We examined data on white pine blister rust (blister rust) collected during the monitoring of whitebark pine trees in the Greater Yellowstone Ecosystem (from 2004-2015). Summaries of repeat observations performed by multiple independent observers are reviewed and discussed. These summaries show variability among observers and the potential for errors being made in blister rust status. Based on this assessment, we utilized occupancy models to analyze blister rust prevalence while explicitly accounting for imperfect detection. Available covariates were used to model both the probability of a tree being infected with blister rust and the probability of an observer detecting the infection. The fitted model provided strong evidence that the probability of blister rust infection increases as tree diameter increases and decreases as site elevation increases. Most importantly, we found evidence of heterogeneity in detection probabilities related to tree size and average slope of a transect. These results suggested that detecting the presence of blister rust was more difficult in larger trees. Also, there was evidence that blister rust was easier to detect on transects located on steeper slopes.
Our model accounted for potential impacts of observer experience on blister rust detection probabilities and also showed moderate variability among the different observers in their ability to detect blister rust. Based on these model results, we suggest that multiple observer sampling continue in future field seasons in order to allow blister rust prevalence estimates to be corrected for imperfect detection. We suggest that the multiple observer effort be spread out across many transects (instead of concentrated at a few each field season) while retaining the overall proportion of trees with multiple observers around 5-20%. Estimates of prevalence are confounded with detection unless it is explicitly accounted for in an analysis and we demonstrate how an occupancy model can be used to do account for this source of observation error.
","language":"English","publisher":"National Park Service","publisherLocation":"Fort Collins, CO","usgsCitation":"Wright, W.J., and Irvine, K.M., 2017, Assessment of imperfect detection of blister rust in whitebark pine within the Greater Yellowstone Ecosystem: Natural Resource Report 2017/1457, vi, 24 p.","productDescription":"vi, 24 p.","ipdsId":"IP-083274","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":342431,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":342427,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/DataStore/Reference/Profile/2240718"}],"country":"United States","state":"idaho, Montana, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.5,\n 41.918628865183045\n ],\n [\n -108.446044921875,\n 41.918628865183045\n ],\n [\n -108.446044921875,\n 45.75985868785574\n ],\n [\n -112.5,\n 45.75985868785574\n ],\n [\n -112.5,\n 41.918628865183045\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5940f9b0e4b0764e6c63ea9b","contributors":{"authors":[{"text":"Wright, Wilson J.","contributorId":192867,"corporation":false,"usgs":false,"family":"Wright","given":"Wilson","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":697959,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irvine, Kathryn M. 0000-0002-6426-940X kirvine@usgs.gov","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":2218,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","email":"kirvine@usgs.gov","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":697958,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70188460,"text":"70188460 - 2017 - Predation by Acanthurus leucopareius on black-band disease in Kauai, Hawaii","interactions":[],"lastModifiedDate":"2017-07-03T09:55:55","indexId":"70188460","displayToPublicDate":"2017-06-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1106,"text":"Bulletin of Marine Science","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Predation by Acanthurus leucopareius on black-band disease in Kauai, Hawaii","title":"Predation by Acanthurus leucopareius on black-band disease in Kauai, Hawaii","docAbstract":"No abstract available.
The goal of petrochronology is to extract information about the rates and conditions at which rocks and magmas are transported through the Earth’s crust. Garnering this information from the rock record greatly benefits from integrating textural and compositional data with radiometric dating of accessory minerals. Length scales of crystal growth and diffusive transport in accessory minerals under realistic geologic conditions are typically in the range of 1–10’s of μm, and in some cases even substantially smaller, with zircon having among the lowest diffusion coefficients at a given temperature (e.g., Cherniak and Watson 2003). Intrinsic to the compartmentalization of geochemical and geochronologic information from intra-crystal domains is the requirement to determine accessory mineral compositions using techniques that sample at commensurate spatial scales so as to not convolute the geologic signals that are recorded within crystals, as may be the case with single grain or large grain fragment analysis by isotope dilution thermal ionization mass spectrometry (ID-TIMS; e.g., Schaltegger and Davies 2017, this volume; Schoene and Baxter 2017, this volume). Small crystals can also be difficult to extract by mineral separation techniques traditionally used in geochronology, which also lead to a loss of petrographic context. Secondary Ionization Mass Spectrometry, that is SIMS performed with an ion microprobe, is an analytical technique ideally suited to meet the high spatial resolution analysis requirements that are critical for petrochronology (Table 1).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Reviews in Mineralogy and Geochemistry","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Mineralogical Society of America","publisherLocation":"Washington, D.C.","usgsCitation":"Schmitt, A.K., and Vazquez, J.A., 2017, Secondary ionization mass spectrometry analysis in petrochronology, chap. 7 of Reviews in Mineralogy and Geochemistry, v. 83, no. 1, p. 199-230.","productDescription":"32 p.","startPage":"199","endPage":"230","ipdsId":"IP-078815","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":342430,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":342410,"type":{"id":15,"text":"Index Page"},"url":"https://rimg.geoscienceworld.org/content/83/1/199"}],"volume":"83","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5940f9b1e4b0764e6c63eaa8","contributors":{"authors":[{"text":"Schmitt, Axel K.","contributorId":127614,"corporation":false,"usgs":false,"family":"Schmitt","given":"Axel","email":"","middleInitial":"K.","affiliations":[{"id":7081,"text":"University of California - Los Angeles","active":true,"usgs":false}],"preferred":false,"id":697889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vazquez, Jorge A. 0000-0003-2754-0456 jvazquez@usgs.gov","orcid":"https://orcid.org/0000-0003-2754-0456","contributorId":4458,"corporation":false,"usgs":true,"family":"Vazquez","given":"Jorge","email":"jvazquez@usgs.gov","middleInitial":"A.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true}],"preferred":true,"id":697888,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70188462,"text":"ds1043 - 2017 - Transient electromagnetic soundings in the San Luis Valley, Colorado, near the Great Sand Dunes National Park and Preserve and the Alamosa National Wildlife Refuge (field seasons 2007, 2009, and 2011)","interactions":[],"lastModifiedDate":"2017-06-13T14:30:55","indexId":"ds1043","displayToPublicDate":"2017-06-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1043","title":"Transient electromagnetic soundings in the San Luis Valley, Colorado, near the Great Sand Dunes National Park and Preserve and the Alamosa National Wildlife Refuge (field seasons 2007, 2009, and 2011)","docAbstract":"Transient electromagnetic (TEM) soundings were made in the San Luis Valley, Colorado, to map the location of a blue clay unit as well as to investigate the presence of suspected faults. A total of 147 soundings were made near and in Great Sand Dunes National Park and Preserve, and an additional 6 soundings were made near Hansen Bluff on the eastern edge of the Alamosa National Wildlife Refuge. The blue clay is a significant hydrologic feature in the area that separates an unconfined surface aquifer from a deeper confined aquifer. Knowledge of its location is important to regional hydrological models. Previous analysis of well logs has shown that the blue clay has a resistivity of 10 ohm-meters or less, which is in contrast to the higher resistivity of sand, gravel, and other clay units found in the area, making it a very good target for TEM soundings. The top of the blue clay was found to have considerable relief, suggesting the possibility of deformation of the clay during or after deposition. Because of rift activity, deformation is to be expected. Of the TEM profiles made across faults identified by aeromagnetic data, some showed resistivity variations and (or) subsurface elevation relief of resistivity units, suggestive of faulting. Such patterns were not associated with all suspected faults. The Hansen Bluff profile showed variations in resistivity and depth to conductor that coincide with a scarp between the highlands to the east and the floodplain of the Rio Grande to the west.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1043","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Fitterman, D.V., 2017, Transient electromagnetic soundings in the San Luis Valley, Colorado, near the Great Sand Dunes National Park and Preserve and the Alamosa National Wildlife Refuge (field seasons 2007, 2009, and 2011): U.S. Geological Survey Data Series 1043, 39 p., https://doi.org/10.3133/ds1043.","productDescription":"Report: vii; 52 p.","startPage":"1","endPage":"39","numberOfPages":"52","onlineOnly":"Y","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":342428,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7D21VQ5","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Transient Electromagnetic Sounding Data Collected in the San Luis Valley, Colorado near the Great Sand Dunes National Park and Preserve and the Alamosa National Wildlife Refuge (Field Seasons 2007, 2009, and 2011)"},{"id":342407,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1043/coverthb.jpg"},{"id":342408,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1043/ds1043.pdf","text":"Report","size":"3.92 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1043"}],"country":"United States ","state":"Colorado","otherGeospatial":"San Luis Valley ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.11145019531249,\n 37.42906945530332\n ],\n [\n -105.56488037109375,\n 37.42906945530332\n ],\n [\n -105.56488037109375,\n 37.93444993515032\n ],\n [\n -106.11145019531249,\n 37.93444993515032\n ],\n [\n -106.11145019531249,\n 37.42906945530332\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Crustal Geophysics and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS 964
Denver, CO 80225
Earthquake recurrence models assume that major surface-rupturing earthquakes are followed by periods of reduced rupture probability as stress rebuilds. Although purely periodic, time- or slip-predictable rupture models are known to be oversimplifications, a paucity of long records of fault slip clouds understanding of fault behavior and earthquake recurrence over multiple ruptures. Here, we report a 16 kyr history of fault slip—including a pulse of accelerated slip from 6.4 to 6.0 ka—determined using a Monte Carlo analysis of well-dated offset landforms along the central Altyn Tagh strike-slip fault (ATF) in northwest China. This pulse punctuates a median rate of 8.1+1.2/−0.9 mm/a and likely resulted from either a flurry of temporally clustered ∼Mw 7.5 ground-rupturing earthquakes or a single large >Mw 8.2 earthquake. The clustered earthquake scenario implies rapid re-rupture of a fault reach >195 km long and indicates decoupled rates of elastic strain energy accumulation versus dissipation, conceptualized as a crustal stress battery. If the pulse reflects a single event, slip-magnitude scaling implies that it ruptured much of the ATF with slip similar to, or exceeding, the largest documented historical ruptures. Both scenarios indicate fault rupture behavior that deviates from classic time- or slip-predictable models.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2016.11.024","usgsCitation":"Gold, R.D., Cowgill, E., Arrowsmith, J.R., and Friedrich, A.M., 2017, Pulsed strain release on the Altyn Tagh fault, northwest China: Earth and Planetary Science Letters, v. 459, p. 291-300, https://doi.org/10.1016/j.epsl.2016.11.024.","productDescription":"10 p.","startPage":"291","endPage":"300","ipdsId":"IP-081268","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":469832,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2016.11.024","text":"Publisher Index Page"},{"id":342419,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"Altyn Tagh fault","volume":"459","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5940f9b1e4b0764e6c63eaa4","contributors":{"authors":[{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":697890,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cowgill, Eric","contributorId":192850,"corporation":false,"usgs":false,"family":"Cowgill","given":"Eric","affiliations":[],"preferred":false,"id":697891,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arrowsmith, J. Ramon","contributorId":80209,"corporation":false,"usgs":false,"family":"Arrowsmith","given":"J.","email":"","middleInitial":"Ramon","affiliations":[{"id":24511,"text":"Arizona State University, Tempe AZ USA 85287","active":true,"usgs":false}],"preferred":false,"id":697892,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Friedrich, Anke M.","contributorId":192852,"corporation":false,"usgs":false,"family":"Friedrich","given":"Anke","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":697916,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188850,"text":"70188850 - 2017 - A multi-scale evaluation of pack stock effects on subalpine meadow plant communities in the Sierra Nevada","interactions":[],"lastModifiedDate":"2017-06-27T10:52:46","indexId":"70188850","displayToPublicDate":"2017-06-13T00:00:00","publicationYear":"2017","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":"A multi-scale evaluation of pack stock effects on subalpine meadow plant communities in the Sierra Nevada","docAbstract":"We evaluated the influence of pack stock (i.e., horse and mule) use on meadow plant communities in Sequoia and Yosemite National Parks in the Sierra Nevada of California. Meadows were sampled to account for inherent variability across multiple scales by: 1) controlling for among-meadow variability by using remotely sensed hydro-climatic and geospatial data to pair stock use meadows with similar non-stock (reference) sites, 2) accounting for within-meadow variation in the local hydrology using in-situ soil moisture readings, and 3) incorporating variation in stock use intensity by sampling across the entire available gradient of pack stock use. Increased cover of bare ground was detected only within “dry” meadow areas at the two most heavily used pack stock meadows (maximum animals per night per hectare). There was no difference in plant community composition for any level of soil moisture or pack stock use. Increased local-scale spatial variability in plant community composition (species dispersion) was detected in “wet” meadow areas at the two most heavily used meadows. These results suggest that at the meadow scale, plant communities are generally resistant to the contemporary levels of recreational pack stock use. However, finer-scale within-meadow responses such as increased bare ground or spatial variability in the plant community can be a function of local-scale hydrological conditions. Wilderness managers can improve monitoring of disturbance in Sierra Nevada meadows by adopting multiple plant community indices while simultaneously considering local moisture regimes.
","language":"English","publisher":"PLoS ONE","doi":"10.1371/journal.pone.0178536","usgsCitation":"Lee, S.R., Berlow, E.L., Ostoja, S., Brooks, M.L., Génin, A., Matchett, J.R., and Hart, S.C., 2017, A multi-scale evaluation of pack stock effects on subalpine meadow plant communities in the Sierra Nevada: PLoS ONE, v. 12, no. 6, p. 1-20, https://doi.org/10.1371/journal.pone.0178536.","productDescription":"20 p. ","startPage":"1","endPage":"20","ipdsId":"IP-080186","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":469756,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0178536","text":"Publisher Index Page"},{"id":342904,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Kings Canyon National Park, Sequoia National Park, Yosemite National Park ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.03662109374999,\n 39.977120098439634\n ],\n [\n -121.57470703125,\n 40.98819156349393\n ],\n [\n -122.091064453125,\n 40.763901280945866\n ],\n [\n -121.13525390625,\n 38.58252615935333\n ],\n [\n -119.95971679687499,\n 37.31775185163688\n ],\n [\n -118.927001953125,\n 36.2265501474709\n ],\n [\n -118.76220703125001,\n 35.263561862152095\n ],\n [\n -118.9215087890625,\n 34.95349314197422\n ],\n [\n -118.8006591796875,\n 34.786739162702524\n ],\n [\n -118.641357421875,\n 34.79576153473033\n ],\n [\n -118.114013671875,\n 35.17380831799959\n ],\n [\n -117.8009033203125,\n 35.64390523787731\n ],\n [\n -118.333740234375,\n 37.37015718405753\n ],\n [\n -120.03662109374999,\n 39.977120098439634\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"6","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-13","publicationStatus":"PW","scienceBaseUri":"59521d1fe4b062508e3c3657","contributors":{"authors":[{"text":"Lee, Steven R. 0000-0002-4581-3684 srlee@usgs.gov","orcid":"https://orcid.org/0000-0002-4581-3684","contributorId":5630,"corporation":false,"usgs":true,"family":"Lee","given":"Steven","email":"srlee@usgs.gov","middleInitial":"R.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":700684,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berlow, Eric L.","contributorId":91416,"corporation":false,"usgs":false,"family":"Berlow","given":"Eric","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":700685,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ostoja, Steven M.","contributorId":193514,"corporation":false,"usgs":false,"family":"Ostoja","given":"Steven M.","affiliations":[],"preferred":false,"id":700686,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brooks, Matthew L. 0000-0002-3518-6787 mlbrooks@usgs.gov","orcid":"https://orcid.org/0000-0002-3518-6787","contributorId":393,"corporation":false,"usgs":true,"family":"Brooks","given":"Matthew","email":"mlbrooks@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":700683,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Génin, Alexandre","contributorId":193515,"corporation":false,"usgs":false,"family":"Génin","given":"Alexandre","affiliations":[],"preferred":false,"id":700687,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Matchett, John R. 0000-0002-2905-6468 jmatchett@usgs.gov","orcid":"https://orcid.org/0000-0002-2905-6468","contributorId":1669,"corporation":false,"usgs":true,"family":"Matchett","given":"John","email":"jmatchett@usgs.gov","middleInitial":"R.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":700688,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hart, Stephen C.","contributorId":189074,"corporation":false,"usgs":false,"family":"Hart","given":"Stephen","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":700689,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70181758,"text":"ofr20171018 - 2017 - Five hydrologic and landscape databases for selected National Wildlife Refuges in the Southeastern United States","interactions":[],"lastModifiedDate":"2017-06-12T10:19:48","indexId":"ofr20171018","displayToPublicDate":"2017-06-12T09:45:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1018","title":"Five hydrologic and landscape databases for selected National Wildlife Refuges in the Southeastern United States","docAbstract":"This report serves as metadata and a user guide for five out of six hydrologic and landscape databases developed by the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, to describe data-collection, data-reduction, and data-analysis methods used to construct the databases and provides statistical and graphical descriptions of the databases. Six hydrologic and landscape databases were developed: (1) the Cache River and White River National Wildlife Refuges (NWRs) and contributing watersheds in Arkansas, Missouri, and Oklahoma, (2) the Cahaba River NWR and contributing watersheds in Alabama, (3) the Caloosahatchee and J.N. “Ding” Darling NWRs and contributing watersheds in Florida, (4) the Clarks River NWR and contributing watersheds in Kentucky, Tennessee, and Mississippi, (5) the Lower Suwannee NWR and contributing watersheds in Georgia and Florida, and (6) the Okefenokee NWR and contributing watersheds in Georgia and Florida. Each database is composed of a set of ASCII files, Microsoft Access files, and Microsoft Excel files. The databases were developed as an assessment and evaluation tool for use in examining NWR-specific hydrologic patterns and trends as related to water availability and water quality for NWR ecosystems, habitats, and target species. The databases include hydrologic time-series data, summary statistics on landscape and hydrologic time-series data, and hydroecological metrics that can be used to assess NWR hydrologic conditions and the availability of aquatic and riparian habitat. Landscape data that describe the NWR physiographic setting and the locations of hydrologic data-collection stations were compiled and mapped. Categories of landscape data include land cover, soil hydrologic characteristics, physiographic features, geographic and hydrographic boundaries, hydrographic features, and regional runoff estimates. The geographic extent of each database covers an area within which human activities, climatic variation, and hydrologic processes can potentially affect the hydrologic regime of the NWRs and adjacent areas.
The hydrologic and landscape database for the Cache and White River NWRs and contributing watersheds in Arkansas, Missouri, and Oklahoma has been described and documented in detail (Buell and others, 2012). This report serves as a companion to the Buell and others (2012) report to describe and document the five subsequent hydrologic and landscape databases that were developed: Chapter A—the Cahaba River NWR and contributing watersheds in Alabama, Chapter B—the Caloosahatchee and J.N. “Ding” Darling NWRs and contributing watersheds in Florida, Chapter C—the Clarks River NWR and contributing watersheds in Kentucky, Tennessee, and Mississippi, Chapter D—the Lower Suwannee NWR and contributing watersheds in Georgia and Florida, and Chapter E—the Okefenokee NWR and contributing watersheds in Georgia and Florida.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171018","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Buell, G.R., Gurley, L.N., Calhoun, D.L., and Hunt, A.M., 2017, Five hydrologic and landscape databases for selected National Wildlife Refuges in Southeastern United States: U.S. Geological Survey Open-File Report 2017–1018, 366 p., https://doi.org/10.3133/ofr20171018.","productDescription":"Report: xvi, 386 p. 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\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
Humidity cell tests (HCTs) are long-term (20 to >300 weeks) leach tests that are considered by some to be the among the most reliable geochemical characterization methods for estimating the leachate quality of mined materials. A number of modifications have been added to the original HCT method, but the interpretation of test results varies widely. We suggest that the HCTs represent an underutilized source of geochemical data, with a year-long test generating approximately 2500 individual chemical data points. The HCT concentration peaks and valleys can be thought of as a “chromatogram” of reactions that may occur in the field, whereby peaks in concentrations are associated with different geochemical processes, including sulfate salt dissolution, sulfide oxidation, and dissolution of rock-forming minerals, some of which can neutralize acid. Some of these reactions occur simultaneously, some do not, and geochemical modeling can be used to help distinguish the dominant processes. Our detailed examination, including speciation and inverse modeling, of HCTs from three projects with different geology and mineralization shows that rapid sulfide oxidation dominates over a limited period of time that starts between 40 and 200 weeks of testing. The applicability of laboratory tests results to predicting field leachate concentrations, loads, or rates of reaction has not been adequately demonstrated, although early flush releases and rapid sulfide oxidation rates in HCTs should have some relevance to field conditions. Knowledge of possible maximum solute concentrations is needed to design effective treatment and mitigation approaches. Early flush and maximum sulfide oxidation results from HCTs should be retained and used in environmental models. Factors that complicate the use of HCTs include: sample representation, time for microbial oxidizers to grow, sample storage before testing, geochemical reactions that add or remove constituents, and the HCT results chosen for use in modeling the environmental performance at mine sites. Improved guidance is needed for more consistent interpretation and use of HCT results that rely on identifying: the geochemical processes; the mineralogy, including secondary mineralogy; the available surface area for reactions; and the influence of hydrologic processes on leachate concentrations in runoff, streams, and groundwater.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2017.03.016","usgsCitation":"Maest, A., and Nordstrom, D.K., 2017, A geochemical examination of humidity cell tests: Applied Geochemistry, v. 81, p. 109-131, https://doi.org/10.1016/j.apgeochem.2017.03.016.","productDescription":"23 p.","startPage":"109","endPage":"131","ipdsId":"IP-085397","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":469758,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2017.03.016","text":"Publisher Index Page"},{"id":344491,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"81","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59819315e4b0e2f5d463b79b","contributors":{"authors":[{"text":"Maest, Ann","contributorId":195266,"corporation":false,"usgs":false,"family":"Maest","given":"Ann","affiliations":[],"preferred":false,"id":706653,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nordstrom, D. Kirk 0000-0003-3283-5136 dkn@usgs.gov","orcid":"https://orcid.org/0000-0003-3283-5136","contributorId":749,"corporation":false,"usgs":true,"family":"Nordstrom","given":"D.","email":"dkn@usgs.gov","middleInitial":"Kirk","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":false,"id":706652,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70191343,"text":"70191343 - 2017 - Differences in breeding bird assemblages related to reed canary grass cover cover and forest structure on the Upper Mississippi River","interactions":[],"lastModifiedDate":"2017-10-05T14:07:05","indexId":"70191343","displayToPublicDate":"2017-06-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Differences in breeding bird assemblages related to reed canary grass cover cover and forest structure on the Upper Mississippi River","docAbstract":"Floodplain forest of the Upper Mississippi River provides habitat for an abundant and diverse breeding bird community. However, reed canary grass Phalaris arundinacea invasion is a serious threat to the future condition of this forest. Reed canary grass is a well-known aggressive invader of wetland systems in the northern tier states of the conterminous United States. Aided by altered flow regimes and nutrient inputs from agriculture, reed canary grass has formed dense stands in canopy gaps and forest edges, retarding tree regeneration. We sampled vegetation and breeding birds in Upper Mississippi River floodplain forest edge and interior areas to 1) measure reed canary grass cover and 2) evaluate whether the breeding bird assemblage responded to differences in reed canary grass cover. Reed canary grass was found far into forest interiors, and its cover was similar between interior and edge sites. Bird assemblages differed between areas with more or less reed canary grass cover (.53% cover breakpoint). Common yellowthroat Geothlypis trichas, black-capped chickadee Parus atricapillus, and rose-breasted grosbeak Pheucticus ludovicianus were more common and American redstart Setophaga ruticilla, great crested flycatcher Myiarchus crinitus, and Baltimore oriole Icterus galbula were less common in sites with more reed canary grass cover. Bird diversity and abundance were similar between sites with different reed canary grass cover. A stronger divergence in bird assemblages was associated with ground cover ,15%, resulting from prolonged spring flooding. These sites hosted more prothonotary warbler Protonotaria citrea, but they had reduced bird abundance and diversity compared to other sites. Our results indicate that frequently flooded sites may be important for prothonotary warblers and that bird assemblages shift in response to reed canary grass invasion.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/012016-JFWM-002","usgsCitation":"Kirsch, E.M., and Gray, B.R., 2017, Differences in breeding bird assemblages related to reed canary grass cover cover and forest structure on the Upper Mississippi River: Journal of Fish and Wildlife Management, v. 8, no. 1, p. 260-271, https://doi.org/10.3996/012016-JFWM-002.","productDescription":"12 p.","startPage":"260","endPage":"271","ipdsId":"IP-071439","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":487160,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/012016-jfwm-002","text":"Publisher Index Page"},{"id":346426,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.87773132324219,\n 44.80814739879984\n ],\n [\n -93.04183959960938,\n 44.80035239611148\n ],\n [\n -93.08029174804688,\n 44.6334823448553\n ],\n [\n -92.823486328125,\n 44.50923820945519\n ],\n [\n -92.5048828125,\n 44.47495104782301\n ],\n [\n -92.46299743652344,\n 44.51903083890047\n ],\n [\n -92.49458312988281,\n 44.603668403518775\n ],\n [\n -92.63671875,\n 44.71161010858431\n ],\n [\n -92.87773132324219,\n 44.80814739879984\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-01","publicationStatus":"PW","scienceBaseUri":"59d744a2e4b05fe04cc7e320","contributors":{"authors":[{"text":"Kirsch, Eileen M. 0000-0002-2818-5022 ekirsch@usgs.gov","orcid":"https://orcid.org/0000-0002-2818-5022","contributorId":3477,"corporation":false,"usgs":true,"family":"Kirsch","given":"Eileen","email":"ekirsch@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":712013,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gray, Brian R. 0000-0001-7682-9550 brgray@usgs.gov","orcid":"https://orcid.org/0000-0001-7682-9550","contributorId":2615,"corporation":false,"usgs":true,"family":"Gray","given":"Brian","email":"brgray@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":712014,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70188459,"text":"70188459 - 2017 - Migration trends of Sockeye Salmon at the northern edge of their distribution","interactions":[],"lastModifiedDate":"2017-06-12T13:18:09","indexId":"70188459","displayToPublicDate":"2017-06-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Migration trends of Sockeye Salmon at the northern edge of their distribution","docAbstract":"Climate change is affecting arctic and subarctic ecosystems, and anadromous fish such as Pacific salmon Oncorhynchus spp. are particularly susceptible due to the physiological challenge of spawning migrations. Predicting how migratory timing will change under Arctic warming scenarios requires an understanding of how environmental factors drive salmon migrations. Multiple mechanisms exist by which environmental conditions may influence migrating salmon, including altered migration cues from the ocean and natal river. We explored relationships between interannual variability and annual migration timing (2003–2014) of Sockeye Salmon O. nerka in a subarctic watershed with environmental conditions at broad, intermediate, and local spatial scales. Low numbers of Sockeye Salmon have returned to this high-latitude watershed in recent years, and run size has been a dominant influence on the migration duration and the midpoint date of the run. The duration of the migration upriver varied by as much as 25 d across years, and shorter run durations were associated with smaller run sizes. The duration of the migration was also extended with warmer sea surface temperatures in the staging area and lower values of the North Pacific Index. The midpoint date of the total run was earlier when the run size was larger, whereas the midpoint date was delayed during years in which river temperatures warmed earlier in the season. Documenting factors related to the migration of Sockeye Salmon near the northern limit of their range provides insights into the determinants of salmon migrations and suggests processes that could be important for determining future changes in arctic and subarctic ecosystems.
","language":"English","publisher":"American Fisheries Society","doi":"10.1080/00028487.2017.1302992","usgsCitation":"Carey, M.P., Zimmerman, C.E., Keith, K.D., Schelske, M., Lean, C., and Douglas, D.C., 2017, Migration trends of Sockeye Salmon at the northern edge of their distribution: Transactions of the American Fisheries Society, v. 146, no. 4, p. 791-802, https://doi.org/10.1080/00028487.2017.1302992.","productDescription":"12 p.","startPage":"791","endPage":"802","ipdsId":"IP-080815","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":438300,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71834PF","text":"USGS data release","linkHelpText":"Count of Sockeye Salmon (Oncorhynchus nerka), River Temperature, and River Height in the Pilgrim River, Nome, Alaska, 2003-2014"},{"id":342405,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -175.95703125,\n 48.04870994288686\n ],\n [\n -122.78320312499999,\n 48.04870994288686\n ],\n [\n -122.78320312499999,\n 65.44000165965534\n ],\n [\n -175.95703125,\n 65.44000165965534\n ],\n [\n -175.95703125,\n 48.04870994288686\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"146","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-08","publicationStatus":"PW","scienceBaseUri":"593fa82fe4b0764e6c62793c","contributors":{"authors":[{"text":"Carey, Michael P. 0000-0002-3327-8995 mcarey@usgs.gov","orcid":"https://orcid.org/0000-0002-3327-8995","contributorId":5397,"corporation":false,"usgs":true,"family":"Carey","given":"Michael","email":"mcarey@usgs.gov","middleInitial":"P.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":697870,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zimmerman, Christian E. 0000-0002-3646-0688 czimmerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3646-0688","contributorId":410,"corporation":false,"usgs":true,"family":"Zimmerman","given":"Christian","email":"czimmerman@usgs.gov","middleInitial":"E.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":697869,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keith, Kevin D.","contributorId":192846,"corporation":false,"usgs":false,"family":"Keith","given":"Kevin","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":697871,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schelske, Merlyn","contributorId":192847,"corporation":false,"usgs":false,"family":"Schelske","given":"Merlyn","email":"","affiliations":[],"preferred":false,"id":697872,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lean, Charles","contributorId":189274,"corporation":false,"usgs":false,"family":"Lean","given":"Charles","email":"","affiliations":[],"preferred":false,"id":697873,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":697874,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70193465,"text":"70193465 - 2017 - Influence of trap modifications and environmental predictors on capture success of southern flying squirrels","interactions":[],"lastModifiedDate":"2017-11-02T13:11:40","indexId":"70193465","displayToPublicDate":"2017-06-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Influence of trap modifications and environmental predictors on capture success of southern flying squirrels","docAbstract":"Sherman traps are the most commonly used live traps in studies of small mammals and have been successfully used in the capture of arboreal species such as the southern flying squirrel (Glaucomys volans). However, southern flying squirrels spend proportionately less time foraging on the ground, which necessitates above-ground trapping methods and modifications of capture protocols. Further, quantitative estimates of the factors affecting capture success of flying squirrel populations have focused solely on effects of trapping methodologies. We developed and evaluated the efficacy of a portable Sherman trap design for capturing southern flying squirrels during 2015–2016 at the Alice L. Kibbe Field Station, Illinois, USA. Additionally, we used logistic regression to quantify potential effects of time-dependent (e.g., weather) and time-independent (e.g., habitat, extrinsic) factors on capture success of southern flying squirrels. We recorded 165 capture events (119 F, 44 M, 2 unknown) using our modified Sherman trap design. Probability of capture success decreased 0.10/1° C increase in daily maximum temperature and by 0.09/unit increase (km/hr) in wind speed. Conversely, probability of capture success increased by 1.2/1° C increase in daily minimum temperature. The probability of capturing flying squirrels was negatively associated with trap orientation. When tree-mounted traps are required, our modified trap design is a safe, efficient, and cost-effective method of capturing animals when moderate weather (temp and wind speed) conditions prevail. Further, we believe that strategic placement of traps (e.g., northeast side of tree) and quantitative information on site-specific (e.g., trap location) characteristics (e.g., topographical features, slope, aspect, climatologic factors) could increase southern flying squirrel capture success. © 2017 The Wildlife Society.
","language":"English","publisher":"Wiley","doi":"10.1002/wsb.769","usgsCitation":"Jacques, C.N., Zweep, J.S., Scheihing, M.E., Rechkemmer, W.T., Jenkins, S.E., Klaver, R.W., and Dubay, S.A., 2017, Influence of trap modifications and environmental predictors on capture success of southern flying squirrels: Wildlife Society Bulletin, v. 41, no. 2, p. 313-321, https://doi.org/10.1002/wsb.769.","productDescription":"9 p.","startPage":"313","endPage":"321","ipdsId":"IP-078961","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":469757,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doaj.org/article/7c2cd54ecd554011aca5558f735f007e","text":"Publisher Index Page"},{"id":348085,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois","county":"Hancock","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.52435302734375,\n 40.02130468739707\n ],\n [\n -90.50537109375,\n 40.02130468739707\n ],\n [\n -90.50537109375,\n 40.701463603604594\n ],\n [\n -91.52435302734375,\n 40.701463603604594\n ],\n [\n -91.52435302734375,\n 40.02130468739707\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-14","publicationStatus":"PW","scienceBaseUri":"59fc2ea4e4b0531197b27f81","contributors":{"authors":[{"text":"Jacques, Christopher N.","contributorId":15521,"corporation":false,"usgs":true,"family":"Jacques","given":"Christopher","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":719677,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zweep, James S.","contributorId":199664,"corporation":false,"usgs":false,"family":"Zweep","given":"James","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":719678,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scheihing, Mary E.","contributorId":199665,"corporation":false,"usgs":false,"family":"Scheihing","given":"Mary","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":719679,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rechkemmer, Will T.","contributorId":196304,"corporation":false,"usgs":false,"family":"Rechkemmer","given":"Will","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":719680,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jenkins, Sean E.","contributorId":199666,"corporation":false,"usgs":false,"family":"Jenkins","given":"Sean","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":719681,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719145,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dubay, Shelli A.","contributorId":171437,"corporation":false,"usgs":false,"family":"Dubay","given":"Shelli","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":719682,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70188458,"text":"70188458 - 2017 - Synoptic sampling and principal components analysis to identify sources of water and metals to an acid mine drainage stream","interactions":[],"lastModifiedDate":"2017-07-12T10:23:19","indexId":"70188458","displayToPublicDate":"2017-06-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1564,"text":"Environmental Science and Pollution Research","active":true,"publicationSubtype":{"id":10}},"title":"Synoptic sampling and principal components analysis to identify sources of water and metals to an acid mine drainage stream","docAbstract":"Combining the synoptic mass balance approach with principal components analysis (PCA) can be an effective method for discretising the chemistry of inflows and source areas in watersheds where contamination is diffuse in nature and/or complicated by groundwater interactions. This paper presents a field-scale study in which synoptic sampling and PCA are employed in a mineralized watershed (Lion Creek, Colorado, USA) under low flow conditions to (i) quantify the impacts of mining activity on stream water quality; (ii) quantify the spatial pattern of constituent loading; and (iii) identify inflow sources most responsible for observed changes in stream chemistry and constituent loading. Several of the constituents investigated (Al, Cd, Cu, Fe, Mn, Zn) fail to meet chronic aquatic life standards along most of the study reach. The spatial pattern of constituent loading suggests four primary sources of contamination under low flow conditions. Three of these sources are associated with acidic (pH <3.1) seeps that enter along the left bank of Lion Creek. Investigation of inflow water (trace metal and major ion) chemistry using PCA suggests a hydraulic connection between many of the left bank inflows and mine water in the Minnesota Mine shaft located to the north-east of the river channel. In addition, water chemistry data during a rainfall-runoff event suggests the spatial pattern of constituent loading may be modified during rainfall due to dissolution of efflorescent salts or erosion of streamside tailings. These data point to the complexity of contaminant mobilisation processes and constituent loading in mining-affected watersheds but the combined synoptic sampling and PCA approach enables a conceptual model of contaminant dynamics to be developed to inform remediation.
","language":"English","publisher":"Springer","doi":"10.1007/s11356-017-9038-x","usgsCitation":"Byrne, P., Runkel, R.L., and Walton-Day, K., 2017, Synoptic sampling and principal components analysis to identify sources of water and metals to an acid mine drainage stream: Environmental Science and Pollution Research, v. 24, no. 20, p. 17220-17240, https://doi.org/10.1007/s11356-017-9038-x.","productDescription":"21 p.","startPage":"17220","endPage":"17240","ipdsId":"IP-080091","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":469759,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11356-017-9038-x","text":"Publisher Index Page"},{"id":342403,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"20","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-06","publicationStatus":"PW","scienceBaseUri":"593fa830e4b0764e6c627943","contributors":{"authors":[{"text":"Byrne, Patrick","contributorId":192845,"corporation":false,"usgs":false,"family":"Byrne","given":"Patrick","affiliations":[],"preferred":false,"id":697867,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":697866,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walton-Day, Katherine 0000-0002-9146-6193 kwaltond@usgs.gov","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":184043,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","email":"kwaltond@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":697868,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70198788,"text":"70198788 - 2017 - Vegetation recovery in tidal marshes reveals critical slowing down under increased inundation","interactions":[],"lastModifiedDate":"2018-08-24T12:24:18","indexId":"70198788","displayToPublicDate":"2017-06-09T16:38:08","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Vegetation recovery in tidal marshes reveals critical slowing down under increased inundation","docAbstract":"A declining rate of recovery following disturbance has been proposed as an important early warning for impending tipping points in complex systems. Despite extensive theoretical and laboratory studies, this ‘critical slowing down’ remains largely untested in the complex settings of real-world ecosystems. Here, we provide both observational and experimental support of critical slowing down along natural stress gradients in tidal marsh ecosystems. Time series of aerial images of European marsh development reveal a consistent lengthening of recovery time as inundation stress increases. We corroborate this finding with transplantation experiments in European and North American tidal marshes. In particular, our results emphasize the power of direct observational or experimental measures of recovery over indirect statistical signatures, such as spatial variance or autocorrelation. Our results indicate that the phenomenon of critical slowing down can provide a powerful tool to probe the resilience of natural ecosystems.
","language":"English","publisher":"Springer","doi":"10.1038/ncomms15811","usgsCitation":"van Belzen, J., van de Koppel, J., Kirwan, M.L., van der Wal, D., Herman, P.M., Dakos, V., Kefi, S., Scheffer, M., Guntenspergen, G.R., and Bouma, T.J., 2017, Vegetation recovery in tidal marshes reveals critical slowing down under increased inundation: Nature Communications, v. 8, Article 15811; 7 p., https://doi.org/10.1038/ncomms15811.","productDescription":"Article 15811; 7 p.","ipdsId":"IP-081830","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":469760,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/ncomms15811","text":"Publisher Index Page"},{"id":356635,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-09","publicationStatus":"PW","scienceBaseUri":"5b98a423e4b0702d0e843075","contributors":{"authors":[{"text":"van Belzen, Jim","contributorId":207154,"corporation":false,"usgs":false,"family":"van Belzen","given":"Jim","email":"","affiliations":[{"id":37466,"text":"Department of Estuarine and Delta Systems, Royal Netherlands Institute for Sea Research (NIOZ) and Utrecht University, PO Box 140, Yerseke NL-4400 AC, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":742951,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van de Koppel, Johan","contributorId":207155,"corporation":false,"usgs":false,"family":"van de Koppel","given":"Johan","email":"","affiliations":[{"id":37466,"text":"Department of Estuarine and Delta Systems, Royal Netherlands Institute for Sea Research (NIOZ) and Utrecht University, PO Box 140, Yerseke NL-4400 AC, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":742952,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kirwan, Matthew L.","contributorId":191373,"corporation":false,"usgs":false,"family":"Kirwan","given":"Matthew","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":742953,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"van der Wal, Daphne","contributorId":207156,"corporation":false,"usgs":false,"family":"van der Wal","given":"Daphne","email":"","affiliations":[{"id":37466,"text":"Department of Estuarine and Delta Systems, Royal Netherlands Institute for Sea Research (NIOZ) and Utrecht University, PO Box 140, Yerseke NL-4400 AC, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":742954,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Herman, Peter M. J.","contributorId":207157,"corporation":false,"usgs":false,"family":"Herman","given":"Peter","email":"","middleInitial":"M. J.","affiliations":[{"id":37466,"text":"Department of Estuarine and Delta Systems, Royal Netherlands Institute for Sea Research (NIOZ) and Utrecht University, PO Box 140, Yerseke NL-4400 AC, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":742955,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dakos, Vasilis","contributorId":198880,"corporation":false,"usgs":false,"family":"Dakos","given":"Vasilis","email":"","affiliations":[],"preferred":false,"id":742956,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kefi, Sonia","contributorId":207158,"corporation":false,"usgs":false,"family":"Kefi","given":"Sonia","email":"","affiliations":[{"id":37467,"text":"Institut des Sciences de l'Evolution, Université de Montpellier, CNRS, IRD, EPHE, CC065, 34095 Montpellier Cedex 05, France","active":true,"usgs":false}],"preferred":false,"id":742957,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Scheffer, Marten","contributorId":207159,"corporation":false,"usgs":false,"family":"Scheffer","given":"Marten","email":"","affiliations":[{"id":37468,"text":"Aquatic Ecology and Water Quality Management Group, Environmental Science Department, Wageningen University, Wageningen NL-6700 AA, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":742958,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Guntenspergen, Glenn R. 0000-0002-8593-0244 glenn_guntenspergen@usgs.gov","orcid":"https://orcid.org/0000-0002-8593-0244","contributorId":2885,"corporation":false,"usgs":true,"family":"Guntenspergen","given":"Glenn","email":"glenn_guntenspergen@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":742950,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bouma, Tjeerd J.","contributorId":207160,"corporation":false,"usgs":false,"family":"Bouma","given":"Tjeerd","email":"","middleInitial":"J.","affiliations":[{"id":37466,"text":"Department of Estuarine and Delta Systems, Royal Netherlands Institute for Sea Research (NIOZ) and Utrecht University, PO Box 140, Yerseke NL-4400 AC, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":742959,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70188425,"text":"70188425 - 2017 - The added value of time-variable microgravimetry to the understanding of how volcanoes work","interactions":[],"lastModifiedDate":"2018-10-25T16:02:45","indexId":"70188425","displayToPublicDate":"2017-06-09T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1431,"text":"Earth-Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"The added value of time-variable microgravimetry to the understanding of how volcanoes work","docAbstract":"During the past few decades, time-variable volcano gravimetry has shown great potential for imaging subsurface processes at active volcanoes (including some processes that might otherwise remain “hidden”), especially when combined with other methods (e.g., ground deformation, seismicity, and gas emissions). By supplying information on changes in the distribution of bulk mass over time, gravimetry can provide information regarding processes such as magma accumulation in void space, gas segregation at shallow depths, and mechanisms driving volcanic uplift and subsidence.\n\nDespite its potential, time-variable volcano gravimetry is an underexploited method, not widely adopted by volcano researchers or observatories. The cost of instrumentation and the difficulty in using it under harsh environmental conditions is a significant impediment to the exploitation of gravimetry at many volcanoes. In addition, retrieving useful information from gravity changes in noisy volcanic environments is a major challenge. While these difficulties are not trivial, neither are they insurmountable; indeed, creative efforts in a variety of volcanic settings highlight the value of time-variable gravimetry for understanding hazards as well as revealing fundamental insights into how volcanoes work.\n\nBuilding on previous work, we provide a comprehensive review of time-variable volcano gravimetry, including discussions of instrumentation, modeling and analysis techniques, and case studies that emphasize what can be learned from campaign, continuous, and hybrid gravity observations. We are hopeful that this exploration of time-variable volcano gravimetry will excite more scientists about the potential of the method, spurring further application, development, and innovation.","language":"English","publisher":"Elsevier","doi":"10.1016/j.earscirev.2017.04.014","usgsCitation":"Carbone, D., Poland, M.P., Greco, F., and Diament, M., 2017, The added value of time-variable microgravimetry to the understanding of how volcanoes work: Earth-Science Reviews, v. 169, p. 146-179, https://doi.org/10.1016/j.earscirev.2017.04.014.","productDescription":"34 p. ","startPage":"146","endPage":"179","ipdsId":"IP-079219","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":342326,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Earth","volume":"169","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"593ad6dfe4b0764e6c60213b","contributors":{"authors":[{"text":"Carbone, Daniele","contributorId":124561,"corporation":false,"usgs":false,"family":"Carbone","given":"Daniele","email":"","affiliations":[{"id":5113,"text":"INGV","active":true,"usgs":false}],"preferred":false,"id":697680,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Poland, Michael P. 0000-0001-5240-6123 mpoland@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":146118,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","email":"mpoland@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":697679,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Greco, Filippo","contributorId":192761,"corporation":false,"usgs":false,"family":"Greco","given":"Filippo","email":"","affiliations":[],"preferred":false,"id":697681,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Diament, Michel","contributorId":190642,"corporation":false,"usgs":false,"family":"Diament","given":"Michel","email":"","affiliations":[],"preferred":false,"id":697682,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188426,"text":"70188426 - 2017 - The spectrum of persistent volcanic flank instability: A review and proposed framework based on Kīlauea, Piton de la Fournaise, and Etna","interactions":[],"lastModifiedDate":"2017-06-09T09:18:57","indexId":"70188426","displayToPublicDate":"2017-06-09T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"The spectrum of persistent volcanic flank instability: A review and proposed framework based on Kīlauea, Piton de la Fournaise, and Etna","docAbstract":"Persistent motion of the south flank of Kīlauea Volcano, Hawai'i, has been known for several decades, but has only recently been identified at other large basaltic volcanoes—namely Piton de la Fournaise (La Réunion) and Etna (Sicily)—thanks to the advent of space geodetic techniques. Nevertheless, understanding of long-term flank instability is based largely on the example of Kīlauea, despite the large differences in the manifestations and mechanisms of the process when viewed through a comparative lens. For example, the rate of flank motion at Kīlauea is several times that of Etna and Piton de la Fournaise and is accommodated on a slip plane several km deeper than is probably present at the other two volcanoes. Gravitational spreading also appears to be the dominant driving force at Kīlauea, given the long-term steady motion of the volcano's south flank regardless of eruptive/intrusive activity, whereas magmatic activity plays a larger role in flank deformation at Etna and Piton de la Fournaise. Kīlauea and Etna, however, are both characterized by heavily faulted flanks, while Piton de la Fournaise shows little evidence for flank faulting. A helpful means of understanding the spectrum of persistent flank motion at large basaltic edifices may be through a framework defined on one hand by magmatic activity (which encompasses both magma supply and edifice size), and on the other hand by the structural setting of the volcano (especially the characteristics of the subvolcanic basement or subhorizontal intravolcanic weak zones). A volcano's size and magmatic activity will dictate the extent to which gravitational and magmatic forces can drive motion of an unstable flank (and possibly the level of faulting of that flank), while the volcano's structural setting governs whether or not a plane of weakness exists beneath or within the edifice and can facilitate flank slip. Considering persistent flank instability using this conceptual model is an alternative to using a single volcano as a “type example”—especially given that the example is usually Kīlauea, which defines an extreme end of the spectrum—and can provide a basis for understanding why flank motion may or may not exist on other large basaltic volcanoes worldwide.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2017.05.004","usgsCitation":"Poland, M.P., Peltier, A., Bonaforte, A., and Puglisi, G., 2017, The spectrum of persistent volcanic flank instability: A review and proposed framework based on Kīlauea, Piton de la Fournaise, and Etna: Journal of Volcanology and Geothermal Research, v. 339, p. 63-80, https://doi.org/10.1016/j.jvolgeores.2017.05.004.","productDescription":"18 p.","startPage":"63","endPage":"80","ipdsId":"IP-083995","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":469761,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://insu.hal.science/insu-03748853","text":"Publisher Index Page"},{"id":342321,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"France, Italy, United States","otherGeospatial":"Etna, Kīlauea Volcano, Piton de la Fournaise","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -154.883333,\n 19.55\n ],\n [\n -155.5,\n 19.55\n ],\n [\n -155.5,\n 19.116667\n ],\n [\n -154.883333,\n 19.116667\n ],\n [\n -154.883333,\n 19.55\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 55.666667,\n -21.152778\n ],\n [\n 55.666667,\n -21.319444\n ],\n [\n 55.836111,\n -21.319444\n ],\n [\n 55.836111,\n -21.152778\n ],\n [\n 55.666667,\n -21.152778\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 14.766667,\n 37.916667\n ],\n [\n 15.233333,\n 37.916667\n ],\n [\n 15.233333,\n 37.483333\n ],\n [\n 14.766667,\n 37.483333\n ],\n [\n 14.766667,\n 37.916667\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"339","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"593ad6dee4b0764e6c602139","contributors":{"authors":[{"text":"Poland, Michael P. 0000-0001-5240-6123 mpoland@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":146118,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","email":"mpoland@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":697683,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peltier, Aline","contributorId":149410,"corporation":false,"usgs":false,"family":"Peltier","given":"Aline","email":"","affiliations":[],"preferred":false,"id":697684,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bonaforte, Alessandro","contributorId":192762,"corporation":false,"usgs":false,"family":"Bonaforte","given":"Alessandro","email":"","affiliations":[],"preferred":false,"id":697685,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Puglisi, Giuseppe","contributorId":192763,"corporation":false,"usgs":false,"family":"Puglisi","given":"Giuseppe","email":"","affiliations":[],"preferred":false,"id":697686,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70187352,"text":"sir20175036 - 2017 - Performance measures for a Mississippi River reintroduction into the forested wetlands of Maurepas Swamp","interactions":[],"lastModifiedDate":"2017-06-09T09:28:31","indexId":"sir20175036","displayToPublicDate":"2017-06-09T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5036","title":"Performance measures for a Mississippi River reintroduction into the forested wetlands of Maurepas Swamp","docAbstract":"The use of freshwater diversions (river reintroductions) from the Mississippi River as a restoration tool to rehabilitate Louisiana coastal wetlands has been promoted widely since the first such diversion at Caernarvon became operational in the early 1990s. To date, aside from the Bonnet Carré Spillway (which is designed and operated for flood control), there are only four operational Mississippi River freshwater diversions (two gated structures and two siphons) in coastal Louisiana, and they all target salinity intrusion, shellfish management, and (or) the enhancement of the integrity of marsh habitat. River reintroductions carry small sediment loads for various design reasons, but they can be effective in delivering freshwater to combat saltwater intrusion and increase the delivery of nutrients and suspended fine-grained sediments to receiving wetlands. River reintroductions may be an ideal restoration tool for targeting coastal swamp forest habitat; much of the area of swamp forest habitat in coastal Louisiana is undergoing saltwater intrusion, high rates of submergence, and lack of riverine flow leading to reduced concentrations of important nutrients and suspended sediments, which sustain growth and regeneration, help to aerate swamp soils, and remove toxic compounds from the rhizosphere.
The State of Louisiana Coastal Protection and Restoration Authority (CPRA) has made it a priority to establish a small freshwater river diversion into a coastal swamp forest located between Baton Rouge and New Orleans, Louisiana, to reintroduce Mississippi River water to Maurepas Swamp. While a full understanding of how a coastal swamp forest will respond to new freshwater loading through a Mississippi River reintroduction is unknown, this report provides guidance based on the available literature for establishing performance measures that can be used for evaluating the effectiveness of a Mississippi River reintroduction into the forested wetlands of Maurepas Swamp (project PO-29 of the Coastal Wetlands Planning, Protection and Restoration Act) and aid in adaptive management of the project. PO-29 is a small river reintroduction in scope, and through its operation, it will provide information about the feasibility and reasonable expectations for future river reintroduction projects targeting coastal swamp forests in Louisiana.
Located near Garyville, Louisiana, the Mississippi River reintroduction into Maurepas Swamp project is being designed to deliver a maximum flow of 57 cubic meters per second (m3/s) (or about 2,000 cubic feet per second [ft3/s]) directly from the river, but with a maximum flow through the outflow channel of 42 m3/s (or 1,500 ft3/s) available for at least half of the year. The river reintroduction will divert Mississippi River water through channelized flow and surface water to impact approximately 16,583 hectares (ha) of wetland habitat, much of which is swamp forest and swamp forest transitioning into marsh habitat. The Mississippi River reintroduction into Maurepas Swamp and associated outfall management features collectively should facilitate connectivity of water between the Mississippi River and the entire project area.
At any given location, hydrologic connectivity should occur at intervals between twice yearly and once per decade, and hydrologic management must allow the potential for water drawdowns to foster tree regeneration every 3–13 years. The river reintroduction is also anticipated to maintain salinity in swamp forests dominated by Taxodium distichum (baldcypress) to less than 1.3 practical salinity units (psu) and maintain salinity in mixed baldcypress and Nyssa aquatica (water tupelo) swamp forests to less than 0.8 psu. The river reintroduction should promote soil surface elevation gains of 8–9 millimeters per year (mm/yr) (range, 4.9–12.1 mm/yr) to offset relative sea-level rise and keep total river water nitrate (NO3-) loading into Maurepas Swamp to about 11.25 grams (g) of nitrogen (N) per square meter per year (m-2 yr-1 ) (range, 7.1–15.4 g N m-2 yr-1) to promote near complete uptake of NO3- by the vegetation in the receiving wetlands and reduce impacts to water quality in adjacent and connected water ways (for example, Blind River) and Lake Maurepas. With these performance measures maintained over time, we further expect swamp forest stands to realize improvements in stand density index of as much as 30–45 percent of maximum values for the stand type while maintaining an overstory leaf area index of 2.0–2.9 square meters per square meter or higher as swamp forests recover from decades of low flow, saltwater intrusion, reduced nutrients, and surface elevation deficits associated with isolation from the Mississippi River.
Associated with these performance measures are two major uncertainties: (1) an assumption that we can rely on existing data, literature, and modeling from coastal swamp forests to establish these performance measures and (2) an unknown time frame for evaluating these performance measures. Some performance measures can be assessed quickly, such as those associated with hydrology and nutrient uptake. Some performance measures, such as changes in soil surface elevation and forest structural integrity, could take longer to assess. Once performance measures are assessed across different time scales, however, adjustments to operations of the Mississippi River reintroduction into Maurepas Swamp can be swift. The proposed performance measures are ideal targets, mostly without specific consideration of practical, operational constraints. The measures are intended to be the basis by which adaptive management of the diversion structures can be evaluated. The measures are defined without regard to current conditions so that project success can be evaluated on net outcomes rather than specific change from existing conditions. We expect that the Mississippi River reintroduction into Maurepas Swamp will slow degradation and extend the life of the swamp for decades to centuries.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175036","collaboration":"Prepared in cooperation with the Coastal Protection and Restoration Authority (CPRA) of Louisiana","usgsCitation":"Krauss, K.W., Shaffer, G.P., Keim, R.F., Chambers, J.L., Wood, W.B., and Hartley, S.B., 2017, Performance measures for a Mississippi River reintroduction into the forested wetlands of Maurepas Swamp: U.S. Geological Survey Scientific Investigations Report 2017–5036, 56 p., https://doi.org/10.3133/sir20175036.","productDescription":"vii, 56 p.","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-076437","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research 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A colony of honey bees is an amazing organism when it is healthy; it is a superorganism in many senses of the word. As with any organism, maintaining a state of health requires cohesiveness and interplay among cells and tissues and, in the case of a honey bee colony, the bees themselves. The individual bees that make up a honey bee colony deliver to the superorganism what it needs: pollen and nectar collected from flowering plants that contain nutrients necessary for growth and survival. Honey bees with access to better and more complete nutrition exhibit improved immune system function and behavioral defenses for fighting off effects of pathogens and pesticides (Evans and Spivak 2010; Mao, Schuler, and Berenbaum 2013; Wahl and Ulm 1983). Sadly, as this story is often told in the headlines, the focus is rarely about what it means for a honey bee colony to be healthy and is instead primarily focused on colony survival rates. Bee colonies are chronically exposed to parasitic mites, viruses, diseases, miticides, pesticides, and poor nutrition, which weaken and make innate defenses insufficient at overcoming these combined stressors. Colonies that are chronically weakened can be even more susceptible to infections and levels of pesticide exposure that might otherwise be innocuous, further promoting a downward spiral of health. Sick and weakened bees diminish the colony’s resiliency, ultimately leading to a breakdown in the social structure, production, efficiency, immunity, and reproduction of the colony, and eventual or sudden colony death.
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provides more than 40 percent of California’s drinking water. To protect this vital resource, the State of California created the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The Priority Basin Project of the GAMA Program provides a comprehensive assessment of the State’s groundwater quality and increases public access to groundwater-quality information. The Western San Joaquin Valley is one of the study units being evaluated.
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6000 J Street, Placer Hall
Sacramento, California 95819
We know economic and social policy has implications for ecosystems at large, but the consequences for a given geographic area or specific wildlife population are more difficult to conceptualize and communicate. Species distribution models, which extrapolate species-habitat relationships across ecological scales, are capable of predicting population changes in distribution and abundance in response to management and policy, and thus, are an ideal means for facilitating proactive management within a larger policy framework. To illustrate the capabilities of species distribution modeling in scenario planning for wildlife populations, we projected an existing distribution model for ring-necked pheasants (Phasianus colchicus) onto a series of alternative future landscape scenarios for Nebraska, USA. Based on our scenarios, we qualitatively and quantitatively estimated the effects of agricultural policy decisions on pheasant populations across Nebraska, in specific management regions, and at wildlife management areas.
","language":"English","publisher":"Wildlife Society","doi":"10.1002/wsb.763","usgsCitation":"Fontaine, J.J., Jorgensen, C., Stuber, E.F., Gruber, L.F., Bishop, A.A., Lusk, J.J., Zach, E.S., and Decker, K.L., 2017, Species distributions models in wildlife planning: agricultural policy and wildlife management in the great plains: Wildlife Society Bulletin, v. 41, no. 2, p. 194-204, https://doi.org/10.1002/wsb.763.","productDescription":"11 p.","startPage":"194","endPage":"204","ipdsId":"IP-074173","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500009,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/96c855f789ed430aa033cce5d08fd393","text":"External Repository"},{"id":347513,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","volume":"41","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-09","publicationStatus":"PW","scienceBaseUri":"59f83a35e4b063d5d30980d6","contributors":{"authors":[{"text":"Fontaine, Joseph J. 0000-0002-7639-9156 jfontaine@usgs.gov","orcid":"https://orcid.org/0000-0002-7639-9156","contributorId":3820,"corporation":false,"usgs":true,"family":"Fontaine","given":"Joseph","email":"jfontaine@usgs.gov","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":716477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jorgensen, Christopher","contributorId":198580,"corporation":false,"usgs":false,"family":"Jorgensen","given":"Christopher","affiliations":[],"preferred":false,"id":716478,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stuber, Erica F.","contributorId":198581,"corporation":false,"usgs":false,"family":"Stuber","given":"Erica","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":716479,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gruber, Lutz F.","contributorId":198582,"corporation":false,"usgs":false,"family":"Gruber","given":"Lutz","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":716480,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bishop, Andrew A.","contributorId":93323,"corporation":false,"usgs":true,"family":"Bishop","given":"Andrew","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":716481,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lusk, Jeffrey J.","contributorId":198584,"corporation":false,"usgs":false,"family":"Lusk","given":"Jeffrey","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":716482,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zach, Eric S.","contributorId":198585,"corporation":false,"usgs":false,"family":"Zach","given":"Eric","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":716483,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Decker, Karie L.","contributorId":51094,"corporation":false,"usgs":true,"family":"Decker","given":"Karie","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":716484,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70187444,"text":"sir20175032 - 2017 - Groundwater quality in the Western San Joaquin Valley study unit, 2010: California GAMA Priority Basin Project","interactions":[],"lastModifiedDate":"2019-12-30T14:45:28","indexId":"sir20175032","displayToPublicDate":"2017-06-09T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5032","title":"Groundwater quality in the Western San Joaquin Valley study unit, 2010: California GAMA Priority Basin Project","docAbstract":"Water quality in groundwater resources used for public drinking-water supply in the Western San Joaquin Valley (WSJV) was investigated by the USGS in cooperation with the California State Water Resources Control Board (SWRCB) as part of its Groundwater Ambient Monitoring and Assessment (GAMA) Program Priority Basin Project. The WSJV includes two study areas: the Delta–Mendota and Westside subbasins of the San Joaquin Valley groundwater basin. Study objectives for the WSJV study unit included two assessment types: (1) a status assessment yielding quantitative estimates of the current (2010) status of groundwater quality in the groundwater resources used for public drinking water, and (2) an evaluation of natural and anthropogenic factors that could be affecting the groundwater quality. The assessments characterized the quality of untreated groundwater, not the quality of treated drinking water delivered to consumers by water distributors.
The status assessment was based on data collected from 43 wells sampled by the U.S. Geological Survey for the GAMA Priority Basin Project (USGS-GAMA) in 2010 and data compiled in the SWRCB Division of Drinking Water (SWRCB-DDW) database for 74 additional public-supply wells sampled for regulatory compliance purposes between 2007 and 2010. To provide context, concentrations of constituents measured in groundwater were compared to U.S. Environmental Protection Agency (EPA) and SWRCB-DDW regulatory and non-regulatory benchmarks for drinking-water quality. The status assessment used a spatially weighted, grid-based method to estimate the proportion of the groundwater resources used for public drinking water that has concentrations for particular constituents or class of constituents approaching or above benchmark concentrations. This method provides statistically unbiased results at the study-area scale within the WSJV study unit, and permits comparison of the two study areas to other areas assessed by the GAMA Priority Basin Project statewide.
Groundwater resources used for public drinking water in the WSJV study unit are among the most saline and most affected by high concentrations of inorganic constituents of all groundwater resources used for public drinking water that have been assessed by the GAMA Priority Basin Project statewide. Among the 82 GAMA Priority Basin Project study areas statewide, the Delta–Mendota study area ranked above the 90th percentile for aquifer-scale proportions of groundwater resources having concentrations of total dissolved solids (TDS), sulfate, chloride, manganese, boron, chromium(VI), selenium, and strontium above benchmarks, and the Westside study area ranked above the 90th percentile for TDS, sulfate, manganese, and boron.
In the WSJV study unit as a whole, one or more inorganic constituents with regulatory or non-regulatory, health-based benchmarks were present at concentrations above benchmarks in about 53 percent of the groundwater resources used for public drinking water, and one or more organic constituents with regulatory health-based benchmarks were detected at concentrations above benchmarks in about 3 percent of the resource. Individual constituents present at concentrations greater than health-based benchmarks in greater than 2 percent of groundwater resources used for public drinking water included: boron (51 percent, SWRCB-DDW notification level), chromium(VI) (25 percent, SWRCB-DDW maximum contaminant level (MCL)), arsenic (10 percent, EPA MCL), strontium (5.1 percent, EPA Lifetime health advisory level (HAL)), nitrate (3.9 percent, EPA MCL), molybdenum (3.8 percent, EPA HAL), selenium (2.6 percent, EPA MCL), and benzene (2.6 percent, SWRCB-DDW MCL). In addition, 50 percent of the resource had TDS concentrations greater than non-regulatory, aesthetic-based SWRCB-DDW upper secondary maximum contaminant level (SMCL), and 44 percent had manganese concentrations greater than the SWRCB-DDW SMCL.
Natural and anthropogenic factors that could affect the groundwater quality were evaluated by using results from statistical testing of associations between constituent concentrations and values of potential explanatory factors, inferences from geochemical and age-dating tracer results, and by considering the water-quality results in the context of the hydrogeologic setting of the WSJV study unit.
Natural factors, particularly the lithologies of the source areas for groundwater recharge and of the aquifers, were the dominant factors affecting groundwater quality in most of the WSJV study unit. However, where groundwater resources used for public supply included groundwater recharged in the modern era, mobilization of constituents by recharge of water used for irrigation also affected groundwater quality. Public-supply wells in the Westside study area had a median depth of 305 m and primarily tapped groundwater recharged hundreds to thousands of years ago, whereas public-supply wells in the Delta–Mendota study area had a median depth of 85 m and primarily tapped either groundwater recharged within the last 60 years or groundwater consisting of mixtures of this modern recharge and older recharge.
Public-supply wells in the WSJV study unit are screened in the Tulare Formation and zones above and below the Corcoran Clay Member are used. The Tulare Formation primarily consists of alluvial sediments derived from the Coast Ranges to the west, except along the valley trough at the eastern margin of the WSJV study unit where the Tulare Formation consists of fluvial sands derived from the Sierra Nevada to the east. Groundwater from wells screened in the Sierra Nevada sands had manganese-reducing or manganese- and iron-reducing oxidation-reduction (redox) conditions. These redox conditions commonly were associated with elevated arsenic or molybdenum concentrations, and the dominance of arsenic(III) in the dissolved arsenic supports reductive dissolution of iron and manganese oxyhydroxides as the mechanism. In addition, groundwater from many wells screened in Sierra Nevada sands contained low concentrations of nitrite or ammonium, indicating reduction of nitrate by denitrification or dissimilatory processes, respectively.
Geology of the Coast Ranges westward of the study unit strongly affects groundwater quality in the WSJV. Elevated concentrations of TDS, sulfate, boron, selenium and strontium in groundwater were primarily associated with aquifer sediments and recharge derived from areas of the Coast Ranges dominated by Cretaceous-to-Miocene age, organic-rich, reduced marine shales, known as the source of selenium in WSJV soils, surface water, and groundwater. Low sulfur-isotopic values (δ34S) of dissolved sulfate indicate that the sulfate was largely derived from oxidation of biogenic pyrite from the shales, and correlations with trace element concentrations, geologic setting, and groundwater geochemical modeling indicated that distributions of sulfate, strontium, and selenium in groundwater were controlled by dissolution of secondary sulfate minerals in soils and sediments.
Elevated concentrations of chromium(VI) were primarily associated with aquifer sediments and recharge derived from areas of the Coast Ranges dominated by the Franciscan Complex and ultramafic rocks. The Franciscan Complex also has boron-rich, sodium-chloride dominated hydrothermal fluids that contribute to elevated concentrations of boron and TDS.
Groundwater from wells screened in Coast Ranges alluvium was primarily oxic and relatively alkaline (median pH value of 7.55) in the Delta–Mendota study area, and primarily nitrate-reducing or suboxic and alkaline (median pH value of 8.4) in the Westside study area. Many groundwater samples from those wells have elevated concentrations of arsenic(V), molybdenum, selenium, or chromium(VI), consistent with desorption of metal oxyanions from mineral surfaces under those geochemical conditions.
High concentrations of benzene were associated with deep wells located in the vicinity of petroleum deposits at the southern end of the Westside study area. Groundwater from these wells had premodern age and anoxic geochemical conditions, and the ratios among concentrations of hydrocarbon constituents were different from ratios found in fuels and combustion products, which is consistent with a geogenic source for the benzene rather than contamination from anthropogenic sources.
Water stable-isotope compositions, groundwater recharge temperatures, and groundwater ages were used to infer four types of groundwater: (1) groundwater derived from natural recharge of water from major rivers draining the Sierra Nevada; (2) groundwater primarily derived from natural recharge of water from Coast Ranges runoff; (3) groundwater derived from recharge of pumped groundwater applied to the land surface for irrigation; and (4) groundwater derived from recharge during a period of much cooler paleoclimate. Water previously used for irrigation was found both above and below the Corcoran Clay, supporting earlier inferences that this clay member is no longer a robust confining unit.
Recharge of water used for irrigation has direct and indirect effects on groundwater quality. Elevated nitrate concentrations and detections of herbicides and fumigants in the Delta–Mendota study area generally were associated with greater agricultural land use near the well and with water recharged during the last 60 years. However, the extent of the groundwater resource affected by agricultural sources of nitrate was limited by groundwater redox conditions sufficient to reduce nitrate. The detection frequency of perchlorate in Delta–Mendota groundwater was greater than expected for natural conditions. Perchlorate, nitrate, selenium, and strontium concentrations were correlated with one another and were greater in groundwater inferred to be recharge of previously pumped groundwater used for irrigation. The source of the perchlorate, selenium, and strontium appears to be salts deposited in the soils and sediments of the arid WSJV that are dissolved and flushed into groundwater by the increased amount of recharge caused by irrigation. In the Delta–Mendota study area, the groundwater with elevated concentrations of selenium was found deeper in the aquifer system than it was reported by a previous study 25 years earlier, suggesting that this transient front of groundwater with elevated concentrations of constituents derived from dissolution of soil salts by irrigation recharge is moving down through the aquifer system and is now reaching the depth zone used for public drinking water supply.
California Water Science Center
California GAMA
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Marine iron–manganese oxide coatings occur in many shallow and deep-water areas of the global ocean and can form in three ways: 1) Fe–Mn crusts can precipitate from seawater onto rocks on seamounts; 2) Fe–Mn nodules can form on the sediment surface around a nucleus by diagenetic processes in sediment pore water; 3) encrustations can precipitate from hydrothermal fluids. These oxide coatings have been growing for thousands to tens of millions of years. They represent a vast archive of how oceans have changed, including variations of climate, ocean currents, geological activity, erosion processes on land, and even anthropogenic impact. A growing toolbox of age-dating methods and element and isotopic signatures are being used to exploit these archives.
","language":"English","publisher":"Mineralogical Society of America","doi":"10.2113/gselements.13.3.177","usgsCitation":"Koschinsky, A., and Hein, J.R., 2017, Marine ferromanganese encrustations: Archives of changing oceans: Elements, v. 13, no. 3, p. 177-182, https://doi.org/10.2113/gselements.13.3.177.","productDescription":"6 p.","startPage":"177","endPage":"182","ipdsId":"IP-081254","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":342316,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-01","publicationStatus":"PW","scienceBaseUri":"593ad6e0e4b0764e6c602143","contributors":{"authors":[{"text":"Koschinsky, Andrea","contributorId":83813,"corporation":false,"usgs":true,"family":"Koschinsky","given":"Andrea","affiliations":[],"preferred":false,"id":697668,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hein, James R. 0000-0002-5321-899X jhein@usgs.gov","orcid":"https://orcid.org/0000-0002-5321-899X","contributorId":140835,"corporation":false,"usgs":true,"family":"Hein","given":"James","email":"jhein@usgs.gov","middleInitial":"R.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":697667,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70188400,"text":"70188400 - 2017 - An updated geospatial liquefaction model for global application","interactions":[],"lastModifiedDate":"2017-06-08T10:30:00","indexId":"70188400","displayToPublicDate":"2017-06-08T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"An updated geospatial liquefaction model for global application","docAbstract":"We present an updated geospatial approach to estimation of earthquake-induced liquefaction from globally available geospatial proxies. Our previous iteration of the geospatial liquefaction model was based on mapped liquefaction surface effects from four earthquakes in Christchurch, New Zealand, and Kobe, Japan, paired with geospatial explanatory variables including slope-derived VS30, compound topographic index, and magnitude-adjusted peak ground acceleration from ShakeMap. The updated geospatial liquefaction model presented herein improves the performance and the generality of the model. The updates include (1) expanding the liquefaction database to 27 earthquake events across 6 countries, (2) addressing the sampling of nonliquefaction for incomplete liquefaction inventories, (3) testing interaction effects between explanatory variables, and (4) overall improving model performance. While we test 14 geospatial proxies for soil density and soil saturation, the most promising geospatial parameters are slope-derived VS30, modeled water table depth, distance to coast, distance to river, distance to closest water body, and precipitation. We found that peak ground velocity (PGV) performs better than peak ground acceleration (PGA) as the shaking intensity parameter. We present two models which offer improved performance over prior models. We evaluate model performance using the area under the curve under the Receiver Operating Characteristic (ROC) curve (AUC) and the Brier score. The best-performing model in a coastal setting uses distance to coast but is problematic for regions away from the coast. The second best model, using PGV, VS30, water table depth, distance to closest water body, and precipitation, performs better in noncoastal regions and thus is the model we recommend for global implementation.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120160198","usgsCitation":"Zhu, J., Baise, L.G., and Thompson, E.M., 2017, An updated geospatial liquefaction model for global application: Bulletin of the Seismological Society of America, v. 107, no. 3, p. 1365-1385, https://doi.org/10.1785/0120160198.","productDescription":"21 p. ","startPage":"1365","endPage":"1385","ipdsId":"IP-081714","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":342287,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Japan, New Zealand","city":"Christchurch, Kobe","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 172.8053283691406,\n -43.57939602461447\n ],\n [\n 172.73529052734375,\n -43.39007990915452\n ],\n [\n 172.65289306640625,\n -43.40205426790564\n ],\n [\n 172.5457763671875,\n -43.4509250075837\n ],\n [\n 172.51007080078122,\n -43.500752435690394\n ],\n [\n 172.4798583984375,\n -43.5515340832395\n ],\n [\n 172.48809814453125,\n -43.593322162687436\n ],\n [\n 172.52105712890625,\n -43.62712937016884\n ],\n [\n 172.58834838867188,\n -43.65098183989868\n ],\n [\n 172.628173828125,\n -43.659924074789096\n ],\n [\n 172.8053283691406,\n -43.57939602461447\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 135.10025024414062,\n 34.64394177616416\n ],\n [\n 135.1318359375,\n 34.6241677899049\n ],\n [\n 135.1819610595703,\n 34.61456158160819\n ],\n [\n 135.24032592773438,\n 34.61456158160819\n ],\n [\n 135.30555725097653,\n 34.62699293367839\n ],\n [\n 135.3206634521484,\n 34.64733112904415\n ],\n [\n 135.3289031982422,\n 34.68573411017608\n ],\n [\n 135.31997680664062,\n 34.722426197808446\n ],\n [\n 135.31173706054688,\n 34.74894726028228\n ],\n [\n 135.2849578857422,\n 34.75853788866992\n ],\n [\n 135.24238586425778,\n 34.75458894128615\n ],\n [\n 135.1922607421875,\n 34.742740966060076\n ],\n [\n 135.14076232910156,\n 34.72355492704221\n ],\n [\n 135.10025024414062,\n 34.64394177616416\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"107","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-02","publicationStatus":"PW","scienceBaseUri":"593ad6e1e4b0764e6c602149","contributors":{"authors":[{"text":"Zhu, Jing","contributorId":152048,"corporation":false,"usgs":false,"family":"Zhu","given":"Jing","email":"","affiliations":[{"id":6936,"text":"Tufts University","active":true,"usgs":false}],"preferred":false,"id":697589,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baise, Laurie G.","contributorId":127395,"corporation":false,"usgs":false,"family":"Baise","given":"Laurie","email":"","middleInitial":"G.","affiliations":[{"id":6936,"text":"Tufts University","active":true,"usgs":false}],"preferred":false,"id":697590,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":146592,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":697591,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188407,"text":"70188407 - 2017 - Collecting a better water-quality sample: Reducing vertical stratification bias in open and closed channels","interactions":[],"lastModifiedDate":"2017-06-08T15:03:15","indexId":"70188407","displayToPublicDate":"2017-06-08T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Collecting a better water-quality sample: Reducing vertical stratification bias in open and closed channels","docAbstract":"Collection of water-quality samples that accurately characterize average particle concentrations and distributions in channels can be complicated by large sources of variability. The U.S. Geological Survey (USGS) developed a fully automated Depth-Integrated Sample Arm (DISA) as a way to reduce bias and improve accuracy in water-quality concentration data. The DISA was designed to integrate with existing autosampler configurations commonly used for the collection of water-quality samples in vertical profile thereby providing a better representation of average suspended sediment and sediment-associated pollutant concentrations and distributions than traditional fixed-point samplers. In controlled laboratory experiments, known concentrations of suspended sediment ranging from 596 to 1,189 mg/L were injected into a 3 foot diameter closed channel (circular pipe) with regulated flows ranging from 1.4 to 27.8 ft3 /s. Median suspended sediment concentrations in water-quality samples collected using the DISA were within 7 percent of the known, injected value compared to 96 percent for traditional fixed-point samplers. Field evaluation of this technology in open channel fluvial systems showed median differences between paired DISA and fixed-point samples to be within 3 percent. The range of particle size measured in the open channel was generally that of clay and silt. Differences between the concentration and distribution measured between the two sampler configurations could potentially be much larger in open channels that transport larger particles, such as sand.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 5th Federal Interagency Hydrologic Modeling Conference and the 10th Federal Interagency Sedimentation Conference","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Joint Federal Interagency Conference 2015","conferenceDate":"April 19-23, 2015","conferenceLocation":"Reno, NV","language":"English","publisher":"Department of Interior","publisherLocation":"Reston, VA","usgsCitation":"Selbig, W.R., 2017, Collecting a better water-quality sample: Reducing vertical stratification bias in open and closed channels, in Proceedings of the 5th Federal Interagency Hydrologic Modeling Conference and the 10th Federal Interagency Sedimentation Conference, Reno, NV, April 19-23, 2015, 11 p.","productDescription":"11 p.","ipdsId":"IP-060694","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":342312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":342311,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://acwi.gov/sos/pubs/3rdJFIC/"}],"publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"593ad6e1e4b0764e6c602147","contributors":{"authors":[{"text":"Selbig, William R. 0000-0003-1403-8280 wrselbig@usgs.gov","orcid":"https://orcid.org/0000-0003-1403-8280","contributorId":877,"corporation":false,"usgs":true,"family":"Selbig","given":"William","email":"wrselbig@usgs.gov","middleInitial":"R.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":697626,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70188416,"text":"70188416 - 2017 - Frictional strength of wet and dry montmorillonite","interactions":[],"lastModifiedDate":"2017-06-14T11:55:32","indexId":"70188416","displayToPublicDate":"2017-06-08T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Frictional strength of wet and dry montmorillonite","docAbstract":"Montmorillonite is a common mineral in fault zones, and its low strength relative to other common gouge minerals is important in many models of fault rheology. However, the coefficient of friction, μ, varies with degree of saturation and is not well constrained in the literature due to the difficulty of establishing fully drained or fully dried states in the laboratory. We measured μ of both saturated and oven-dried montmorillonite at normal stresses up to 700 MPa. Care was taken to shear saturated samples slowly enough to avoid pore fluid overpressure. For saturated samples, μ increased from 0.10 to 0.28 with applied effective normal stress, while for dry samples μ decreased from 0.78 to 0.45. The steady state rate dependence of friction, (a − b), was positive, promoting stable sliding. The wide disparity in reported frictional strengths can be attributed to experimental procedures that promote differing degrees of partial saturation or overpressured pore fluid conditions.
","language":"English","publisher":"AGU","doi":"10.1002/2016JB013658","usgsCitation":"Morrow, C.A., Moore, D.E., and Lockner, D.A., 2017, Frictional strength of wet and dry montmorillonite: Journal of Geophysical Research, v. 122, no. 5, p. 3392-3409, https://doi.org/10.1002/2016JB013658.","productDescription":"18 p.","startPage":"3392","endPage":"3409","ipdsId":"IP-079028","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":342317,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"122","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-06","publicationStatus":"PW","scienceBaseUri":"593ad6e1e4b0764e6c602145","contributors":{"authors":[{"text":"Morrow, Carolyn A. 0000-0003-3500-6181 cmorrow@usgs.gov","orcid":"https://orcid.org/0000-0003-3500-6181","contributorId":3206,"corporation":false,"usgs":true,"family":"Morrow","given":"Carolyn","email":"cmorrow@usgs.gov","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":697651,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Diane E. 0000-0002-8641-1075 dmoore@usgs.gov","orcid":"https://orcid.org/0000-0002-8641-1075","contributorId":2704,"corporation":false,"usgs":true,"family":"Moore","given":"Diane","email":"dmoore@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":697653,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lockner, David A. 0000-0001-8630-6833 dlockner@usgs.gov","orcid":"https://orcid.org/0000-0001-8630-6833","contributorId":567,"corporation":false,"usgs":true,"family":"Lockner","given":"David","email":"dlockner@usgs.gov","middleInitial":"A.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":697652,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}