1.02 km fence/km2), and cattle grazing (mean = 1.9 cows/km2), compared to migratory and summer ranges. Out of all ranges, 36.5 % of ranges did not have any zoning regulations, indicating the potential for future development. Our results show that elk in the GYE rely on habitat outside of protected areas, and face landscape-scale conservation challenges such as habitat fragmentation from human development, particularly in winter ranges. Future conservation strategies for wildlife in this system need to encompass coordination across both public and private land to ensure migratory connectivity.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2022.109752","usgsCitation":"Gigliottia, L.C., Wenjing Xu, Zuckerman, G., Atwood, M.P., Eric K. Cole, Alyson Courtemanch, Dewey, S., Gude, J.A., Hnilicka, P., Kauffman, M., Kroetz, K., Lawson, A., Leonard, B., MacNulty, D., Maichak, E., McWhirter, D., Mong, T.W., Proffitt, K., Scurlock, B., Stahler, D., and Middleton, A.D., 2022, Wildlife migrations highlight importance of both private lands and protected areas in the Greater Yellowstone Ecosystem: Biological Conservation, v. 275, 109752, 9 p., https://doi.org/10.1016/j.biocon.2022.109752.","productDescription":"109752, 9 p.","ipdsId":"IP-141698","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":446239,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2022.109752","text":"Publisher Index Page"},{"id":430023,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Greater Yellowstone Ecosystem","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.79292022701935,\n 45.50034062999211\n ],\n [\n -111.79292022701935,\n 43.92815809407176\n ],\n [\n -109.67116747387536,\n 43.92815809407176\n ],\n [\n -109.67116747387536,\n 45.50034062999211\n ],\n [\n -111.79292022701935,\n 45.50034062999211\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"275","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gigliottia, Laura C.","contributorId":338702,"corporation":false,"usgs":false,"family":"Gigliottia","given":"Laura","email":"","middleInitial":"C.","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":903466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wenjing Xu","contributorId":338703,"corporation":false,"usgs":false,"family":"Wenjing Xu","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":903467,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zuckerman, Gabriel","contributorId":338704,"corporation":false,"usgs":false,"family":"Zuckerman","given":"Gabriel","email":"","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":903468,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Atwood, M. 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2022 - Corrigendum: Associations between cyanobacteria and indices of secondary production in the western basin of Lake Erie","interactions":[],"lastModifiedDate":"2024-09-26T13:40:43.394886","indexId":"70258749","displayToPublicDate":"2022-10-03T08:32:30","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Corrigendum: Associations between cyanobacteria and indices of secondary production in the western basin of Lake Erie","docAbstract":"In the last year, we became aware that data used in our above-referenced manuscript from 2018 published in Limnology and Oceanography contained significant errors. In the 2018 manuscript, we found that indices of secondary production were negatively correlated to indices of cyanobacterial abundance and toxicity. Unfortunately, one of our indices of cyanobacterial abundance (biovolume) and our measurement of toxicity (microcystin concentration) were inaccurate in the data we used in the 2018 manuscript. Upon discovering these errors, we immediately began correcting the repositories where these data were available. Having corrected those data repositories, we are now reporting on the re-analysis of the data using the methods previously described in the 2018 manuscript. Although the relationships are slightly different, our interpretation is that the conclusions of the 2018 manuscript are still valid using the corrected data. The data errors we experienced were traced to a spreadsheet that was used to share data among the research team, which had errors caused by mistakes in copy-pasting formulas instead of data and ‘inadvertent’ edits that were saved by the spreadsheet's auto-save function. We apologize to the scientific community for these errors.
","language":"English","publisher":"ASLO","doi":"10.1002/lno.12216","usgsCitation":"Larson, J.H., Evans, M.A., Kennedy, R., Bailey, S., Loftin, K.A., Laughrey, Z.R., Femmer, R.A., Schaeffer, J., Richardson, W., Wynne, T., Nelson, J.C., and Duris, J.W., 2022, Corrigendum: Associations between cyanobacteria and indices of secondary production in the western basin of Lake Erie: Limnology and Oceanography, v. 67, no. 11, p. 2617-2620, https://doi.org/10.1002/lno.12216.","productDescription":"4 p.","startPage":"2617","endPage":"2620","ipdsId":"IP-138062","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":462275,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.51669311523438,\n 41.51063406062076\n ],\n [\n -82.77923583984375,\n 41.51063406062076\n ],\n [\n -82.77923583984375,\n 42.04011410708205\n ],\n [\n -83.51669311523438,\n 42.04011410708205\n ],\n [\n -83.51669311523438,\n 41.51063406062076\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"67","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-10-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Larson, James H. 0000-0002-6414-9758 jhlarson@usgs.gov","orcid":"https://orcid.org/0000-0002-6414-9758","contributorId":4250,"corporation":false,"usgs":true,"family":"Larson","given":"James","email":"jhlarson@usgs.gov","middleInitial":"H.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":913946,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evans, Mary Anne 0000-0002-1627-7210 maevans@usgs.gov","orcid":"https://orcid.org/0000-0002-1627-7210","contributorId":149358,"corporation":false,"usgs":true,"family":"Evans","given":"Mary","email":"maevans@usgs.gov","middleInitial":"Anne","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":913947,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kennedy, Robert J 0000-0003-2135-5022","orcid":"https://orcid.org/0000-0003-2135-5022","contributorId":215686,"corporation":false,"usgs":false,"family":"Kennedy","given":"Robert J","affiliations":[{"id":39305,"text":"Former UMESC employee - retired","active":true,"usgs":false}],"preferred":false,"id":913948,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bailey, Sean 0000-0003-0361-7914 sbailey@usgs.gov","orcid":"https://orcid.org/0000-0003-0361-7914","contributorId":198515,"corporation":false,"usgs":true,"family":"Bailey","given":"Sean","email":"sbailey@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":913949,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loftin, Keith A. 0000-0001-5291-876X","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":221964,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":913950,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Laughrey, Zachary R. 0000-0002-7630-2078 zlaughrey@usgs.gov","orcid":"https://orcid.org/0000-0002-7630-2078","contributorId":198516,"corporation":false,"usgs":true,"family":"Laughrey","given":"Zachary","email":"zlaughrey@usgs.gov","middleInitial":"R.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":913951,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Femmer, Robin A.","contributorId":344511,"corporation":false,"usgs":false,"family":"Femmer","given":"Robin","email":"","middleInitial":"A.","affiliations":[{"id":82381,"text":"former USGS, Kansas Water Science Center employee","active":true,"usgs":false}],"preferred":false,"id":913952,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schaeffer, Jeff S.","contributorId":344512,"corporation":false,"usgs":false,"family":"Schaeffer","given":"Jeff S.","affiliations":[{"id":56209,"text":"Tennessee Tech University","active":true,"usgs":false}],"preferred":false,"id":913953,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Richardson, William B. 0000-0002-7471-4394","orcid":"https://orcid.org/0000-0002-7471-4394","contributorId":344513,"corporation":false,"usgs":false,"family":"Richardson","given":"William B.","affiliations":[{"id":64165,"text":"former USGS, UMESC employee (retired)","active":true,"usgs":false}],"preferred":false,"id":913954,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wynne, T.T.","contributorId":344514,"corporation":false,"usgs":false,"family":"Wynne","given":"T.T.","email":"","affiliations":[{"id":34793,"text":"National Oceanic and Atmospheric Administration (NOAA)","active":true,"usgs":false}],"preferred":false,"id":913955,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Nelson, John C. 0000-0002-7105-0107 jcnelson@usgs.gov","orcid":"https://orcid.org/0000-0002-7105-0107","contributorId":149361,"corporation":false,"usgs":true,"family":"Nelson","given":"John","email":"jcnelson@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":913956,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Duris, Joseph W. 0000-0002-8669-8109 jwduris@usgs.gov","orcid":"https://orcid.org/0000-0002-8669-8109","contributorId":1981,"corporation":false,"usgs":true,"family":"Duris","given":"Joseph","email":"jwduris@usgs.gov","middleInitial":"W.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":false,"id":913957,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70237184,"text":"70237184 - 2022 - Last Glacial Maximum and early deglaciation in the Stura Valley, southwestern European Alps","interactions":[],"lastModifiedDate":"2022-10-04T11:59:18.208422","indexId":"70237184","displayToPublicDate":"2022-10-03T06:55:23","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Last Glacial Maximum and early deglaciation in the Stura Valley, southwestern European Alps","docAbstract":"We combined data from geomorphologic surveys, glacial modelling, and 10Be exposure ages of boulders on moraines, to investigate the Last Glacial Maximum (LGM) and the early retreat glacial phases in the Stura Valley of the Maritime Alps. We used the exposure ages to reconstruct the timing of standstills or readvances which interrupted the post-LGM withdrawal, initiated ∼24 ka. We mapped and dated the frontal moraines of a first glacial standstill/readvance at a short distance (∼7 km) from the maximum external limit of the LGM, which occurred at ∼22 ka, and a second one at ∼19 ka (Bühl stadial). This morpho-chronologic succession is congruent with that obtained in the adjacent Gesso Valley and, combined with the similarity of Equilibrium Line Altitude values, demonstrates a consistent glacial response in the Maritime Alps to climatic forcing.
Our data are chronologically consistent with those of the southern flank of the European Alps, stressing not only a general synchroneity of the LGM across the various sectors, but also that of a LGM recessional standstill or readvance at ∼22 ka. The short distance between the LGM moraines and the recessionary phase moraines, and minimal difference in ELA indicate a modest variation in the mass balance of the Maritime Alps glaciers during this time interval. A similar modest variation between LGM and the first recessional phase glacier mass balance is also found throughout the western sector of the Southern Alps but is considerably more pronounced for the glaciers of the central-eastern sectors. This behaviour can be explained by the interplay between the moisture supplied by southern currents sourced in the Western Mediterranean and that advected by the westerlies sourced in the North Atlantic, which affected the various sectors of the Southern Alps differently.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2022.107770","usgsCitation":"Ribolini, A., Spagnolo, M., Cyr, A.J., and Federici, P.R., 2022, Last Glacial Maximum and early deglaciation in the Stura Valley, southwestern European Alps: Quaternary Science Reviews, v. 295, 107770, 17 p., https://doi.org/10.1016/j.quascirev.2022.107770.","productDescription":"107770, 17 p.","ipdsId":"IP-140334","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":446241,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2022.107770","text":"Publisher Index Page"},{"id":435668,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HCE4EC","text":"USGS data release","linkHelpText":"Data release for cosmogenic beryllium-10 exposure ages of moraine boulders in the Stura Valley, Maritime Alps, northwestern Italy"},{"id":407853,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy, France","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 7.305908203125,\n 43.97700467496408\n ],\n [\n 7.80029296875,\n 43.97700467496408\n ],\n [\n 7.80029296875,\n 44.32384807250689\n ],\n [\n 7.305908203125,\n 44.32384807250689\n ],\n [\n 7.305908203125,\n 43.97700467496408\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"295","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ribolini, Adriano 0000-0001-5851-8775","orcid":"https://orcid.org/0000-0001-5851-8775","contributorId":291770,"corporation":false,"usgs":false,"family":"Ribolini","given":"Adriano","email":"","affiliations":[{"id":62747,"text":"Department of Earth Sciences, University of Pisa, Italy","active":true,"usgs":false}],"preferred":false,"id":853586,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spagnolo, Matteo 0000-0002-2753-338X","orcid":"https://orcid.org/0000-0002-2753-338X","contributorId":291771,"corporation":false,"usgs":false,"family":"Spagnolo","given":"Matteo","email":"","affiliations":[{"id":62748,"text":"Department of Geography & Environment, University of Aberdeen, UK","active":true,"usgs":false}],"preferred":false,"id":853587,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cyr, Andrew J. 0000-0003-2293-5395 acyr@usgs.gov","orcid":"https://orcid.org/0000-0003-2293-5395","contributorId":3539,"corporation":false,"usgs":true,"family":"Cyr","given":"Andrew","email":"acyr@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":853588,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Federici, Paolo Roberto","contributorId":297167,"corporation":false,"usgs":false,"family":"Federici","given":"Paolo","email":"","middleInitial":"Roberto","affiliations":[{"id":64311,"text":"retired, Department of Earth Sciences, University of Pisa, Italy","active":true,"usgs":false}],"preferred":false,"id":853589,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237716,"text":"70237716 - 2022 - Urbanization of grasslands in the Denver area affects streamflow responses to rainfall events","interactions":[],"lastModifiedDate":"2022-10-20T11:54:45.648562","indexId":"70237716","displayToPublicDate":"2022-10-03T06:52:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Urbanization of grasslands in the Denver area affects streamflow responses to rainfall events","docAbstract":"A thorough understanding of how urbanization affects stream hydrology is crucial for effective and sustainable water management, particularly in rapidly urbanizing regions. This study presents a comprehensive analysis of changes in streamflow response to rainfall events across a rural to urban gradient in the semi-arid area of Denver, Colorado. We used 8 years of April to October instantaneous streamflow data in 21 watersheds ranging in size from 0.8 to 90 km2 and with impervious areas ranging from 1% to 47%. With these data, we applied a semi-automated method to identify a total of 2877 streamflow responses, which were analysed for event-based metrics of peak flow, runoff depth, runoff to rainfall ratio, time to peak, duration and number of streamflow responses to rainfall events. We also determined whether streamflow responses could be predicted by a precipitation threshold. Watersheds with >10% impervious cover had a precipitation threshold of 1–2 mm/hr needed to produce a streamflow response, compared to thresholds of 4–36 mm/hr for watersheds with less than 10% impervious surface cover. This lower precipitation threshold in more impervious watersheds led to more frequent streamflow responses. On average, streamflow responses had shorter duration and higher peak flows in watersheds with more impervious surface cover. In contrast to other regions, runoff depth, runoff to rainfall ratio and time to peak either gave mixed results or did not vary significantly with imperviousness. These alterations in streamflow response to rainfall events indicate the specific ways that urban development changes how streams respond to rain events in a semi-arid setting. This work points to the need for local adaptation of stormwater management to mitigate the effects of streamflow changes with urbanization.
The Bushy Park Reservoir is a relatively shallow impoundment in southeastern South Carolina. The reservoir, located under a semi-tropical climate, is the principal water supply for the city of Charleston, South Carolina, and the surrounding areas including the Bushy Park Industrial Complex. Although there was an adequate supply of freshwater in the reservoir in 2022, water-quality concerns are present over taste-and-odor and saltwater-intrusion issues. From 2013 to 2015, the U.S. Geological Survey (USGS), in cooperation with the Charleston Water System, engaged in a multi-year study of the hydrology and hydrodynamics of Bushy Park Reservoir to better understand factors affecting water-quality conditions in the reservoir. As part of this study, Charleston Water System worked with Tetra Tech, Inc., a consulting and engineering firm, to develop a Bushy Park Reservoir hydrodynamic and water-quality modeling framework, built upon earlier efforts by both Tetra Tech and the U.S. Army Corps of Engineers. At the completion of the new modeling framework, the USGS was requested to evaluate the calibrated hydrodynamic and water-quality model.
The Bushy Park Reservoir Environmental Fluid Dynamics Code (EFDC) model was calibrated for the time period from January 1, 2012, to December 31, 2015. The general modeling approach for the newly revised modeling framework, as briefly detailed in this report, was developed with EFDC. The EFDC is a grid-based modeling package that can simulate three-dimensional flow, transport, and water quality in surface-water systems. This report evaluated the capacity of Tetra Tech’s Bushy Park EFDC model to simulate water discharge, water circulation, surface elevations, temperature, salinity, and other water-quality parameters.
The USGS model review focused specifically on the following criteria: (1) determine if the model, with additional effort, could be developed into an adequate planning tool for Bushy Park Reservoir; (2) assess the capacity of the model to specifically address water-quality issues in the reservoir related to taste-and-odor and saltwater intrusions; and, (3) evaluate three preliminary water-management scenarios related to reduced water withdrawals in the reservoir and the effect on saltwater intrusion.
Overall, the model was able to simulate discharge, flow velocity, and water-surface elevations with generally good agreement between the simulated and measured values. Specifically, the model was able to demonstrate good agreement for discharge at two USGS continuous discharge locations (USGS station 02172002; USGS station 02172040), with Wilmott index of agreements of 0.86 and 0.75, respectively. A total of seven USGS streamgages, located on the West Branch of the Cooper River, Durham Canal, and the Cooper River, were available for water-surface elevations, with index of agreements ranging from 0.74 to 0.99. However, model-simulated water-surface elevation ranges were appreciably high (compared to measured ranges) for two locations near Pinopolis Dam, farthest upstream on the West Branch of the Cooper River. This result may indicate that too much simulated tidal energy propagated through the model domain.
For water temperature, 16 calibration stations were available for at least part of the 4-year simulation. The index of agreement range for temperature comparisons was from 0.95 to 1.00, indicating excellent agreement between the measured and simulated results. One of the primary future applications for the Bushy Park Reservoir EFDC model is to determine the extent of saltwater intrusions. A wide range in the salinity prediction quality was simulated with the model. The prediction quality ranged from an index of agreement of 0.15 at Cooper River approximately 2.75 miles southeast of the Tee, South Carolina, to 0.92 at West Branch Cooper River near Moncks Corner, South Carolina. Although the model did not accurately simulate some of the larger salinity deviations resulting during individual hydrologic events, the seasonal salinity trends were adequately simulated with the model during the study period (2012–15). Therefore, it may be difficult to simulate extreme hydrologic events, such as during large storms, where high salinity water is exchanged with Bushy Park Reservoir. There was agreement in model simulation with the measured data either on the quantitative index of agreement values or qualitative agreement in the seasonal salinity data trends.
For water quality, the index of agreement values were generally low for total nitrogen, ammonia, nitrate, total Kjeldahl nitrogen, total phosphorus, and orthophosphate. Although general trends were adequately simulated at specific stations, particularly for Bushy Park Reservoir, the model-simulated fit was low across all the constituents described above with index of agreements usually below 0.50. A limitation for simulating nutrient concentrations across the model domain was the lack of characterization for the constituents directly entering Bushy Park Reservoir, or the lack of data directly attributed to the boundary condition (for example, the Cooper River). The other two calibrated water-quality constituents (besides the nutrients mentioned above) were dissolved oxygen and chlorophyll a. Dissolved oxygen varied from index of agreement values from 0.58 to 0.94 for 11 stations, generally indicating agreement with the available measured data. Chlorophyll a, calibrated for seven stations, had a wider range from 0.11 to 0.74 for the index of agreement.
With the current modeling framework, taste-and-odor events, related to cyanobacterial blooms, cannot be directly simulated. However, indirect estimates of cyanobacteria concentrations may be obtained by using the chlorophyll a model outputs, which represent total phytoplankton biomass, and the phytoplankton biovolume data by group (diatoms, green algae, cyanobacteria and others) collected from 2012 to 2015. For the Bushy Park Reservoir modeling framework to be used directly for taste-and-odor issues, cyanobacteria must be simulated and calibrated based on observations of cyanobacteria biomass concentrations. In addition to the cyanobacteria sampling conducted within the reservoir between 2012 and 2015, the new model calibration would also require new algae biomass data-collection efforts to characterize the external sources of cyanobacteria entering the Bushy Park Reservoir from tributaries, as well as the internal cycling, production, and decay of cyanobacteria in the hydrologic system.
Further improvements to the EFDC model would include expanding the collection of boundary condition datasets, such as water-quality monitoring to determine improved nutrient loads into the model domain. Along with improved water-quality monitoring for the major boundary conditions, continuous discharge, for both Foster Creek and the Back River, would further constrain the flow balance and the loads into Bushy Park Reservoir. In addition to better boundary-condition characterization, it is important to better characterize possible shortcomings specifically to the model domain, such as the grid resolution, bathymetry, and numerical hydrodynamic errors. Further consideration of the model may involve a sensitivity analysis to determine if errors in the simulation outputs, such as discharge, water-surface elevations, and salinity, were more likely caused by poor boundary condition characterization or, specifically, the model setup.
Three model scenarios were run with the revised Bushy Park Reservoir model: (1) reduced withdrawals from one of the large intake-discharge locations for Bushy Park Reservoir, the Williams Station; (2) elevated (above background levels) ocean water level causing saltwater intrusion from the ocean through Durham Canal into Bushy Park Reservoir; and (3) overtopping of the Back River Dam at the southernmost end of Bushy Park Reservoir. For the reduced withdrawals scenarios, the largest shift in flow resulted near the Williams Station intake, with the next largest flow change at the southern end of Bushy Park Reservoir, and a net increase in flow out of the Bushy Park Reservoir to the Cooper River by way of the Durham Canal. The effect resulting from scenario 3 on water quality and salinity was small, with larger increases for dissolved oxygen than other constituents at several monitoring stations. For the two scenarios related to saltwater intrusion (including dam overtopping), the changes in salinity generally were found to dissipate in the following 2 weeks and generally back to baseline salinity conditions within 3 months. This result did vary depending on the severity of the storm or length of the dam overtopping event.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221079","collaboration":"Prepared in cooperation with Charleston Water System","usgsCitation":"Smith, E.A., Akasapu-Smith, M., Petkewich, M.D., and Conrads, P.A., 2022, Evaluation of the Bushy Park Reservoir three-dimensional hydrodynamic and water-quality model, South Carolina, 2012–15: U.S. Geological Survey Open-File Report 2022–1079, 35 p., https://doi.org/10.3133/ofr20221079.","productDescription":"Report: ix, 35 p.; Data Release","numberOfPages":"35","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087955","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501828,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113612.htm","linkFileType":{"id":5,"text":"html"}},{"id":407629,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1079/coverthb.jpg"},{"id":407630,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1079/ofr20221079.pdf","text":"Report","size":"5.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1079"},{"id":407632,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1079/ofr20221079.XML"},{"id":407633,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1079/images/"},{"id":407634,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7NG4NVX","text":"USGS data release","linkHelpText":"Water quality data for Bushy Park Reservoir, South Carolina 2013–2015"},{"id":407631,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221079/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1079"}],"country":"United States","state":"South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.39794921875,\n 32.676372772089806\n ],\n [\n -79.661865234375,\n 32.676372772089806\n ],\n [\n -79.661865234375,\n 33.458942753687616\n ],\n [\n -80.39794921875,\n 33.458942753687616\n ],\n [\n -80.39794921875,\n 32.676372772089806\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road, Suite 129
Columbia, SC 29210
Landslides are a significant hazard and dominant feature throughout the landscape of the Pacific Northwest. However, the hazard and risk posed by coseismic landslides triggered by great Cascadia Subduction Zone (CSZ) earthquakes is highly uncertain due to a lack of local and global data. Despite a wealth of other geologic evidence for past earthquakes on the Cascadia Subduction Zone, no landslides have been definitively linked to such earthquakes, even in areas otherwise highly susceptible to failure. While shallow landslides may not leave a lasting topographical signature in the landscape, there are thousands of deep-seated landslides in Cascadia, and these deposits often persist for hundreds of years and multiple earthquake cycles. Synthesizing newly developed inventories of dated large deep-seated landslides in the Oregon Coast Range, we use statistical methods to estimate the proportion of these types of landslides that could have been triggered during past great Cascadia Subduction Zone earthquakes. Statistical analysis of high-precision dendrochronology ages of landslide-dammed lakes and surface roughness-dated bedrock landslides reveal Cascadia Subduction Zone earthquakes may have triggered 0–15 % of large deep-seated landslides in the Oregon Coast Range over multiple earthquake cycles. Our results refine estimates from previous studies and further suggest that coseismic triggering accounts for a small fraction of the total deep-seated bedrock landslides mapped in coastal Cascadia. However, if the real rate of coseismic landslide triggering during CSZ earthquakes is near our estimated upper bound for the 1700 CSZ earthquake, we estimate up to 2400 coseismic large deep-seated landslides could occur in the Oregon Coast Range in a single earthquake. These findings suggest Cascadia is consistent with global observations from other subduction zones and that coseismic landslides may still represent a serious geohazard in the region.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2022.108477","usgsCitation":"Grant, A.R., Struble, W., and LaHusen, S.R., 2022, Limits to coseismic landslides triggered by Cascadia Subduction Zone earthquakes: Geomorphology, v. 418, 108477, 9 p., https://doi.org/10.1016/j.geomorph.2022.108477.","productDescription":"108477, 9 p.","ipdsId":"IP-135543","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":419819,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Oregon Coast Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.28385322714739,\n 46.162939083720374\n ],\n [\n -124.07715534899053,\n 46.16100010342029\n ],\n [\n -124.77236586647153,\n 42.306995247191736\n ],\n [\n -122.40618418763233,\n 42.306995247191736\n ],\n [\n -122.28385322714739,\n 46.162939083720374\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"418","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Grant, Alex R. 0000-0002-5096-4305","orcid":"https://orcid.org/0000-0002-5096-4305","contributorId":219066,"corporation":false,"usgs":true,"family":"Grant","given":"Alex","middleInitial":"R.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":880166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Struble, William 0000-0002-8163-5088","orcid":"https://orcid.org/0000-0002-8163-5088","contributorId":241913,"corporation":false,"usgs":false,"family":"Struble","given":"William","email":"","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":880167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaHusen, Sean Richard 0000-0003-4246-4439","orcid":"https://orcid.org/0000-0003-4246-4439","contributorId":294677,"corporation":false,"usgs":true,"family":"LaHusen","given":"Sean","email":"","middleInitial":"Richard","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":880168,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70239733,"text":"70239733 - 2022 - Chemical geodynamics insights from a machine learning approach","interactions":[],"lastModifiedDate":"2023-01-16T19:24:54.719451","indexId":"70239733","displayToPublicDate":"2022-10-01T13:22:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Chemical geodynamics insights from a machine learning approach","docAbstract":"The radiogenic isotope heterogeneity of oceanic basalts is often assessed using 2D isotope ratio diagrams. But because the underlying data are at least six dimensional (87Sr/86Sr, 143Nd/144Nd, 176Hf/177Hf, and 208,207,206Pb/204Pb), it is important to examine isotopic affinities in multi-dimensional data space. Here, we apply t-distributed stochastic neighbor embedding (t-SNE), a multi-variate statistical data analysis technique, to a recent compilation of radiogenic isotope data of mid ocean ridge (MORB) and ocean island basalts (OIB). The t-SNE results show that the apparent overlap of MORB-OIB data trends in 2-3D isotope ratios diagrams does not exist in multi-dimensional isotope data space, revealing that there is no discrete “component” that is common to most MORB-OIB mantle sources on a global scale. Rather, MORB-OIB sample stochastically distributed small-scale isotopic heterogeneities. Yet, oceanic basalts with the same isotopic affinity, as identified by t-SNE, delineate several globally distributed regional domains. In the regional geodynamic context, the isotopic affinity of MORB and OIB is caused by capturing of actively upwelling mantle by adjacent ridges, and thus melting of mantle with similar origin in on, near, and off-ridge settings. Moreover, within a given isotopic domain, subsidiary upwellings rising from a common deep mantle root often feed OIB volcanism over large surface areas. Overall, the t-SNE results define a fundamentally new basis for relating isotopic variations in oceanic basalts to mantle geodynamics, and may launch a 21st century era of “chemical geodynamics.”
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022GC010606","usgsCitation":"Stracke, A., Willig, M., Genske, F., Béguelin, P., and Todd, E., 2022, Chemical geodynamics insights from a machine learning approach: Geochemistry, Geophysics, Geosystems, v. 23, no. 10, e2022GC010606, 41 p., https://doi.org/10.1029/2022GC010606.","productDescription":"e2022GC010606, 41 p.","ipdsId":"IP-142586","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":446253,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022gc010606","text":"Publisher Index Page"},{"id":411964,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-10-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Stracke, Andreas","contributorId":216038,"corporation":false,"usgs":false,"family":"Stracke","given":"Andreas","email":"","affiliations":[{"id":39353,"text":"Westfälische Wilhelms Universität, Institüt für Mineralogie, Münster, Germany","active":true,"usgs":false}],"preferred":false,"id":861679,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Willig, M.","contributorId":300990,"corporation":false,"usgs":false,"family":"Willig","given":"M.","affiliations":[{"id":65268,"text":"Institut für Mineralogie, Westfälische Wilhelms-Universität Münster","active":true,"usgs":false}],"preferred":false,"id":861680,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Genske, F.","contributorId":300991,"corporation":false,"usgs":false,"family":"Genske","given":"F.","email":"","affiliations":[{"id":65268,"text":"Institut für Mineralogie, Westfälische Wilhelms-Universität Münster","active":true,"usgs":false}],"preferred":false,"id":861681,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Béguelin, P.","contributorId":300992,"corporation":false,"usgs":false,"family":"Béguelin","given":"P.","affiliations":[{"id":65268,"text":"Institut für Mineralogie, Westfälische Wilhelms-Universität Münster","active":true,"usgs":false}],"preferred":false,"id":861682,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Todd, Erin 0000-0002-4871-9730 etodd@usgs.gov","orcid":"https://orcid.org/0000-0002-4871-9730","contributorId":202811,"corporation":false,"usgs":true,"family":"Todd","given":"Erin","email":"etodd@usgs.gov","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":861683,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237223,"text":"70237223 - 2022 - Puget Sound Spatially Referenced Regression on Watershed Attributes (SPARROW)","interactions":[],"lastModifiedDate":"2026-03-18T15:16:45.794674","indexId":"70237223","displayToPublicDate":"2022-10-01T10:07:50","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":23618,"text":"Quality Assurance Project Plan","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"22-03-109","title":"Puget Sound Spatially Referenced Regression on Watershed Attributes (SPARROW)","docAbstract":"The United States Geological Survey (USGS) and the Washington State Department of Ecology (Ecology) are collaborating on the development of refined, seasonal load estimates of total nitrogen and total phosphorus within watersheds draining to Washington waters of the Salish Sea for the period 2005-2020. The modeling approach for this work is based on SPARROW (Spatially Referenced Regression on Watershed Attributes), a watershed modeling technique developed by the USGS. SPARROW is typically used to estimate stream loads throughout a stream network.
The estimated loads will be used within the context of the Puget Sound Nutrient Source Reduction Project to evaluate the influence of watershed contributions of nutrients throughout the stream network and to marine waters. This quality assurance project plan (QAPP) contains details about the technical approach, observational data, spatial and temporal source data, limitations, and quality assurance procedures that will be employed to develop the SPARROW models so that they can be used to inform additional actions to address excess nutrients.
","language":"English","publisher":"Washington State Department of Ecology (Ecology)","usgsCitation":"Figueroa-Kaminsky, C., Wasielewski, J., McCarthy, S., Schmadel, N., Wise, D., Johnson, Z., and Black, R.W., 2022, Puget Sound Spatially Referenced Regression on Watershed Attributes (SPARROW): Quality Assurance Project Plan 22-03-109, 76 p.","productDescription":"76 p.","ipdsId":"IP-143204","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":501247,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501246,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://apps.ecology.wa.gov/publications/SummaryPages/2203109.html"}],"country":"United States","state":"Washington","otherGeospatial":"Puget Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.52217372194855,\n 48.967370797762754\n ],\n [\n -123.88225129810027,\n 48.967370797762754\n ],\n [\n -123.88225129810027,\n 45.85254256141661\n ],\n [\n -120.52217372194855,\n 45.85254256141661\n ],\n [\n -120.52217372194855,\n 48.967370797762754\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Figueroa-Kaminsky, Cristiana","contributorId":350514,"corporation":false,"usgs":false,"family":"Figueroa-Kaminsky","given":"Cristiana","affiliations":[{"id":25353,"text":"Washington State Department of Ecology","active":true,"usgs":false}],"preferred":false,"id":957136,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wasielewski, Jamie K. 0009-0005-7497-3344","orcid":"https://orcid.org/0009-0005-7497-3344","contributorId":344993,"corporation":false,"usgs":false,"family":"Wasielewski","given":"Jamie K.","affiliations":[{"id":82458,"text":"Washington Dept. of Ecology","active":true,"usgs":false}],"preferred":false,"id":957137,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCarthy, Sheelagh","contributorId":367235,"corporation":false,"usgs":false,"family":"McCarthy","given":"Sheelagh","affiliations":[],"preferred":false,"id":957138,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmadel, Noah 0000-0002-2046-1694","orcid":"https://orcid.org/0000-0002-2046-1694","contributorId":219105,"corporation":false,"usgs":true,"family":"Schmadel","given":"Noah","email":"","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":957139,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wise, Daniel 0000-0002-1215-9612","orcid":"https://orcid.org/0000-0002-1215-9612","contributorId":217259,"corporation":false,"usgs":true,"family":"Wise","given":"Daniel","email":"","affiliations":[],"preferred":true,"id":957140,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Zachary 0000-0002-0149-5223 zjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-0149-5223","contributorId":190399,"corporation":false,"usgs":true,"family":"Johnson","given":"Zachary","email":"zjohnson@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":957141,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Black, Robert W. 0000-0002-4748-8213 rwblack@usgs.gov","orcid":"https://orcid.org/0000-0002-4748-8213","contributorId":1820,"corporation":false,"usgs":true,"family":"Black","given":"Robert","email":"rwblack@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853672,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237944,"text":"70237944 - 2022 - Using Landsat and MODIS satellite collections to examine extent, timing, and potential impacts of surface water inundation in California croplands☆","interactions":[],"lastModifiedDate":"2022-11-01T12:04:00.80791","indexId":"70237944","displayToPublicDate":"2022-10-01T06:59:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5098,"text":"Remote Sensing Applications: Society and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Using Landsat and MODIS satellite collections to examine extent, timing, and potential impacts of surface water inundation in California croplands☆","docAbstract":"The state of California, United States of America produces many crop products that are both utilized domestically and exported throughout the world. With nearly 39,000 km2 of croplands, monitoring unintentional and intentional surface water inundation is important for water resource management and flood hazard readiness. We examine inundation dynamics in California croplands from 2003 to 2020 by intersecting monthly surface water maps (n = 216 months) derived using two satellite remote sensing platforms (Landsat and Moderate Resolution Imaging Spectroradiometer [MODIS]) with a high-quality cropland map generated by the California Department of Water Resources. Surface water maps were produced using the Dynamic Surface Water Extent model, in which satellite image pixels are classified into different levels of detection confidence. Our analysis focused on calculating monthly and annual occurrence of “high confidence” water for each satellite collection across eight cropland types and 58 counties. Results indicate that 49.9% (MODIS) to 56.4% (Landsat) of croplands were inundated at least once during the 18-year timespan. Rice crops, due to their unique need of consistent surface water and dominance as a crop type in CA, had the highest proportion of and mean annual inundation area, while citrus crops had the lowest. Mean monthly inundation patterns in most croplands followed California's precipitation patterns with high inundation during the winter and spring rainy season. At the county level, croplands in the southern Central Valley typically had high occurrences of inundation in conjunction with large crop areas. Exposure and sensitivity of inundation for three crop types (citrus, deciduous, and vineyards) that are typically less associated with intentional inundation were geographically variable, but overall were generally highest in counties in the southern Central Valley, California's primary agricultural region. Flood and precipitation related crop insurance claims indicated that rice had the highest mean indemnity payout for any month with damages typically occurring in March and April. Insurance claims were also high in deciduous fruit and nut crops, which had peak damages in February. A comparison between inundation results and insurance claims suggests that the inundation mapped by our process coincides with claim activity. These data elucidate water inundation patterns across the state that can serve to inform farmers, insurers, decision makers, resource managers, and flood mitigation professionals.
Chloride deicers have been applied by the Oregon Department of Transportation (ODOT) to Interstate Route 5 (I–5) from the Oregon-California border north to mile marker 10 for several years in the high-elevation area known as the Siskiyou Pass. Magnesium chloride (MgCl2) and sodium chloride (NaCl) are applied to keep the interstate highway safe for drivers and allow for efficient transport of goods and people through adverse weather conditions, particularly snow and ice. The U.S. Geological Survey entered into a cooperative agreement with ODOT to research the effects of chloride deicers in the Carter and Wall Creek watersheds that drain the vicinity of the Siskiyou Pass.
The Stochastic Empirical Loading and Dilution Model (SELDM) was used to estimate combinations of prestorm-streamflow, stormflow, highway-runoff, and event mean constituent concentrations (EMCs), as well as stormwater-constituent loads at sites of interest. The study evaluated the effects of roadway application of chloride deicers on downstream and highway-runoff conditions (particularly EMCs), exceedance rates of criterion maximum concentrations, and concurrent runoff loads of stormwater constituents from a site of interest. SELDM was also used to evaluate the efficiency of hydrograph extension best management practices to reduce peak constituent concentrations. Several SELDM scenarios were developed as sensitivity analyses to evaluate the model benefit of collecting specific local sets of data, such as streamflow, precipitation, highway-runoff and riverine water-quality samples, and volumetric runoff coefficient statistics.
Results of the study showed that for SELDM modeling in the Siskiyou Pass area, (1) the inclusion of local streamflow data is important for obtaining accurate downstream EMCs, (2) the inclusion of precipitation data is important for highway and concurrent runoff load calculations, and (3) water-quality constituent EMC data from highway runoff and upstream stormflows are the most important data to collect for highway runoff and upstream water-quality constituent concentration statistics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225091","collaboration":"Prepared in cooperation with the Oregon Department of Transportation","usgsCitation":"Stonewall, A.J., Yates, M.C., and Granato, G.E., 2022, Assessing the impact of chloride deicer application in the Siskiyou Pass, southern Oregon: U.S. Geological Survey Scientific Investigations Report 2022–5091, 94 p., https://doi.org/10.3133/sir20225091.","productDescription":"Report: xii, 93 p.; Data Release; 4 Tables","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-107308","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":407662,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091_table_3.csv","text":"Table 3","size":"13 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5091 Table 3"},{"id":407660,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5091/coverthb.jpg"},{"id":407661,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5091"},{"id":407666,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091_table_17.csv","text":"Table 17","size":"2 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5091 Table 17"},{"id":407668,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091_table_26.csv","text":"Table 26","size":"4 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5091 Table 26"},{"id":407670,"rank":9,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5091/images"},{"id":407671,"rank":10,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091.XML"},{"id":407664,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091_table_10.csv","text":"Table 10","size":"2 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5091 Table 10"},{"id":407673,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q1PP61","text":"USGS data release","description":"USGS data release","linkHelpText":"Stochastic Empirical Loading and Dilution Model (SELDM) model archive and instructions for the Siskiyou Pass, Oregon"},{"id":407675,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091_tables3_10_17_26_csv.zip","text":"Tables 3, 10, 17, and 26 CSV files","size":"7 KB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2022-5091 Tables 3, 10, 17, and 26"},{"id":407674,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091_tables3_10_17_26.xlsx","text":"Tables 3, 10, 17, and 26 Excel file","size":"36 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5091 Tables 3, 10, 17, and 26"}],"country":"United States","state":"Oregon","otherGeospatial":"Siskiyou Pass","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.32,\n 42.02\n ],\n [\n -122.32,\n 42.02\n ],\n [\n -122.32,\n 42.02\n ],\n [\n -122.40,\n 42.08\n ],\n [\n -122.40,\n 42.08\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
In 2021, the U.S. Geological Survey (USGS) focused adult salmon survey efforts in the Snake River on deepwater redd searches and fish collection for parentage-based tagging (PBT) analyses. We use used a boat-mounted underwater video camera to count 93 deepwater redds at 17 of the 28 sites surveyed. Redd depths averaged 3.9 m. In conjunction with the Idaho Power Company, we collected genetic samples from 346 live fall Chinook salmon (Oncorhynchus tshawytscha) and 15 carcasses at 48 unique geographic locations that spanned 90 river kilometers. Seventy-two fish were recovered at Eureka Bar (river kilometer [rkm] 307.1) and High Range (rkm 332.3), which accounted for 20% of all collected fish in 2021. Most (291 fish) post-spawned salmon were collected during the first two weeks of November around the peak of spawning. A summary of 2021 PBT results produced by the Idaho Power Company can be found in Appendix A.2.
Beach seining and PIT tagging of subyearling fall Chinook salmon was conducted in Snake and Salmon rivers to obtain information on population metrics and growth as well as to provide data for ongoing life-cycle modeling. In the Snake River, we collected 13,710 subyearlings, tagged 6,299, and recaptured 981 (15.6%). Using 8-mm tags in 45–49-mm fish allowed us to represent an additional 40% of the juvenile population through PIT tagging beyond just using standard 9- and 12-mm tags. In the Salmon River, we captured 103 natural subyearlings with the majority (60%) of fish being captured at two sites: rkm 11 and 20. We tagged 27 subyearlings, and no fish were recaptured. In Lower Granite Reservoir, we captured 4,887 subyearlings, PIT tagged 2,585, and recaptured 312.
Many of the subyearlings we tagged in the Snake River were detected passing Lower Granite Dam, but no fish tagged in the Salmon River were detected. In total we detected 673 (7.6%) tagged fish at Lower Granite Dam, and detection rates varied by tag size and passage route. More subyearlings were detected passing via the removable spill weir (RSW) earlier in the season while more fish were detected passing through the juvenile fish bypass system (JBS) later in the season. In general, fish tagged with 12-mm PIT tags had higher detection rates than fish tagged with smaller tags. Survival to Lower Granite Dam was low and ranged from 0.195 to 0.228. Growth of subyearlings was similar between riverine and reservoir reaches but was slightly lower in Lower Granite Reservoir. Only 19 subyearlings were recaptured at Lower Granite Dam in early autumn that predominantly originated from the Clearwater River (1 fish was from the Snake River). Mean standard deviation (SD) fork length and mass growth rates were 0.92±0.10 mm/d and 0.06±0.01 g/g/d, respectively.
","language":"English","publisher":"Bonneville Power Administration","usgsCitation":"2022, Snake River fall Chinook salmon research and monitoring, 66 p.","productDescription":"66 p.","ipdsId":"IP-146145","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":419248,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":419239,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Idaho, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.38976363947546,\n 46.95039331929084\n ],\n [\n -117.38976363947546,\n 45.32672178879673\n ],\n [\n -116.27624197937624,\n 45.32672178879673\n ],\n [\n -116.27624197937624,\n 46.95039331929084\n ],\n [\n -117.38976363947546,\n 46.95039331929084\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Tiffan, Kenneth F. 0000-0002-5831-2846 ktiffan@usgs.gov","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":3200,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","email":"ktiffan@usgs.gov","middleInitial":"F.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":878877,"contributorType":{"id":2,"text":"Editors"},"rank":1}]}} ,{"id":70237236,"text":"70237236 - 2022 - Modeling protected species distributions and habitats to inform siting and management of pioneering ocean industries: A case study for Gulf of Mexico aquaculture","interactions":[],"lastModifiedDate":"2022-10-05T13:55:41.861651","indexId":"70237236","displayToPublicDate":"2022-09-30T08:50:51","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Modeling protected species distributions and habitats to inform siting and management of pioneering ocean industries: A case study for Gulf of Mexico aquaculture","docAbstract":"Marine Spatial Planning (MSP) provides a process that uses spatial data and models to evaluate environmental, social, economic, cultural, and management trade-offs when siting (i.e., strategically locating) ocean industries. Aquaculture is the fastest-growing food sector in the world. The United States (U.S.) has substantial opportunity for offshore aquaculture development given the size of its exclusive economic zone, habitat diversity, and variety of candidate species for cultivation. However, promising aquaculture areas overlap many protected species habitats. Aquaculture siting surveys, construction, operations, and decommissioning can alter protected species habitat and behavior. Additionally, aquaculture-associated vessel activity, underwater noise, and physical interactions between protected species and farms can increase the risk of injury and mortality. In 2020, the U.S. Gulf of Mexico was identified as one of the first regions to be evaluated for offshore aquaculture opportunities as directed by a Presidential Executive Order. We developed a transparent and repeatable method to identify aquaculture opportunity areas (AOAs) with the least conflict with protected species. First, we developed a generalized scoring approach for protected species that captures their vulnerability to adverse effects from anthropogenic activities using conservation status and demographic information. Next, we applied this approach to data layers for eight species listed under the Endangered Species Act, including five species of sea turtles, Rice’s whale, smalltooth sawfish, and giant manta ray. Next, we evaluated four methods for mathematically combining scores (i.e., Arithmetic mean, Geometric mean, Product, Lowest Scoring layer) to generate a combined protected species data layer. The Product approach provided the most logical ordering of, and the greatest contrast in, site suitability scores. Finally, we integrated the combined protected species data layer into a multi-criteria decision-making modeling framework for MSP. This process identified AOAs with reduced potential for protected species conflict. These modeling methods are transferable to other regions, to other sensitive or protected species, and for spatial planning for other ocean-uses.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0267333","usgsCitation":"Farmer, N.A., Powell, J.R., Morris, J.A., Soldevilla, M.S., Wickliffe, L.C., Jossart, J.A., MacKay, J.K., Randall, A.L., Bath, G.E., Ruvelas, P., Gray, L., Lee, J., Piniak, W., Garrison, L., Hardy, R., Hart, K., Sasso, C., Stokes, L., and Riley, K.L., 2022, Modeling protected species distributions and habitats to inform siting and management of pioneering ocean industries: A case study for Gulf of Mexico aquaculture: PLoS ONE, v. 17, no. 9, e0267333, 29 p., https://doi.org/10.1371/journal.pone.0267333.","productDescription":"e0267333, 29 p.","ipdsId":"IP-139636","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":446276,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0267333","text":"Publisher Index 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variability","docAbstract":"The Miocene Climatic Optimum (MCO) provides important insights into how the climate system operates under elevated temperatures and atmospheric CO2 levels. Few western North Atlantic paleotemperature or paleoecological records exist from the MCO, despite their importance for understanding both regional and global climate dynamics. Here we present quantitative MCO paleoecological data from the western North Atlantic, specifically from the Baltimore Gas & Electric (BG&E) marine sediment core from southern Maryland, USA. We examine alkenones and planktic foraminifera and document the first sea surface temperature (SST) and productivity estimates for the MCO and the Middle Miocene Climate Transition (MMCT) from the continental shelf. Increased levels of planktic foraminifer species diversity and surface productivity accompany high sea level intervals of the MCO, indicating coastal upwelling. Cooling episodes correlate to unconformities in the BG&E core that reflect sea level lowstands; these and sedimentary cycles tie the record to eccentricity-paced Antarctic ice sheet growth and decay. This dynamic record not only captures the variability in SST, sea level and coastal productivity during the warm MCO and the transition to cooler global temperatures during the MMCT, but it also demonstrates the variability in local conditions within and between intervals of high sea level.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.palaeo.2022.111249","usgsCitation":"Robinson, M., Dowsett, H., and Herbert, T.D., 2022, Very high Middle Miocene surface productivity on the U.S. mid-Atlantic shelf amid glacioeustatic sea level variability: Palaeogeography, Palaeoclimatology, Palaeoecology, v. 606, 111249, 8 p., https://doi.org/10.1016/j.palaeo.2022.111249.","productDescription":"111249, 8 p.","ipdsId":"IP-134945","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":419149,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Calvert Cliffs outcrops, North Atlantic Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.44217013377451,\n 38.43620755767674\n ],\n [\n -76.44217013377451,\n 38.37747005174839\n ],\n [\n -76.37931582397617,\n 38.37747005174839\n ],\n [\n -76.37931582397617,\n 38.43620755767674\n ],\n [\n -76.44217013377451,\n 38.43620755767674\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -25.880283258156595,\n 58.581436910456176\n ],\n [\n -25.880283258156595,\n 47.08269537606205\n ],\n [\n -10.390328859982219,\n 47.08269537606205\n ],\n [\n -10.390328859982219,\n 58.581436910456176\n ],\n [\n -25.880283258156595,\n 58.581436910456176\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"606","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Robinson, Marci M. 0000-0002-9200-4097","orcid":"https://orcid.org/0000-0002-9200-4097","contributorId":316788,"corporation":false,"usgs":true,"family":"Robinson","given":"Marci M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":878298,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dowsett, Harry J. 0000-0003-1983-7524","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":316789,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":878299,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herbert, Timothy D.","contributorId":192841,"corporation":false,"usgs":false,"family":"Herbert","given":"Timothy","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":878300,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70259193,"text":"70259193 - 2022 - Geologic models underpinning the 2018 US Geological Survey assessment of hydrocarbon resources in the Eagle Ford Group and associated Cenomanian–Turonian strata, United States Gulf Coast, Texas","interactions":[],"lastModifiedDate":"2024-10-01T11:59:33.435963","indexId":"70259193","displayToPublicDate":"2022-09-30T06:57:23","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":605,"text":"AAPG Bulletin","printIssn":"0149-1423","active":true,"publicationSubtype":{"id":10}},"title":"Geologic models underpinning the 2018 US Geological Survey assessment of hydrocarbon resources in the Eagle Ford Group and associated Cenomanian–Turonian strata, United States Gulf Coast, Texas","docAbstract":"The availability of new geologic and production data has greatly increased since 2010, when the US Geological Survey (USGS) last assessed undiscovered, technically recoverable oil and gas resources in the Cenomanian–Turonian (CT) Eagle Ford Group (EFG) across Texas. This new information facilitated an updated assessment of undiscovered continuous oil and gas resources in the Eagle Ford and associated CT strata.
Literature and USGS research data were used to build the geologic models for the assessment units (AUs). The USGS defined six continuous AUs within the EFG: (1) Eagle Ford Marl Continuous Oil, (2) Eagle Ford Marl Continuous Gas, (3) Submarine Plateau-Karnes Trough Continuous Oil, (4) Submarine Plateau-Karnes Trough Continuous Gas, (5) CT Mudstone Continuous Oil, and (6) CT Mudstone Continuous Gas. An additional AU, the CT Slope Continuous Gas AU, was defined but not quantitatively assessed. The boundaries of these AUs were defined by thickness, lithofacies, thermal maturity, regional geology, and the spatial distribution of productive fairways.
The resulting total mean estimates for undiscovered, technically recoverable resources for these six AUs are 8.5 billion bbl of oil and 66 trillion ft3 of gas. These results for both oil and gas resources are within the top five volumes of previously assessed continuous accumulations in the United States and attest to the importance of the EFG and associated CT strata as a significant source of petroleum well into the future.
","language":"English","publisher":"American Association of Petroleum Geologists (AAPG)","doi":"10.1306/02072218153","usgsCitation":"Whidden, K.J., Pitman, J., Leathers-Miller, H., Pearson, O.N., Gianoutsos, N.J., Kinney, S.A., Birdwell, J.E., Paxton, S.T., Burke, L.A., and Dubiel, R., 2022, Geologic models underpinning the 2018 US Geological Survey assessment of hydrocarbon resources in the Eagle Ford Group and associated Cenomanian–Turonian strata, United States Gulf Coast, Texas: AAPG Bulletin, v. 106, no. 8, p. 1625-1652, https://doi.org/10.1306/02072218153.","productDescription":"28 p.","startPage":"1625","endPage":"1652","ipdsId":"IP-097639","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":462434,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Gulf Coast","geographicExtents":"{\n \"type\": 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Numerical models provide a physics-based framework for assimilating and making sense of information that by itself only provides a limited description of the hydrologic system. Often, numerical models are the best option for quantifying even intuitively obvious connections between human activities and water resource impacts. However, despite many recent advances in model data assimilation and uncertainty quantification, the process of constructing numerical models remains laborious, expensive, and opaque, often precluding their use in decision making. Modflow-setup aims to provide rapid and consistent construction of MODFLOW groundwater models through robust and repeatable automation. Common model construction tasks are distilled in an open-source, online code base that is tested and extensible through collaborative version control. Input to Modflow-setup consists of a single configuration file that summarizes the workflow for building a model, including source data, construction options, and output packages. Source data providing model structure and parameter information including shapefiles, rasters, NetCDF files, tables, and other (geolocated) sources to MODFLOW models are read in and mapped to the model discretization, using Flopy and other general open-source scientific Python libraries. In a few minutes, an external array-based MODFLOW model amenable to parameter estimation and uncertainty quantification is produced. This paper describes the core functionality of Modflow-setup, including a worked example of a MODFLOW 6 model for evaluating pumping impacts to a lake in central Wisconsin, United States.
Landsat 9 (L9) was launched on September 27, 2021, from Vandenberg Space Force Base in California. The U. S. Geological Survey (USGS) released Level-1 data, geometrically orthorectified and radiometrically calibrated imagery in digital numbers that can be scaled to Top-of-Atmosphere reflectance, and Level-2 data, geometrically orthorectified and radiometrically calibrated surface reflectance imagery, to the public on February 10, 2022. From September 27, 2021 to early January of 2022, the satellite and its two instruments, the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS), were in their commissioning phase, updating key radiometric and geometric calibration parameters for both the spacecraft and the instruments. The data acquired during the commissioning phase of the spacecraft and instruments were reprocessed with the newly determined post-launch calibration parameters prior to the releasing of the data to the public. After the public release of the data, the calibration parameters of the sensors and the spacecraft continue to be monitored to ensure the data released to the public is of the same high quality as previous Landsat data products. This paper discusses three key geometric performance aspects of the L9 spacecraft and its instruments during its early mission time frame (September 27, 2021 to June 27, 2022) including geodetic accuracy, geometric accuracy, and within band registration accuracy of the L9 products generated. |
The major streams in Oklahoma, and the alluvial aquifers associated with those major streams, are important resources for the 39 federally recognized Tribes in Oklahoma. Many Tribal Governments are interested in developing water-management plans (hereinafter referred to as “water plans”) to preserve water resources for the future. This report provides a general overview of the types of information and data needed to prepare comprehensive water plans. To assist Tribes in the development of water plans, the U.S. Geological Survey, in cooperation with the Bureau of Indian Affairs, has outlined the steps necessary for creation of such plans.
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U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501
Digital water-surface profile maps for a 14-mile reach of the Mississippi River near Prairie Island, Minnesota, from the confluence of the St. Croix River at Prescott, Wisconsin, to upstream from the U.S. Army Corps of Engineers Lock and Dam No. 3 (U.S. Army Corps of Engineers National Inventory of Dams number MN00595) in Welch, Minnesota, were created by the U.S. Geological Survey (USGS) in cooperation with the Prairie Island Indian Community in the State of Minnesota. The water-surface profile maps depict estimates of the areal extent and depth of water inundation corresponding to selected water levels (stages) at the USGS streamgage Mississippi River at Prescott, Wisconsin (USGS station number 05344500). Current conditions for estimating near-real-time areas of inundation by use of USGS streamgage information may be obtained from the National Water Information System on the internet at https://doi.org/10.5066/F7P55KJN.
In this study, water-surface profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated by using the most current stage-discharge relations at the USGS streamgage Mississippi River at Prescott, Wisconsin (USGS station number 05344500). The hydraulic model was then used to determine four water-surface profiles for stream stages at 37.00, 39.00, 40.00, and 41.00 feet (ft) referenced to the streamgage datum and ranging from bankfull to approximately 2 ft below the highest recorded stage at the streamgage, which is 43.11 ft. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.35-ft vertical and 1.97-ft root mean square error horizontal resolution) in order to delineate the area inundated at each stage.
The combination of maps provided in this report and real-time stage data from the upstream USGS streamgage provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for post flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215018","collaboration":"Prepared in cooperation with the Prairie Island Indian Community in the State of Minnesota","usgsCitation":"Krall, A.L., and Prokopec, J.G., 2022, Water-surface profile maps for the Mississippi River near Prairie Island, Minnesota, 2019: U.S. Geological Survey Scientific Investigations Report 2021–5018, 11 p., https://doi.org/10.3133/sir20215018.","productDescription":"Report: vii, 11 p.; Data Release; Dataset","numberOfPages":"24","onlineOnly":"Y","ipdsId":"IP-112181","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":407610,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":407609,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BO105J","text":"USGS data release","linkHelpText":"Water-surface profile map files for the Mississippi River near Prairie Island, Welch, Minnesota, 2019"},{"id":407606,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5018/sir20215018.XML"},{"id":407608,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5018/images"},{"id":407604,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5018/coverthb.jpg"},{"id":407605,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5018/sir20215018.pdf","text":"Report","size":"3.93 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5018"}],"country":"United States","state":"Minnesota","otherGeospatial":"Prairie Island, Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.81524658203125,\n 44.739417680004166\n ],\n [\n -92.82691955566405,\n 44.73307677899358\n ],\n [\n -92.82966613769531,\n 44.72185655851067\n ],\n [\n -92.81524658203125,\n 44.70380207177485\n ],\n [\n -92.779541015625,\n 44.66914120165374\n ],\n [\n -92.72323608398438,\n 44.62566377574352\n ],\n [\n -92.66006469726562,\n 44.595356872562235\n ],\n [\n -92.5982666015625,\n 44.583131873047186\n ],\n [\n -92.5653076171875,\n 44.59046718130883\n ],\n [\n -92.55638122558594,\n 44.62615246716885\n ],\n [\n -92.603759765625,\n 44.67207109153918\n ],\n [\n -92.713623046875,\n 44.73746670759647\n ],\n [\n -92.801513671875,\n 44.75453548416007\n ],\n [\n -92.81524658203125,\n 44.739417680004166\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Through the collection and analysis of shallow and deep subseafloor sediments, rocks, fluids, and life, scientific ocean drilling has enriched our understanding of the complex Earth system. Among other achievements, scientific ocean drilling has documented the history of Earth’s climate, the waxing and waning of polar ice sheets, the past changes in ocean and atmospheric circulation, the existence and function of microbial life in the subseafloor, the compositional variations in Earth’s crust and underlying mantle, and the physical and chemical processes acting at subduction zones, including those associated with tsunamigenic earthquakes. Over the decades, more than 12,000 articles that depend on analyses of scientific ocean drilling samples and geophysical data have been published, many detailing breakthrough contributions to global knowledge about the Earth system. Approximately 45% of these publications were led by U.S.-affiliated authors (International Ocean Discovery Program Publication Services, 2021).
Since the mid-1980s, the workhorse of this multidisciplinary, international research effort has been the riserless D/V JOIDES Resolution, operated by Texas A&M University with funding from the U.S. National Science Foundation (NSF). D/V JOIDES Resolution has conducted the vast majority of scientific ocean drilling expeditions and collected most of the scientific cores over that period, including 82% of the expeditions and 93% of the cores in the last decade alone, despite being one of three platforms that is operated within the International Ocean Discovery Program. However, D/V JOIDES Resolution is approaching the end of its useful life.
With a strong commitment to continue scientific ocean drilling beyond the end of the current phase, the community developed a document outlining the research frontiers that should be pursued. Exploring Earth by Scientific Ocean Drilling: 2050 Science Framework (Koppers and Coggon, 2020) describes seven scientific strategic objectives that focus on understanding interconnections within the Earth system and five flagship initiatives that integrate these objectives into long-term research efforts that address issues facing society. Additional elements in the 2050 Science Framework, including STEM education, workforce development, technology development, and innovative applications of data analytics, will advance the goals of scientific ocean drilling. Addressing the 2050 Science Framework also requires building partnerships with allied U.S. and international science programs and strengthening existing ones.
To implement a significant portion of the 2050 Science Framework, the U.S. scientific community seeks to lease or acquire a newly built, globally ranging, state-of-the art, riserless drilling vessel. The many and varied technical and human resources requirements for successful accomplishment of scientific and educational goals summarized in this document and described in detail in the 2050 Science Framework require broad community input and careful consideration.
Following receipt of NSF’s formal Request for Assistance to the United States Science Support Program (USSSP), the U.S. scientific ocean drilling community conducted a one-year exercise to identify its national scientific needs and priorities in order to determine the Science Mission Requirements (SMRs) presented here. This community effort included:
(1) a U.S. community-wide survey to identify the specific operational and technical capabilities critical to addressing science in the 2050 Science Framework;
(2) a series of online workshops focusing on critical capabilities identified by the survey; and
(3) a large in-person workshop to synthesize the results of the survey and the virtual workshops (Appendix 1). The approach was designed to reach as many participants as possible. Overall, 278 survey responses were received from U.S. community members, representing 104 unique institutions from 39 states and the District of Columbia, and 137 unique individuals participated in the workshops (Appendix 2).
The results of this effort comprise two classes of SMRs: Foundational Science Mission Requirements and Primary Science Mission Requirements. Foundational SMRs define minimum criteria for a new riserless drilling vessel that can address significant portions of the 2050 Science Framework. Primary SMRs build upon the Foundational SMRs and will create more robust science opportunities and data collection capabilities, will increase progress in addressing the 2050 Science Framework objectives, and will provide more real-time ship-to-shore interaction to improve science productivity, engagement, and outreach.
Primary Science Mission Requirements include:
NSF’s investment in a new globally ranging, riserless drilling vessel will have a powerful economic multiplier effect, including the infusion of additional science support funds in the United States for training and research, the development of new technologies and tools, and the associated scientific and technical workforce development. The skills and knowledge gained through scientific ocean drilling are translatable to careers in fields such as sustainable energy development (e.g., geothermal and offshore wind), carbon sequestration, data management and cyberinfrastructure, biotechnology, communications, science education, policy, hazard mitigation, and environmental management.
The United States is a leader in a well-established and internationally collaborative scientific ocean drilling community. A modern, globally ranging, riserless drilling vessel will allow the United States to expand its leadership position, address broad scientific questions that current capabilities preclude, and cultivate equitable international, multidisciplinary collaborations that will ensure scientific ocean drilling’s future success.
","language":"English","publisher":"United States Science Support Program (USSSP), U.S. Scientific Ocean Drilling","usgsCitation":"Carr, S., Collett, T., Dodd, J.P., Fryer, P., Fulton, P., Gulick, S., Kitajima, H., Koppers, A.A., Marcks, B., Miller, D.J., Rosenthal, Y., Slagle, A., Tominaga, M., Torres, M.E., and Wellner, J., 2022, Science mission requirements for a globally ranging, riserless drilling vessel for U.S. Scientific Ocean Drilling, 26 p.","productDescription":"26 p.","ipdsId":"IP-144494","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":422238,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":422216,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://usoceandiscovery.org/smr/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Robinson, Rebecca S.","contributorId":6688,"corporation":false,"usgs":true,"family":"Robinson","given":"Rebecca","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":887153,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Dugan, Brandon","contributorId":10213,"corporation":false,"usgs":true,"family":"Dugan","given":"Brandon","email":"","affiliations":[],"preferred":false,"id":887154,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Brenner, Carl","contributorId":331260,"corporation":false,"usgs":false,"family":"Brenner","given":"Carl","email":"","affiliations":[],"preferred":false,"id":887155,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Krissek, Lawrence","contributorId":194498,"corporation":false,"usgs":false,"family":"Krissek","given":"Lawrence","email":"","affiliations":[],"preferred":false,"id":887156,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Carr, Stephanie A","contributorId":189201,"corporation":false,"usgs":false,"family":"Carr","given":"Stephanie A","affiliations":[],"preferred":false,"id":887063,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collett, Timothy 0000-0002-7598-4708","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":220806,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":887064,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dodd, Justin P.","contributorId":209767,"corporation":false,"usgs":false,"family":"Dodd","given":"Justin","email":"","middleInitial":"P.","affiliations":[{"id":13666,"text":"Northern Illinois University","active":true,"usgs":false}],"preferred":false,"id":887065,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fryer, Patricia","contributorId":298539,"corporation":false,"usgs":false,"family":"Fryer","given":"Patricia","email":"","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":887066,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fulton, Patrick","contributorId":34832,"corporation":false,"usgs":true,"family":"Fulton","given":"Patrick","email":"","affiliations":[],"preferred":false,"id":887152,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gulick, Sean P. S.","contributorId":147201,"corporation":false,"usgs":false,"family":"Gulick","given":"Sean P. S.","affiliations":[{"id":13603,"text":"University of Texas, Austin","active":true,"usgs":false}],"preferred":false,"id":887067,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kitajima, Hiroko","contributorId":270795,"corporation":false,"usgs":false,"family":"Kitajima","given":"Hiroko","email":"","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":887068,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Koppers, Anthony A.P. 0000-0002-8136-5372","orcid":"https://orcid.org/0000-0002-8136-5372","contributorId":222435,"corporation":false,"usgs":false,"family":"Koppers","given":"Anthony","email":"","middleInitial":"A.P.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":887069,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Marcks, Basia","contributorId":331243,"corporation":false,"usgs":false,"family":"Marcks","given":"Basia","email":"","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":887070,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Miller, D. Jay","contributorId":67644,"corporation":false,"usgs":false,"family":"Miller","given":"D.","email":"","middleInitial":"Jay","affiliations":[],"preferred":false,"id":887071,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rosenthal, Yair 0000-0002-7546-6011","orcid":"https://orcid.org/0000-0002-7546-6011","contributorId":260126,"corporation":false,"usgs":false,"family":"Rosenthal","given":"Yair","email":"","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":887072,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Slagle, Angela","contributorId":331244,"corporation":false,"usgs":false,"family":"Slagle","given":"Angela","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":887073,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Tominaga, Masako 0000-0002-1169-4146","orcid":"https://orcid.org/0000-0002-1169-4146","contributorId":200937,"corporation":false,"usgs":false,"family":"Tominaga","given":"Masako","email":"","affiliations":[],"preferred":false,"id":887074,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Torres, Marta E.","contributorId":196035,"corporation":false,"usgs":false,"family":"Torres","given":"Marta","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":887075,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Wellner, Julia","contributorId":331245,"corporation":false,"usgs":false,"family":"Wellner","given":"Julia","email":"","affiliations":[{"id":36391,"text":"University of Houston","active":true,"usgs":false}],"preferred":false,"id":887076,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70243126,"text":"70243126 - 2022 - Defining the timing, extent, and conditions of Paleozoic metamorphism in the southern Appalachian Blue Ridge terranes of Tennessee, North Carolina, and northern Georgia","interactions":[],"lastModifiedDate":"2023-05-01T13:24:29.11304","indexId":"70243126","displayToPublicDate":"2022-09-29T08:19:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3524,"text":"Tectonics","active":true,"publicationSubtype":{"id":10}},"title":"Defining the timing, extent, and conditions of Paleozoic metamorphism in the southern Appalachian Blue Ridge terranes of Tennessee, North Carolina, and northern Georgia","docAbstract":"The tectonometamorphic evolution of the southern Appalachians, which results from multiple Paleozoic orogenies (Taconic, Neoacadian, and Alleghanian), has lacked a consensus interpretation regarding its thermal-metamorphic history. The Blue Ridge terranes have remained the focus of the debate, with the interpreted timing of regional Barrovian metamorphism and associated deformation ranging from early (Taconic) to late Paleozoic (Alleghanian). New monazite U-Pb geochronology and thermobarometric data are integrated with previously reported geo- and thermochronology to delimit the Paleozoic thermal-metamorphic evolution of these terranes. Monazite compositional, textural, and U-Pb age systematics are remarkably consistent for all samples, yielding a single dominant age mode for each sample. The western, central, and eastern Blue Ridge terranes yield weighted mean monazite U-Pb ages of 450–441, 459–457, and 458–453 Ma, respectively. Thermodynamic modeling using mineral assemblages yields peak conditions of 600°C–650°C and 5.8–8.9 kbar for staurolite and kyanite grade western Blue Ridge units, including the stratigraphically youngest unit in the Murphy syncline, which also yields a weighted mean monazite U-Pb age of 441 Ma. The Taconic metamorphic core of the central Blue Ridge yields peak conditions of 775°C and ∼11.5 kbar. Combined, these ages indicate that the relatively intact Barrovian metamorphic progression mapped across the Blue Ridge of Tennessee, North Carolina, and northern Georgia is solely of Ordovician (Taconic) age. Synthesis of this new data with existing geo- and thermochronology support a model of Barrovian metamorphism resulting from construction of a Taconic accretionary wedge and subduction complex, followed by post-Taconic unroofing during Neoacadian and Alleghanian thrusting.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022TC007406","usgsCitation":"Thigpen, J.R., Moecher, D.P., Stowell, H.H., Merschat, A.J., Hatcher, R.D., Powell, N.E., Spencer, B.M., Mako, C.A., Bollen, E.M., and Kylander-Clark, A.R., 2022, Defining the timing, extent, and conditions of Paleozoic metamorphism in the southern Appalachian Blue Ridge terranes of Tennessee, North Carolina, and northern Georgia: Tectonics, v. 41, no. 10, e2022TC007406, 28 p., https://doi.org/10.1029/2022TC007406.","productDescription":"e2022TC007406, 28 p.","ipdsId":"IP-145448","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":416548,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia, North Carolina, Tennessee","otherGeospatial":"southern Appalachian Blue Ridge terranes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.77372589012849,\n 34.07019929846841\n ],\n [\n -82.80328432233892,\n 34.27121850429879\n ],\n [\n -81.50322391776882,\n 35.24927075561507\n ],\n [\n -80.41655982688857,\n 36.494074809618084\n ],\n [\n -82.97300068094341,\n 36.582906770830434\n ],\n [\n -85.85067880218081,\n 34.489221005195844\n ],\n [\n -85.24847064236673,\n 33.80004016867814\n ],\n [\n -84.77372589012849,\n 34.07019929846841\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"41","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Thigpen, J. Ryan","contributorId":173115,"corporation":false,"usgs":false,"family":"Thigpen","given":"J.","email":"","middleInitial":"Ryan","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":871179,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moecher, David P.","contributorId":304620,"corporation":false,"usgs":false,"family":"Moecher","given":"David","email":"","middleInitial":"P.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":871180,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stowell, Harold H.","contributorId":304621,"corporation":false,"usgs":false,"family":"Stowell","given":"Harold","email":"","middleInitial":"H.","affiliations":[{"id":36730,"text":"University of Alabama","active":true,"usgs":false}],"preferred":false,"id":871181,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Merschat, Arthur J. 0000-0002-9314-4067 amerschat@usgs.gov","orcid":"https://orcid.org/0000-0002-9314-4067","contributorId":4556,"corporation":false,"usgs":true,"family":"Merschat","given":"Arthur","email":"amerschat@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":871182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hatcher, Robert D. Jr.","contributorId":121402,"corporation":false,"usgs":true,"family":"Hatcher","given":"Robert","suffix":"Jr.","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":871183,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Powell, Nicholas Edwin 0000-0003-3654-8759","orcid":"https://orcid.org/0000-0003-3654-8759","contributorId":304622,"corporation":false,"usgs":true,"family":"Powell","given":"Nicholas","email":"","middleInitial":"Edwin","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":871184,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Spencer, Brandon M.","contributorId":304623,"corporation":false,"usgs":false,"family":"Spencer","given":"Brandon","email":"","middleInitial":"M.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":871185,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mako, Calvin A.","contributorId":304624,"corporation":false,"usgs":false,"family":"Mako","given":"Calvin","email":"","middleInitial":"A.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":871186,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bollen, Elizabeth M.","contributorId":304625,"corporation":false,"usgs":false,"family":"Bollen","given":"Elizabeth","email":"","middleInitial":"M.","affiliations":[{"id":36730,"text":"University of Alabama","active":true,"usgs":false}],"preferred":false,"id":871187,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kylander-Clark, Andrew R C","contributorId":269776,"corporation":false,"usgs":false,"family":"Kylander-Clark","given":"Andrew","email":"","middleInitial":"R C","affiliations":[{"id":27356,"text":"UC-Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":871188,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70238814,"text":"70238814 - 2022 - Considering behavioral state when predicting habitat use: Behavior-specific spatial models for the endangered Tasmanian wedge-tailed eagle","interactions":[],"lastModifiedDate":"2022-12-13T13:00:23.977736","indexId":"70238814","displayToPublicDate":"2022-09-29T06:55:51","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Considering behavioral state when predicting habitat use: Behavior-specific spatial models for the endangered Tasmanian wedge-tailed eagle","docAbstract":"Effective planning for species conservation often requires an understanding of habitat use. The resources an animal selects within the landscape relate to its behavioral state and, therefore, incorporating behavior into habitat selection analyses can help inform management of threatened species. Here we present an approach for developing behavior-specific spatial habitat-use models using large quantities of GPS telemetry data. Using hidden Markov models, we first characterize 231,478 GPS fixes from 22 recently fledged endangered Tasmanian wedge-tailed eagles (Aquila audax fleayi) as reflective of either perching, short-distance flight, or long-distance flight. We then use a multivariate habitat selection ratio to develop spatial models predicting where these behavioral states occur. Recently fledged Tasmanian wedge-tailed eagles selected for areas close to forest edges during perching and short distance flights, whereas they selected more strongly for areas with steep topography (slopes > 15°) and further from forest edges for longer flights. Models using distance to forest edge and topographic slope effectively predicted where eagles engaged in long flights (R2 > 0.91, rs > 0.90) in each of six regions, whereas the performance varied by region for models describing perching (R2 = 0.43–0.97, rs = 0.80–0.97) and short flights (R2 = 0.34–0.93, rs = 0.63–1.00). Our study provides a detailed understanding of habitat use by young Tasmanian wedge-tailed eagles, which has multiple applications in the ongoing conservation of the population. Our method illustrates a framework for spatially explicit and behavior-specific habitat selection analyses that can be applied to other species of conservation concern.