Phytoplankton are critical component of the food web in most large rivers and estuaries, and thus identifying dominant controls on phytoplankton abundance and species composition is important to scientists, managers, and policymakers. Recent studies from a variety of systems indicate that ammonium ( NH+4) in treated wastewater effluent decreases primary production and alters phytoplankton species composition. However, these findings are based mainly on laboratory and enclosure studies, which may not adequately represent natural systems. To test effects of effluent high in ammonium on phytoplankton at the ecosystem scale, we conducted whole-river–scale experiments by halting discharges to the Sacramento River from the regional wastewater treatment plant (WWTP), and used a Lagrangian approach to compare changes in phytoplankton abundance and species composition in the presence (+EFF) and absence (−EFF) of effluent. Over 5 d of downstream travel from 20 km above to 50 km below the WWTP, chlorophyll concentrations declined from 15–25 to ∼2.5 μg L−1, irrespective of effluent addition. Benthic diatoms were dominant in most samples. We found no significant difference in phytoplankton abundance or species composition between +EFF and −EFF conditions. Moreover, greatest declines in chlorophyll occurred upstream of the WWTP where NH+4 concentrations were low. Grazing by clams and zooplankton could not account for observed losses, suggesting other factors such as hydrodynamics and light limitation were responsible for phytoplankton declines. These results highlight the advantages of conducting ecosystem-scale, Lagrangian-based experiments to understand the dynamic and complex interplay between physical, chemical, and biological factors that control phytoplankton populations.
","language":"English","publisher":"American Society of Limnology and Oceanography","publisherLocation":"Lawrence, KS","doi":"10.1002/lno.10497","usgsCitation":"Kraus, T.E., Carpenter, K.D., Bergamaschi, B.A., Parker, A., Stumpner, E.B., Downing, B.D., Travis, N., Wilkerson, F., Kendall, C., and Mussen, T., 2017, A river-scale Lagrangian experiment examining controls on phytoplankton dynamics in the presence and absence of treated wastewater effluent high in ammonium: Limnology and Oceanography, v. 62, no. 3, p. 1234-1253, https://doi.org/10.1002/lno.10497.","productDescription":"20 p.","startPage":"1234","endPage":"1253","ipdsId":"IP-069386","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":470064,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.10497","text":"Publisher Index Page"},{"id":335806,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.75,\n 38.616667\n ],\n [\n -121.416667,\n 38.616667\n ],\n [\n -121.416667,\n 38.116667\n ],\n [\n -121.75,\n 38.116667\n ],\n [\n -121.75,\n 38.616667\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"62","issue":"3","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-04","publicationStatus":"PW","scienceBaseUri":"58a819b6e4b025c46429afc2","contributors":{"authors":[{"text":"Kraus, Tamara E. 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During August we also examined the diet and consumption rates of juvenile American Shad Alosa sapidissima, a potential competitor of subyearling Chinook Salmon. Subyearling Chinook Salmon consumed Daphnia in July but switched to feeding on smaller juvenile American Shad in August. We captured no juvenile American Shad in July, but in August juvenile American Shad consumed cyclopoid and calanoid copepods. Stomach evacuation rates for subyearling Chinook Salmon were high during both sample periods (0.58 h−1 in July, 0.51 h−1 in August), and daily ration estimates were slightly higher than values reported in the literature for other subyearlings. By switching from planktivory to piscivory, subyearling Chinook Salmon gained greater growth opportunity. While past studies have shown that juvenile American Shad reduce zooplankton availability for Chinook Salmon subyearlings, our work indicates that they also become important prey after Daphnia abundance declines. The diet and consumption data here can be used in future bioenergetics modeling to estimate the growth of subyearling Chinook Salmon in lower Columbia River reservoirs.","language":"English","publisher":"Taylor & Francis","doi":"10.1080/00028487.2016.1264997","usgsCitation":"Haskell, C.A., Beauchamp, D.A., and Bollins, S.M., 2017, Trophic interactions and consumption rates of subyearling Chinook Salmon and nonnative juvenile American Shad in Columbia River reservoirs: Transactions of the American Fisheries Society, v. 146, no. 2, p. 291-298, https://doi.org/10.1080/00028487.2016.1264997.","productDescription":"Report: 7 p.","startPage":"291","endPage":"298","ipdsId":"IP-077398","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":335804,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.89791870117186,\n 46.616431255357995\n ],\n [\n -119.88006591796874,\n 46.63387921452026\n ],\n [\n -119.893798828125,\n 46.67111414362431\n ],\n [\n -119.89517211914061,\n 46.68666038407398\n ],\n [\n -119.90272521972655,\n 46.71256084516054\n ],\n [\n -119.90341186523436,\n 46.73374289048227\n ],\n [\n -119.92538452148438,\n 46.75397574126115\n ],\n [\n -119.91714477539061,\n 46.778433231125035\n ],\n [\n -119.93362426757811,\n 46.79206816192883\n ],\n [\n -119.95765686035155,\n 46.78783700126104\n ],\n [\n -119.97482299804686,\n 46.77231989968773\n ],\n [\n -119.99885559082028,\n 46.740331163960654\n ],\n [\n -120.0043487548828,\n 46.713973240259\n ],\n [\n -119.99610900878905,\n 46.670642975602824\n ],\n [\n -119.97276306152344,\n 46.64566519258491\n ],\n [\n -119.96520996093749,\n 46.624448583576246\n ],\n [\n -119.94117736816406,\n 46.60039303734549\n ],\n [\n -119.89791870117186,\n 46.616431255357995\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.4170684814453,\n 46.66687348356156\n ],\n [\n -117.43423461914061,\n 46.66074749832068\n ],\n [\n -117.4383544921875,\n 46.65037886496509\n ],\n [\n -117.4376678466797,\n 46.630107159317205\n ],\n [\n -117.41500854492188,\n 46.59756227061819\n ],\n [\n -117.37724304199219,\n 46.53949896024682\n ],\n [\n -117.34565734863281,\n 46.496974231448355\n ],\n [\n -117.30857849121092,\n 46.452050911230394\n ],\n [\n -117.2515869140625,\n 46.40756396630067\n ],\n [\n -117.19047546386719,\n 46.40188216826328\n ],\n [\n -117.08541870117188,\n 46.39951457775959\n ],\n [\n -117.06344604492189,\n 46.41655893628349\n ],\n [\n -117.08473205566406,\n 46.44826620185314\n ],\n [\n -117.11769104003906,\n 46.4903562824648\n ],\n [\n -117.16163635253906,\n 46.52721745699692\n ],\n [\n -117.23854064941406,\n 46.5635815342227\n ],\n [\n -117.27081298828125,\n 46.594731356000686\n ],\n [\n -117.32299804687499,\n 46.628221033128646\n ],\n [\n -117.35870361328124,\n 46.64425101077151\n ],\n [\n -117.3896026611328,\n 46.66074749832068\n ],\n [\n -117.4170684814453,\n 46.66687348356156\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"146","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58a819b7e4b025c46429afc6","contributors":{"authors":[{"text":"Haskell, Craig A. 0000-0002-3604-1758 chaskell@usgs.gov","orcid":"https://orcid.org/0000-0002-3604-1758","contributorId":3458,"corporation":false,"usgs":true,"family":"Haskell","given":"Craig","email":"chaskell@usgs.gov","middleInitial":"A.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":669788,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beauchamp, David A. 0000-0002-3592-8381 fadave@usgs.gov","orcid":"https://orcid.org/0000-0002-3592-8381","contributorId":4205,"corporation":false,"usgs":true,"family":"Beauchamp","given":"David","email":"fadave@usgs.gov","middleInitial":"A.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":669789,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bollins, Stephen M","contributorId":181851,"corporation":false,"usgs":false,"family":"Bollins","given":"Stephen","email":"","middleInitial":"M","affiliations":[],"preferred":false,"id":669790,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188377,"text":"70188377 - 2017 - Gravitational body forces focus North American intraplate earthquakes","interactions":[],"lastModifiedDate":"2017-06-07T14:43:49","indexId":"70188377","displayToPublicDate":"2017-02-17T00:00:00","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":"Gravitational body forces focus North American intraplate earthquakes","docAbstract":"Earthquakes far from tectonic plate boundaries generally exploit ancient faults, but not all intraplate faults are equally active. The North American Great Plains exemplify such intraplate earthquake localization, with both natural and induced seismicity generally clustered in discrete zones. Here we use seismic velocity, gravity and topography to generate a 3D lithospheric density model of the region; subsequent finite-element modelling shows that seismicity focuses in regions of high-gravity-derived deviatoric stress. Furthermore, predicted principal stress directions generally align with those observed independently in earthquake moment tensors and borehole breakouts. Body forces therefore appear to control the state of stress and thus the location and style of intraplate earthquakes in the central United States with no influence from mantle convection or crustal weakness necessary. These results show that mapping where gravitational body forces encourage seismicity is crucial to understanding and appraising intraplate seismic hazard.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/ncomms14314","usgsCitation":"Levandowski, W.B., Zellman, M., and Briggs, R.W., 2017, Gravitational body forces focus North American intraplate earthquakes: Nature Communications, v. 8, Article 14314: 9 p., https://doi.org/10.1038/ncomms14314.","productDescription":"Article 14314: 9 p.","ipdsId":"IP-073321","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":470063,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/ncomms14314","text":"Publisher Index Page"},{"id":342259,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.5,\n 45.5\n ],\n [\n -94.5,\n 45.5\n ],\n [\n -94.5,\n 36.5\n ],\n [\n -105.5,\n 36.5\n ],\n [\n -105.5,\n 45.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-17","publicationStatus":"PW","scienceBaseUri":"593910ace4b0764e6c5e8852","contributors":{"authors":[{"text":"Levandowski, William Brower 0000-0003-4903-5012 wlevandowski@usgs.gov","orcid":"https://orcid.org/0000-0003-4903-5012","contributorId":5729,"corporation":false,"usgs":true,"family":"Levandowski","given":"William","email":"wlevandowski@usgs.gov","middleInitial":"Brower","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":697456,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zellman, Mark","contributorId":167020,"corporation":false,"usgs":false,"family":"Zellman","given":"Mark","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":697457,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Briggs, Richard W. 0000-0001-8108-0046 rbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-8108-0046","contributorId":139002,"corporation":false,"usgs":true,"family":"Briggs","given":"Richard","email":"rbriggs@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":697458,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70180208,"text":"fs20173004 - 2017 - West Africa land use and land cover time series","interactions":[],"lastModifiedDate":"2022-04-01T22:45:54.521094","indexId":"fs20173004","displayToPublicDate":"2017-02-16T18:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3004","title":"West Africa land use and land cover time series","docAbstract":"Started in 1999, the West Africa Land Use Dynamics project represents an effort to map land use and land cover, characterize the trends in time and space, and understand their effects on the environment across West Africa. The outcome of the West Africa Land Use Dynamics project is the production of a three-time period (1975, 2000, and 2013) land use and land cover dataset for the Sub-Saharan region of West Africa, including the Cabo Verde archipelago. The West Africa Land Use Land Cover Time Series dataset offers a unique basis for characterizing and analyzing land changes across the region, systematically and at an unprecedented level of detail.
","language":"English, French","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173004","usgsCitation":"Cotillon, S.E., 2017, West Africa land use and land cover time series: U.S. Geological Survey Fact Sheet 2017–3004, 4 p., https://doi.org/10.3133/fs20173004.","productDescription":"Report: 4 p.; Data release","startPage":"1","endPage":"4","onlineOnly":"Y","ipdsId":"IP-082363","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":335789,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3004/coverthb.jpg"},{"id":335791,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3004/fs20173004.pdf","text":"Report – English","size":"9.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3004 English"},{"id":335792,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3004/fs20173004_French.pdf","text":"Report – French","size":"9.24 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3004 French"},{"id":397981,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F73N21JF","text":"USGS data release","description":"USGS data release","linkHelpText":"West Africa Land Use Land Cover Time Series"}],"country":"Benin, Burkina Faso, Chad, Cote d'Ivoire, The Gambia, Guinea, Guinea Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, Togo","otherGeospatial":"West Africa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 29.443359375,\n 31.203404950917395\n ],\n [\n 22.587890625,\n 33.137551192346145\n ],\n [\n 19.86328125,\n 32.76880048488168\n ],\n [\n 19.775390625,\n 31.952162238024975\n ],\n [\n 19.16015625,\n 31.27855085894653\n ],\n [\n 15.908203125,\n 32.69486597787505\n ],\n [\n 13.886718749999998,\n 33.65120829920497\n ],\n [\n 12.12890625,\n 33.87041555094183\n ],\n [\n 10.546875,\n 34.52466147177172\n ],\n [\n 6.064453125,\n 34.813803317113155\n ],\n [\n 0.439453125,\n 33.65120829920497\n ],\n [\n -3.955078125,\n 32.76880048488168\n ],\n [\n -9.052734375,\n 30.44867367928756\n ],\n [\n -12.3046875,\n 28.536274512989916\n ],\n [\n -16.962890625,\n 25.005972656239187\n ],\n [\n -18.017578125,\n 21.37124437061831\n ],\n [\n -17.490234375,\n 18.895892559415024\n ],\n [\n -18.193359375,\n 16.130262012034756\n ],\n [\n -18.6328125,\n 12.46876014482322\n ],\n [\n -16.5234375,\n 10.14193168613103\n ],\n [\n -14.94140625,\n 8.407168163601076\n ],\n [\n -5.888671875,\n 9.015302333420598\n ],\n [\n 5.185546875,\n 11.43695521614319\n ],\n [\n 12.216796875,\n 10.746969318460001\n ],\n [\n 19.16015625,\n 9.709057068618208\n ],\n [\n 23.115234374999996,\n 11.005904459659451\n ],\n [\n 30.585937499999996,\n 13.838079936422462\n ],\n [\n 32.783203125,\n 18.06231230454674\n ],\n [\n 33.486328125,\n 23.805449612314625\n ],\n [\n 33.57421875,\n 27.527758206861886\n ],\n [\n 29.443359375,\n 31.203404950917395\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, South Dakota 57198-0001
What has driven changes in land use and land cover in West Africa over the past 40 years? What trends or patterns can be discerned in those changes? To answer these questions, the U.S. Geological Survey West Africa Land Use Dynamics project partnered with the Permanent Interstate Committee for Drought Control in the Sahel and the U.S. Agency for International Development/West Africa to map land use and land cover across the region for three time periods (years): 1975, 2000, and 2013. This cooperative effort has resulted in the publication of a 219-page atlas, “Landscapes of West Africa: A Window on a Changing World.” The atlas uses satellite imagery, maps, and pictures to tell a complex story of landscape change at regional and national scales. It includes a collection of focused studies, some of which raise cause for concern, and others that provide considerable hope.
","language":"English, French","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173005","usgsCitation":"Cotillon, S.E., 2017, The landscapes of West Africa—40 years of change: U.S. Geological Survey Fact Sheet 2017–3005, 2 p., https://doi.org/10.3133/fs20173005.","productDescription":"2 p.","onlineOnly":"Y","ipdsId":"IP-082370","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":335758,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3005/fs20173005_French.pdf","text":"Report – French","size":"3.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3005 French"},{"id":335757,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3005/fs20173005.pdf","text":"Report – English","size":"3.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3005 English"},{"id":335751,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3005/coverthb.jpg"}],"otherGeospatial":"West Africa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 10.41107660211324,\n 2.118513429359794\n ],\n [\n 23.28428565006891,\n 11.810719747300766\n ],\n [\n 25.961560823287215,\n 32.42941470582909\n ],\n [\n 6.142411713899236,\n 37.98242670846139\n ],\n [\n -8.211384638428427,\n 36.27077305284628\n ],\n [\n -18.442986479614405,\n 21.57365946263323\n ],\n [\n -15.922624317482331,\n 9.250741295049338\n ],\n [\n -6.7555044485721965,\n 3.0864107262271716\n ],\n [\n 10.41107660211324,\n 2.118513429359794\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, South Dakota 57198-0001
The Floridan aquifer system (FAS) consists of the Upper Floridan aquifer (UFA), an intervening confining unit of highly variable properties, and the Lower Floridan aquifer (LFA). The UFA and LFA are primarily composed of Paleocene- to Oligocene-age carbonate rocks that include, locally, Upper Cretaceous rocks. The FAS extends from coastal areas in southeastern South Carolina and continues southward and westward across the coastal plain of Georgia and Alabama, and underlies all of Florida. The thickness of the FAS varies from less than 100 feet (ft) in aquifer outcrop areas of South Carolina to about 1,700 ft near the city of Brunswick, Georgia.
Locally, in southeastern Georgia and the Brunswick– Glynn County area, the UFA consists of an upper water-bearing zone (UWBZ) and a lower water-bearing zone (LWBZ), as identified by Wait and Gregg (1973), with aquifer test data indicating the upper zone has higher productivity than the lower zone. Near the city of Brunswick, the LFA is composed of two permeable zones: an early middle Eocene-age upper permeable zone (UPZ) and a highly permeable lower zone of limestone (LPZ) of Paleocene and Late Cretaceous age that includes a deeply buried, cavernous, saline water-bearing unit known as the Fernandina permeable zone. Maslia and Prowell (1990) inferred the presence of major northeast–southwest trending faults through the downtown Brunswick area based on structural analysis of geophysical data, northeastward elongation of the potentiometric surface of the UFA, and breaches in the local confining unit that influence the area of chloride contamination. Pronounced horizontal and vertical hydraulic head gradients, caused by pumping in the UFA, allow saline water from the FPZ to migrate upward into the UFA through this system of faults and conduits.
Saltwater was first detected in the FAS in wells completed in the UFA near the southern part of the city of Brunswick in late 1957. By the 1970s, a plume of groundwater with high chloride concentrations had migrated northward toward two major industrial pumping centers, and since 1965, chloride concentrations have steadily increased in the northern part of the city. In 1978, data obtained from a 2,720-ft-deep test well (33H188) drilled south of the city showed water with a chloride concentration of 33,000 milligrams per liter (mg/L), suggesting the saltwater source was located below the UFA in the Fernandina permeable zone (FPZ) of the LFA.
All U.S. Geological Survey (USGS) data collected for this study, including groundwater levels in wells and water-chemistry data, are available in the USGS National Water Information System.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171010","collaboration":"In cooperation with the Brunswick-Glynn County Joint Water and Sewer Commission","usgsCitation":"Cherry, G.S., and Peck, M.F., 2017, Saltwater intrusion in the Floridan aquifer system near downtown Brunswick, Georgia, 1957–2015: U.S. Geological Survey Open-File Report 2017–2010, 10 p., https://doi.org/10.3133/ofr20171010.","productDescription":"10 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-075326","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":334883,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1010/ofr20171010.pdf","text":"Report","size":"1.61 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1010"},{"id":334882,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1010/coverthb1.jpg"}],"country":"United States","state":"Georgia","city":"Brunswick","otherGeospatial":"Floridan Aquifer System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.53263092041016,\n 31.11262192177511\n ],\n [\n -81.46190643310547,\n 31.11262192177511\n ],\n [\n -81.46190643310547,\n 31.207516037787602\n ],\n [\n -81.53263092041016,\n 31.207516037787602\n ],\n [\n -81.53263092041016,\n 31.11262192177511\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
Or visit the South Atlantic Water Science Center Website at
https://www.usgs.gov/water/southatlantic/
A methodology for developing a representative set of storm scenarios based on historical wave buoy and tide gauge data for a region at the Chandeleur Islands, Louisiana, was developed by the U.S. Geological Survey. The total water level was calculated for a 10-year period and analyzed against existing topographic data to identify when storm-induced wave action would affect island morphology. These events were categorized on the basis of the threshold of total water level and duration to create a set of storm scenarios that were simulated, using a high-fidelity, process-based, morphologic evolution model, on an idealized digital elevation model of the Chandeleur Islands. The simulated morphological changes resulting from these scenarios provide a range of impacts that can help coastal managers determine resiliency of proposed or existing coastal structures and identify vulnerable areas within those structures.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171009","usgsCitation":"Mickey, R.C., Long, J.W., Plant, N.G., Thompson, D.M., and Dalyander, P.S., 2017, A methodology for modeling barrier island storm-impact scenarios (ver. 1.1, March 2017): U.S. Geological Survey Open-File Report 2017–1009, 17 p., https://doi.org/10.3133/ofr20171009.","productDescription":"iv, 17 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":337876,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2017/1009/versionHist.txt","linkFileType":{"id":2,"text":"txt"}},{"id":334864,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1009/coverthb2.jpg"},{"id":334865,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1009/ofr20171009.pdf","text":"Report","size":"2.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1009"},{"id":334866,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F72F7KJK","text":"USGS data release","description":"USGS data release","linkHelpText":"Storm-Impact Scenario XBeach Model Input and Results"}],"country":"United States","state":"Louisiana","otherGeospatial":"Chandeleur Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.116667,\n 31\n ],\n [\n -86.95,\n 31\n ],\n [\n -86.95,\n 28.583333\n ],\n [\n -89.116667,\n 28.583333\n ],\n [\n -89.116667,\n 31\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted February 16, 2017; Version 1.1: March 29, 2017","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
http://coastal.er.usgs.gov/
Our main objectives are to (1) learn if pore-evolution models developed from marine mudrocks can be directly applied to lacustrine mudrocks, (2) investigate what controls the different pore types and sizes of Chang 7 organic matter (OM)-rich argillaceous mudstones of the Upper Triassic Yanchang Formation, and (3) describe the texture, fabric, mineralogy, and thermal maturity variation in the Chang 7 mudstones. Lacustrine mudstones from nine cored wells along a depositional dip in the southeastern Ordos Basin, China, were investigated. Helium porosimetry, nitrogen adsorption, and field-emission scanning electron microscopy of Ar-ion milled samples were applied. Measured average total porosity of samples from a proximal to distal transect (
Digital flood-inundation maps for a 4.1-mile reach of the Big Blue River at Shelbyville, Indiana, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. The floodinundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at https://water. usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the Big Blue River at Shelbyville, Ind. (station number 03361500). Near-real-time stages at this streamgage may be obtained from the USGS National Water Information System at https://waterdata. usgs.gov/ or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at https://water.weather.gov/ ahps/, which also forecasts flood hydrographs at this site (SBVI3). Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the most current stage-discharge relation at the Big Blue River at Shelbyville, Ind., streamgage. The calibrated hydraulic model was then used to compute 12 water-surface profiles for flood stages referenced to the streamgage datum and ranging from 9.0 feet, or near bankfull, to 19.4 feet, the highest stage of the current stage-discharge rating curve. The simulated water-surface profiles were then combined with a Geographic Information System digital elevation model (derived from light detection and ranging [lidar] data having a 0.98-foot vertical accuracy and 4.9-foot horizontal resolution) to delineate the area flooded at each water level. The availability of these maps, along with Internet information regarding current stage from the USGS streamgage at the Big Blue River at Shelbyville, Ind., and forecasted stream stages from the NWS, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165166","collaboration":"Prepared in cooperation with the Indiana Office of Community and Rural Affairs","usgsCitation":"Fowler, K.K., 2017, Flood-inundation maps for the Big Blue River at Shelbyville, Indiana: U.S. Geological Survey Scientific Investigations Report 2016–5166, 11 p., https://doi.org/10.3133/sir20165166.","productDescription":"Report: vi, 11 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-077203","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":335209,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7WH2N48","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":" Big Blue River at Shelbyville, Indiana, flood-inundation geospatial datasets"},{"id":335207,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5166/coverthb.jpg"},{"id":335208,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5166/sir20165166.pdf","text":"Report","size":"1.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5166"}],"country":"United States","state":"Indiana","city":"Shelbyville","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.8248519897461,\n 39.504305605954634\n ],\n [\n -85.7457160949707,\n 39.504305605954634\n ],\n [\n -85.7457160949707,\n 39.5546183524477\n ],\n [\n -85.8248519897461,\n 39.5546183524477\n ],\n [\n -85.8248519897461,\n 39.504305605954634\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278–1996
Glacier hypsometry provides a first‐order approach for assessing a glacier's response to climate forcings. We couple the Randolph Glacier Inventory to a suite of in situ observations and climate model output to examine potential change for the ∼27,000 glaciers in Alaska and northwest Canada through the end of the 21st century. By 2100, based on Representative Concentration Pathways (RCPs) 4.5–8.5 forcings, summer temperatures are predicted to increase between +2.1 and +4.6°C, while solid precipitation (snow) is predicted to decrease by −6 to −11%, despite a +9 to +21% increase in total precipitation. Snow is predicted to undergo a pronounced decrease in the fall, shifting the start of the accumulation season back by ∼1 month. In response to these forcings, the regional equilibrium line altitude (ELA) may increase by +105 to +225 m by 2100. The mass balance sensitivity to this increase is highly variable, with the most substantive impact for glaciers with either limited elevation ranges (often small (<1 km2) glaciers, which account for 80% of glaciers in the region) or those with top‐heavy geometries, like icefields. For more than 20% of glaciers, future ELAs, given RCP 6.0 forcings, will exceed the maximum elevation of the glacier, resulting in their eventual demise, while for others, accumulation area ratios will decrease by >60%. Our results highlight the first‐order control of hypsometry on individual glacier response to climate change, and the variability that hypsometry introduces to a regional response to a coherent climate perturbation.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016EF000479","usgsCitation":"Mcgrath, D., Sass, L., O’Neel, S., Arendt, A.A., and Kienholz, C., 2017, Hypsometric control on glacier mass balance sensitivity in Alaska and northwest Canada: Earth's Future, v. 5, no. 3, p. 324-336, https://doi.org/10.1002/2016EF000479.","productDescription":"13 p.","startPage":"324","endPage":"336","ipdsId":"IP-080924","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":470065,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016ef000479","text":"Publisher Index Page"},{"id":369088,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, British Columbia, Yukon","otherGeospatial":"Gulf of Alaska watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -162.7734375,\n 60.108670463036\n ],\n [\n -130.869140625,\n 51.781435604431195\n ],\n [\n -123.134765625,\n 55.02802211299252\n ],\n [\n -123.04687499999999,\n 56.36525013685606\n ],\n [\n -128.671875,\n 66.75724984139227\n ],\n [\n -136.669921875,\n 66.44310650816469\n ],\n [\n -162.7734375,\n 60.108670463036\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"5","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Mcgrath, Daniel 0000-0002-9462-6842 dmcgrath@usgs.gov","orcid":"https://orcid.org/0000-0002-9462-6842","contributorId":145635,"corporation":false,"usgs":true,"family":"Mcgrath","given":"Daniel","email":"dmcgrath@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":774886,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sass, Louis C. 0000-0003-4677-029X lsass@usgs.gov","orcid":"https://orcid.org/0000-0003-4677-029X","contributorId":3555,"corporation":false,"usgs":true,"family":"Sass","given":"Louis C.","email":"lsass@usgs.gov","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":774887,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Neel, Shad 0000-0002-9185-0144 soneel@usgs.gov","orcid":"https://orcid.org/0000-0002-9185-0144","contributorId":166740,"corporation":false,"usgs":true,"family":"O’Neel","given":"Shad","email":"soneel@usgs.gov","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":774888,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arendt, Anthony A.","contributorId":200572,"corporation":false,"usgs":false,"family":"Arendt","given":"Anthony","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":774889,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kienholz, C.","contributorId":146539,"corporation":false,"usgs":false,"family":"Kienholz","given":"C.","email":"","affiliations":[{"id":13097,"text":"Geophysical Institute, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":774890,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70182111,"text":"70182111 - 2017 - Testing model parameters for wave‐induced dune erosion using observations from Hurricane Sandy","interactions":[],"lastModifiedDate":"2018-03-26T13:53:02","indexId":"70182111","displayToPublicDate":"2017-02-16T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Testing model parameters for wave‐induced dune erosion using observations from Hurricane Sandy","docAbstract":"Models of dune erosion depend on a set of assumptions that dictate the predicted evolution of dunes throughout the duration of a storm. Lidar observations made before and after Hurricane Sandy at over 800 profiles with diverse dune elevations, widths, and volumes are used to quantify specific dune erosion model parameters including the dune face slope, which controls dune avalanching, and the trajectory of the dune toe, which controls dune migration. Wave‐impact models of dune erosion assume a vertical dune face and erosion of the dune toe along the foreshore beach slope. Observations presented here show that these assumptions are not always valid and require additional testing if these models are to be used to predict coastal vulnerability for decision‐making purposes. Observed dune face slopes steepened by 43% yet did not become vertical faces, and only 50% of the dunes evolved along a trajectory similar to the foreshore beach slope. Observations also indicate that dune crests were lowered during dune erosion. Moreover, analysis showed a correspondence between dune lowering and narrower beaches, smaller dune volumes, and/or longer wave impact.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016GL071991","usgsCitation":"Overbeck, J.R., Long, J.W., and Stockdon, H.F., 2017, Testing model parameters for wave‐induced dune erosion using observations from Hurricane Sandy: Geophysical Research Letters, v. 44, no. 2, p. 937-945, https://doi.org/10.1002/2016GL071991.","productDescription":"9 p.","startPage":"937","endPage":"945","ipdsId":"IP-082655","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":335723,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, New Jersey, New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.618896484375,\n 37.735969208590504\n ],\n [\n -71.707763671875,\n 37.735969208590504\n ],\n [\n -71.707763671875,\n 40.90520969727358\n ],\n [\n -75.618896484375,\n 40.90520969727358\n ],\n [\n -75.618896484375,\n 37.735969208590504\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-27","publicationStatus":"PW","scienceBaseUri":"58a6c826e4b025c464286252","chorus":{"doi":"10.1002/2016gl071991","url":"http://dx.doi.org/10.1002/2016gl071991","publisher":"Wiley-Blackwell","authors":"Overbeck J. R., Long J. W., Stockdon H. F.","journalName":"Geophysical Research Letters","publicationDate":"1/27/2017","auditedOn":"2/8/2017","publiclyAccessibleDate":"1/27/2017"},"contributors":{"authors":[{"text":"Overbeck, Jacquelyn R.","contributorId":181813,"corporation":false,"usgs":false,"family":"Overbeck","given":"Jacquelyn","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":669638,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, Joseph W. 0000-0003-2912-1992 jwlong@usgs.gov","orcid":"https://orcid.org/0000-0003-2912-1992","contributorId":3303,"corporation":false,"usgs":true,"family":"Long","given":"Joseph","email":"jwlong@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":669637,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stockdon, Hilary F. 0000-0003-0791-4676 hstockdon@usgs.gov","orcid":"https://orcid.org/0000-0003-0791-4676","contributorId":2153,"corporation":false,"usgs":true,"family":"Stockdon","given":"Hilary","email":"hstockdon@usgs.gov","middleInitial":"F.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":669639,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70181798,"text":"ofr20171014 - 2017 - Hurricane Sandy washover deposits on Fire Island, New York","interactions":[],"lastModifiedDate":"2017-02-17T08:26:09","indexId":"ofr20171014","displayToPublicDate":"2017-02-16T00:00:00","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":"2017-1014","title":"Hurricane Sandy washover deposits on Fire Island, New York","docAbstract":"Washover deposits on Fire Island, New York, from Hurricane Sandy in 2012 were investigated a year after the storm to document the sedimentary characteristics of hurricane washover features. Sediment data collected in the field includes stratigraphic descriptions and photos from trenches, bulk sediment samples, U-channels, and gouge and push cores. Samples and push cores were further analyzed in the laboratory for grain size, density variations using x-ray computed tomography (CT), and surface microtexture using a scanning electron microscope (SEM). Elevation profiles of washover features were measured using Differential Global Positioning System (DGPS) with Real Time Kinematic processing. The DGPS elevations were compared to lidar (light detection and ranging) data from pre- and post-Sandy surveys to assess the degree to which washover deposit thicknesses changed within the year following deposition. Hurricane Sandy washover deposits as much as 1 meter thick were observed in trenches. Initial results show that the upper parts of the deposits have been reworked significantly in some places by wind, but there are still areas where the deposits are almost entirely intact. Where mostly intact, the washover deposits consist of massive or weakly laminated sand near the base, overlain by more strongly laminated sands.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171014","usgsCitation":"La Selle, S.M., Lunghino, B.D., Jaffe, B.E., Gelfenbaum, G., and Costa, P.J.M., 2017, Hurricane Sandy washover deposits on Fire Island, New York: U.S. Geological Survey Open-File Report 2017–1014, 30 p., https://doi.org/10.3133/ofr20171014.","productDescription":"v, 30 p.","onlineOnly":"Y","ipdsId":"IP-059939","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":335669,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1014/ofr20171014.pdf","text":"Report","size":"7.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1014"},{"id":335668,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1014/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Fire Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.333333,\n 40.916667\n ],\n [\n -72.45,\n 40.916667\n ],\n [\n -72.45,\n 40.583333\n ],\n [\n -73.333333,\n 40.583333\n ],\n [\n -73.333333,\n 40.916667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Pacific Coastal and Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission Street
Santa Cruz, CA 95060
https://walrus.wr.usgs.gov/
Nutria (Myocaster coypus), invasive, semi-aquatic rodents native to South America, were introduced into Maryland near Blackwater National Wildlife Refuge (BNWR) in 1943. Irruptive population growth, expansion, and destructive feeding habits resulted in the destruction of thousands of acres of emergent marshes at and surrounding BNWR. In 2002, a partnership of federal, state and private entities initiated an eradication campaign to protect remaining wetlands from further damage and facilitate the restoration of coastal wetlands throughout the Chesapeake Bay region. Program staff removed nearly 14,000 nutria from five infested watersheds in a systematic trapping and hunting program between 2002 and 2014. As part of ongoing surveillance activities, the Chesapeake Bay Nutria Eradication Project uses a variety of tools to detect and remove nutria. Project staff developed a floating raft, or monitoring platform, to determine site occupancy. These platforms are placed along waterways and checked periodically for evidence of nutria visitation. We evaluated the effectiveness of monitoring platforms and three associated detection methods: hair snares, presence of scat, and trail cameras. Our objectives were to (1) determine if platform placement on land or water influenced nutria visitation rates, (2) determine if the presence of hair snares influenced visitation rates, and (3) determine method-specific detection probabilities. Our analyses indicated that platforms placed on land were 1.5–3.0 times more likely to be visited than those placed in water and that platforms without snares were an estimated 1.7–3.7 times more likely to be visited than those with snares. Although the presence of snares appears to have discouraged visitation, seasonal variation may confound interpretation of these results. Scat was the least effective method of determining nutria visitation, while hair snares were as effective as cameras. Estimated detection probabilities provided by occupancy modeling were 0.73 for hair snares, 0.71 for cameras and 0.40 for scat. We recommend the use of hair snares on monitoring platforms as they are the most cost-effective and reliable detection method available at this time. Future research should focus on determining the cause for the observed decrease in nutria visits after snares were applied.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-016-1312-1","usgsCitation":"Pepper, M.A., Herrmann, V., Hines, J.E., Nichols, J.D., and Kendrot, S.R., 2017, Evaluation of nutria (Myocastor coypus) detection methods in Maryland, USA: Biological Invasions, v. 19, no. 3, p. 831-841, https://doi.org/10.1007/s10530-016-1312-1.","productDescription":"11 p.","startPage":"831","endPage":"841","ipdsId":"IP-080917","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":335674,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Wicomico River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.7723617553711,\n 38.272419002497735\n ],\n [\n -75.6748580932617,\n 38.272419002497735\n ],\n [\n -75.6748580932617,\n 38.347580040410506\n ],\n [\n -75.7723617553711,\n 38.347580040410506\n ],\n [\n -75.7723617553711,\n 38.272419002497735\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-07","publicationStatus":"PW","scienceBaseUri":"58a6c828e4b025c464286258","contributors":{"authors":[{"text":"Pepper, Margaret A.","contributorId":181781,"corporation":false,"usgs":false,"family":"Pepper","given":"Margaret","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":669510,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herrmann, Valentine","contributorId":181782,"corporation":false,"usgs":false,"family":"Herrmann","given":"Valentine","email":"","affiliations":[],"preferred":false,"id":669511,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hines, James E. 0000-0001-5478-7230 jhines@usgs.gov","orcid":"https://orcid.org/0000-0001-5478-7230","contributorId":146530,"corporation":false,"usgs":true,"family":"Hines","given":"James","email":"jhines@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":669509,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":140652,"corporation":false,"usgs":true,"family":"Nichols","given":"James","email":"jnichols@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":669512,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kendrot, Stephen R","contributorId":181783,"corporation":false,"usgs":false,"family":"Kendrot","given":"Stephen","email":"","middleInitial":"R","affiliations":[],"preferred":false,"id":669513,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70182083,"text":"70182083 - 2017 - Neisseria arctica sp. nov. isolated from nonviable eggs of greater white-fronted geese (Anser albifrons) in Arctic Alaska","interactions":[],"lastModifiedDate":"2017-06-07T10:34:20","indexId":"70182083","displayToPublicDate":"2017-02-16T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2076,"text":"International Journal of Systematic and Evolutionary Microbiology","active":true,"publicationSubtype":{"id":10}},"title":"Neisseria arctica sp. nov. isolated from nonviable eggs of greater white-fronted geese (Anser albifrons) in Arctic Alaska","docAbstract":"During the summers of 2013 and 2014, isolates of a novel Gram-negative coccus in the Neisseria genus were obtained from the contents of nonviable greater white-fronted goose (Anser albifrons) eggs on the Arctic Coastal Plain of Alaska. We used a polyphasic approach to determine whether these isolates represent a novel species. 16S rRNA gene sequences, 23S rRNA gene sequences, and chaperonin 60 gene sequences suggested that these Alaskan isolates are members of a distinct species that is most closely related to Neisseria canis, N. animaloris, and N. shayeganii. Analysis of the rplF gene additionally showed that our isolates are unique and most closely related to N. weaveri. Average nucleotide identity of the whole genome sequence of our type strain was between 71.5% and 74.6% compared to close relatives, further supporting designation as a novel species. Fatty acid methyl ester analysis showed a predominance of C14:0, C16:0, and C16:1ω7c fatty acids. Finally, biochemical characteristics distinguished our isolates from other Neisseria species. The name Neisseria arctica (type strain KH1503T = ATCC TSD-57T = DSM 103136T) is proposed.
","language":"English","publisher":"Microbiology Society","doi":"10.1099/ijsem.0.001773","usgsCitation":"Hansen, C.M., Himschoot, E., Hare, R.F., Meixell, B.W., Van Hemert, C.R., and Hueffer, K., 2017, Neisseria arctica sp. nov. isolated from nonviable eggs of greater white-fronted geese (Anser albifrons) in Arctic Alaska: International Journal of Systematic and Evolutionary Microbiology, v. 67, p. 1115-1119, https://doi.org/10.1099/ijsem.0.001773.","productDescription":"5 p.","startPage":"1115","endPage":"1119","ipdsId":"IP-075282","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":461729,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.1099/ijsem.0.001773","text":"Publisher Index Page"},{"id":335676,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"67","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58a6c829e4b025c46428625a","contributors":{"authors":[{"text":"Hansen, Cristina M.","contributorId":166985,"corporation":false,"usgs":false,"family":"Hansen","given":"Cristina","email":"","middleInitial":"M.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":669489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Himschoot, Elizabeth","contributorId":181769,"corporation":false,"usgs":false,"family":"Himschoot","given":"Elizabeth","email":"","affiliations":[],"preferred":false,"id":669490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hare, Rebekah F.","contributorId":166986,"corporation":false,"usgs":false,"family":"Hare","given":"Rebekah","email":"","middleInitial":"F.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":669491,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meixell, Brandt W. 0000-0002-6738-0349 bmeixell@usgs.gov","orcid":"https://orcid.org/0000-0002-6738-0349","contributorId":138716,"corporation":false,"usgs":true,"family":"Meixell","given":"Brandt","email":"bmeixell@usgs.gov","middleInitial":"W.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":669487,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Van Hemert, Caroline R. 0000-0002-6858-7165 cvanhemert@usgs.gov","orcid":"https://orcid.org/0000-0002-6858-7165","contributorId":3592,"corporation":false,"usgs":true,"family":"Van Hemert","given":"Caroline","email":"cvanhemert@usgs.gov","middleInitial":"R.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":669488,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hueffer, Karsten","contributorId":139938,"corporation":false,"usgs":false,"family":"Hueffer","given":"Karsten","email":"","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":669492,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70182097,"text":"70182097 - 2017 - Potential influence of wildfire in modulating climate-induced forest redistribution in a central Rocky Mountain landscape","interactions":[],"lastModifiedDate":"2017-11-22T17:02:42","indexId":"70182097","displayToPublicDate":"2017-02-16T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1460,"text":"Ecological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Potential influence of wildfire in modulating climate-induced forest redistribution in a central Rocky Mountain landscape","docAbstract":"Introduction
Climate change is expected to impose significant tension on the geographic distribution of tree species. Yet, tree species range shifts may be delayed by their long life spans, capacity to withstand long periods of physiological stress, and dispersal limitations. Wildfire could theoretically break this biological inertia by killing forest canopies and facilitating species redistribution under changing climate. We investigated the capacity of wildfire to modulate climate-induced tree redistribution across a montane landscape in the central Rocky Mountains under three climate scenarios (contemporary and two warmer future climates) and three wildfire scenarios (representing historical, suppressed, and future fire regimes).
Methods
Distributions of four common tree species were projected over 90 years by pairing a climate niche model with a forest landscape simulation model that simulates species dispersal, establishment, and mortality under alternative disturbance regimes and climate scenarios.
Results
Three species (Douglas-fir, lodgepole pine, subalpine fir) declined in abundance over time, due to climate-driven contraction in area suitable for establishment, while one species (ponderosa pine) was unable to exploit climate-driven expansion of area suitable for establishment. Increased fire frequency accelerated declines in area occupied by Douglas-fir, lodgepole pine, and subalpine fir, and it maintained local abundance but not range expansion of ponderosa pine.
Conclusions
Wildfire may play a larger role in eliminating these conifer species along trailing edges of their distributions than facilitating establishment along leading edges, in part due to dispersal limitations and interspecific competition, and future populations may increasingly depend on persistence in locations unfavorable for their establishment.
We investigated the leachability of elements from mill rejects from the high-sulfur, bituminous Upper Pennsylvanian Pittsburgh coal, using the synthetic groundwater leaching procedure (SGLP), long-term leaching (LTL), and the U.S. Environmental Protection Agency's (EPA's) toxicity characteristic leaching procedure (TCLP), and compared their leaching behavior with that of three coal combustion products (CCPs)—bottom ash, economizer fly ash, and fly ash—from the same coal. None of the environmentally hazardous Resource Conservation and Recovery Act of 1976 (RCRA) metals analyzed in the leachates from the mill rejects or the CCPs exceeded U.S. EPA toxicity characteristics (As, Ba, Cd, Cr, Hg, Pb, and Se). Most trace elements leached the least from mill rejects and bottom ash and leached the most from fly ash. The elements Ca, Co, Mg, Mn, and Sr, however, were more concentrated in mill reject leachates than CCP leachates. Most trace elements increased in concentration with increasing SGLP and LTL leaching duration, but As and V decreased in concentration with time in mill reject leachates, suggesting sorption or precipitation of these elements was occurring.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2017.01.002","usgsCitation":"Jones, K.B., and Ruppert, L.F., 2017, Leaching of trace elements from Pittsburgh coal mill rejects compared with coal combustion products from a coal-fired power plant in Ohio, USA: International Journal of Coal Geology, v. 171, p. 130-141, https://doi.org/10.1016/j.coal.2017.01.002.","productDescription":"12 p.","startPage":"130","endPage":"141","ipdsId":"IP-079108","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":357293,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","volume":"171","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bc031d2e4b0fc368eb53a4b","contributors":{"authors":[{"text":"Jones, Kevin B. 0000-0002-6386-2623 kevinjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6386-2623","contributorId":565,"corporation":false,"usgs":true,"family":"Jones","given":"Kevin","email":"kevinjones@usgs.gov","middleInitial":"B.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":744888,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ruppert, Leslie F. 0000-0002-7453-1061 lruppert@usgs.gov","orcid":"https://orcid.org/0000-0002-7453-1061","contributorId":660,"corporation":false,"usgs":true,"family":"Ruppert","given":"Leslie","email":"lruppert@usgs.gov","middleInitial":"F.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":744889,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70181023,"text":"fs20173008 - 2017 - Refining previous estimates of groundwater outflows from the Medina/Diversion Lake system, San Antonio area, Texas","interactions":[],"lastModifiedDate":"2017-02-15T18:02:00","indexId":"fs20173008","displayToPublicDate":"2017-02-15T16:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3008","title":"Refining previous estimates of groundwater outflows from the Medina/Diversion Lake system, San Antonio area, Texas","docAbstract":"In 2016, the U.S. Geological Survey (USGS), in cooperation with the San Antonio Water System, began a study to refine previously derived estimates of groundwater outflows from Medina and Diversion Lakes in south-central Texas near San Antonio. When full, Medina and Diversion Lakes (hereinafter referred to as the Medina/Diversion Lake system) (fig. 1) impound approximately 255,000 acre-feet and 2,555 acre-feet of water, respectively.
Most recharge to the Edwards aquifer occurs as seepage from streams as they cross the outcrop (recharge zone) of the aquifer (Slattery and Miller, 2017). Groundwater outflows from the Medina/Diversion Lake system have also long been recognized as a potentially important additional source of recharge. Puente (1978) published methods for estimating monthly and annual estimates of the potential recharge to the Edwards aquifer from the Medina/Diversion Lake system. During October 1995–September 1996, the USGS conducted a study to better define short-term rates of recharge and to reduce the error and uncertainty associated with estimates of monthly recharge from the Medina/Diversion Lake system (Lambert and others, 2000). As a followup to that study, Slattery and Miller (2017) published estimates of groundwater outflows from detailed water budgets for the Medina/Diversion Lake system during 1955–1964, 1995–1996, and 2001–2002. The water budgets were compiled for selected periods during which time the water-budget components were inferred to be relatively stable and the influence of precipitation, stormwater runoff, and changes in storage were presumably minimal. Linear regression analysis techniques were used by Slattery and Miller (2017) to assess the relation between the stage in Medina Lake and groundwater outflows from the Medina/Diversion Lake system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173008","collaboration":"Prepared in cooperation with the San Antonio Water System","usgsCitation":"Slattery, R.N., Asquith, W.H., and Gordon, J.D., 2017, Refining previous estimates of groundwater outflows from the Medina/Diversion Lake system, San Antonio area, Texas: U.S. Geological Survey Fact Sheet 2017–3008, 2 p., https://doi.org/10.3133/fs20173008.","productDescription":"Report: 2 p.; Data Release","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-082626","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":335145,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ZS2TNF","text":"USGS Data Release","description":"USGS data release","linkHelpText":"Reanalysis of the Medina/Diversion Lake System Water-Budget, with Estimated Recharge to Edwards Aquifer, San Antonio Area, Texas"},{"id":335296,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/sir20045209","text":"SIR 2004–5209","size":"4.22 MB","description":"SIR 2004–5209"},{"id":335143,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3008/coverthb.jpg"},{"id":335144,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3008/fs20173008.pdf","text":"Fact Sheet","size":"332 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017–3008"}],"country":"United States","state":"Texas","otherGeospatial":" Medina River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.05,\n 29.5\n ],\n [\n -98.85,\n 29.5\n ],\n [\n -98.85,\n 29.7\n ],\n [\n -99.05,\n 29.7\n ],\n [\n -99.05,\n 29.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754
The Rapid Land Cover Mapper is an Esri ArcGIS® Desktop add-in, which was created as an alternative to automated or semiautomated mapping methods. Based on a manual photo interpretation technique, the tool facilitates mapping over large areas and through time, and produces time-series raster maps and associated statistics that characterize the changing landscapes. The Rapid Land Cover Mapper add-in can be used with any imagery source to map various themes (for instance, land cover, soils, or forest) at any chosen mapping resolution. The user manual contains all essential information for the user to make full use of the Rapid Land Cover Mapper add-in. This manual includes a description of the add-in functions and capabilities, and step-by-step procedures for using the add-in. The Rapid Land Cover Mapper add-in was successfully used by the U.S. Geological Survey West Africa Land Use Dynamics team to accurately map land use and land cover in 17 West African countries through time (1975, 2000, and 2013).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171012","usgsCitation":"Cotillon, S.E., and Mathis, M.L., 2017, Mapping land cover through time with the Rapid Land Cover Mapper—Documentation and user manual: U.S. Geological Survey Open File Report 2017–1012, 23 p., https://doi.org/10.3133/ofr20171012.","productDescription":"Report: vi, 23; Appendix","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":335429,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1012/ofr20171012.pdf","text":"Report","size":"13.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1012"},{"id":335428,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1012/coverthb.jpg"},{"id":335432,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1012/ofr20171012_appendixtable1-1.xlsx","text":"Appendix Table 1–1","size":"18.8 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2017–1012 Appendix Table 1–1"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Layers of strong geologic contrast within the unsaturated zone can control recharge and contaminant transport to underlying aquifers. Slow diffuse flow in certain geologic layers, and rapid preferential flow in others, complicates the prediction of vertical and lateral fluxes. A simple model is presented, designed to use limited geological site information to predict these critical subsurface processes in response to a sustained infiltration source. The model is developed and tested using site-specific information from the Idaho National Laboratory in the Eastern Snake River Plain (ESRP), USA, where there are natural and anthropogenic sources of high-volume infiltration from floods, spills, leaks, wastewater disposal, retention ponds, and hydrologic field experiments. The thick unsaturated zone overlying the ESRP aquifer is a good example of a sharply stratified unsaturated zone. Sedimentary interbeds are interspersed between massive and fractured basalt units. The combination of surficial sediments, basalts, and interbeds determines the water fluxes through the variably saturated subsurface. Interbeds are generally less conductive, sometimes causing perched water to collect above them. The model successfully predicts the volume and extent of perching and approximates vertical travel times during events that generate high fluxes from the land surface. These developments are applicable to sites having a thick, geologically complex unsaturated zone of substantial thickness in which preferential and diffuse flow, and perching of percolated water, are important to contaminant transport or aquifer recharge.
","language":"English","publisher":"Springer","doi":"10.1007/s10040-016-1496-6","usgsCitation":"Nimmo, J.R., Creasey, K.M., Perkins, K., and Mirus, B.B., 2017, Preferential flow, diffuse flow, and perching in an interbedded fractured-rock unsaturated zone: Hydrogeology Journal, v. 25, no. 2, p. 421-444, https://doi.org/10.1007/s10040-016-1496-6.","productDescription":"24 p.","startPage":"421","endPage":"444","ipdsId":"IP-065100","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":335550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Eastern Snake River Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.333333,\n 44.083333\n ],\n [\n -112.333333,\n 44.083333\n ],\n [\n -112.333333,\n 43.25\n ],\n [\n -113.333333,\n 43.25\n ],\n [\n -113.333333,\n 44.083333\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-26","publicationStatus":"PW","scienceBaseUri":"58a576bee4b057081a24ed30","contributors":{"authors":[{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":654384,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Creasey, Kaitlyn M kcreasey@usgs.gov","contributorId":5799,"corporation":false,"usgs":true,"family":"Creasey","given":"Kaitlyn","email":"kcreasey@usgs.gov","middleInitial":"M","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":654385,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perkins, Kimberlie 0000-0001-8349-447X kperkins@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-447X","contributorId":138544,"corporation":false,"usgs":true,"family":"Perkins","given":"Kimberlie","email":"kperkins@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":654386,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"preferred":true,"id":654387,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70182070,"text":"70182070 - 2017 - Differential responses of dinitrogen fixation, diazotrophic cyanobacteria and ammonia oxidation reveal a potential warming-induced imbalance of the N-cycle in biological soil crusts","interactions":[],"lastModifiedDate":"2017-02-15T17:54:43","indexId":"70182070","displayToPublicDate":"2017-02-15T00:00:00","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":"Differential responses of dinitrogen fixation, diazotrophic cyanobacteria and ammonia oxidation reveal a potential warming-induced imbalance of the N-cycle in biological soil crusts","docAbstract":"N2 fixation and ammonia oxidation (AO) are the two most important processes in the nitrogen (N) cycle of biological soil crusts (BSCs). We studied the short-term response of acetylene reduction assay (ARA) rates, an indicator of potential N2 fixation, and AO rates to temperature (T, -5°C to 35°C) in BSC of different successional stages along the BSC ecological succession and geographic origin (hot Chihuahuan and cooler Great Basin deserts). ARA in all BSCs increased with T until saturation occurred between 15 and 20°C, and declined at 30–35°C. Culture studies using cyanobacteria isolated from these crusts indicated that the saturating effect was traceable to their inability to grow well diazotrophically within the high temperature range. Below saturation, temperature response was exponential, with Q10 significantly different in the two areas (~ 5 for Great Basin BSCs; 2–3 for Chihuahuan BSCs), but similar between the two successional stages. However, in contrast to ARA, AO showed a steady increase to 30–35°C in Great Basin, and Chihuhuan BSCs showed no inhibition at any tested temperature. The T response of AO also differed significantly between Great Basin (Q10 of 4.5–4.8) and Chihuahuan (Q10 of 2.4–2.6) BSCs, but not between successional stages. Response of ARA rates to T did not differ from that of AO in either desert. Thus, while both processes scaled to T in unison until 20°C, they separated to an increasing degree at higher temperature. As future warming is likely to occur in the regions where BSCs are often the dominant living cover, this predicted decoupling is expected to result in higher proportion of nitrates in soil relative to ammonium. As nitrate is more easily lost as leachate or to be reduced to gaseous forms, this could mean a depletion of soil N over large landscapes globally.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0164932","usgsCitation":"Zhou, X., Smith, H.J., Giraldo Silva, A., Belnap, J., and Garcia-Pichel, F., 2017, Differential responses of dinitrogen fixation, diazotrophic cyanobacteria and ammonia oxidation reveal a potential warming-induced imbalance of the N-cycle in biological soil crusts: PLoS ONE, v. 11, no. 10, e0164932; 15 p., https://doi.org/10.1371/journal.pone.0164932.","productDescription":"e0164932; 15 p.","ipdsId":"IP-068683","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":470068,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0164932","text":"Publisher Index Page"},{"id":335655,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-24","publicationStatus":"PW","scienceBaseUri":"58a576b7e4b057081a24ed0a","contributors":{"authors":[{"text":"Zhou, Xiaobing","contributorId":181757,"corporation":false,"usgs":false,"family":"Zhou","given":"Xiaobing","email":"","affiliations":[],"preferred":false,"id":669452,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Hilda J. 0000-0001-5775-1401 hsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-5775-1401","contributorId":4469,"corporation":false,"usgs":true,"family":"Smith","given":"Hilda","email":"hsmith@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":669453,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Giraldo Silva, Ana","contributorId":181758,"corporation":false,"usgs":false,"family":"Giraldo Silva","given":"Ana","email":"","affiliations":[],"preferred":false,"id":669454,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Belnap, Jayne 0000-0001-7471-2279 jayne_belnap@usgs.gov","orcid":"https://orcid.org/0000-0001-7471-2279","contributorId":1332,"corporation":false,"usgs":true,"family":"Belnap","given":"Jayne","email":"jayne_belnap@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":669451,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garcia-Pichel, Ferran","contributorId":166779,"corporation":false,"usgs":false,"family":"Garcia-Pichel","given":"Ferran","email":"","affiliations":[{"id":24511,"text":"Arizona State University, Tempe AZ USA 85287","active":true,"usgs":false}],"preferred":false,"id":669455,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70176352,"text":"70176352 - 2017 - Lithological influences on contemporary and long-term regolith weathering at the Luquillo Critical Zone Observatory","interactions":[],"lastModifiedDate":"2020-12-16T17:00:43.828181","indexId":"70176352","displayToPublicDate":"2017-02-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Lithological influences on contemporary and long-term regolith weathering at the Luquillo Critical Zone Observatory","docAbstract":"Lithologic differences give rise to the differential weatherability of the Earth’s surface and globally variable silicate weathering fluxes, which provide an important negative feedback on climate over geologic timescales. To isolate the influence of lithology on weathering rates and mechanisms, we compare two nearby catchments in the Luquillo Critical Zone Observatory in Puerto Rico, which have similar climate history, relief and vegetation, but differ in bedrock lithology. Regolith and pore water samples with depth were collected from two ridgetops and at three sites along a slope transect in the volcaniclastic Bisley catchment and compared to existing data from the granitic Río Icacos catchment. The depth variations of solid-state and pore water chemistry and quantitative mineralogy were used to calculate mass transfer (tau) and weathering solute profiles, which in turn were used to determine weathering mechanisms and to estimate weathering rates.
Regolith formed on both lithologies is highly leached of most labile elements, although Mg and K are less depleted in the granitic than in the volcaniclastic profiles, reflecting residual biotite in the granitic regolith not present in the volcaniclastics. Profiles of both lithologies that terminate at bedrock corestones are less weathered at depth, near the rock-regolith interfaces. Mg fluxes in the volcaniclastics derive primarily from dissolution of chlorite near the rock-regolith interface and from dissolution of illite and secondary phases in the upper regolith, whereas in the granitic profile, Mg and K fluxes derive from biotite dissolution. Long-term mineral dissolution rates and weathering fluxes were determined by integrating mass losses over the thickness of solid-state weathering fronts, and are therefore averages over the timescale of regolith development. Resulting long-term dissolution rates for minerals in the volcaniclastic regolith include chlorite: 8.9 × 10−14 mol m−2 s−1, illite: 2.1 × 10−14 mol m−2 s−1 and kaolinite: 4.0 × 10−14 mol m−2 s−1. Long-term weathering fluxes are several orders of magnitude lower in the granitic regolith than in the volcaniclastic, despite higher abundances of several elements in the granitic regolith. Contemporary weathering fluxes were determined from net (rain-corrected) solute profiles and thus represent rates over the residence time of water in the regolith. Contemporary weathering fluxes within the granitic regolith are similar to the long-term fluxes. In contrast, the long-term fluxes are faster than the contemporary fluxes in the volcaniclastic regolith. Contemporary fluxes in the granitic regolith are generally also slightly faster than in the volcaniclastic. The differences in weathering fluxes over space and time between these two watersheds indicate significant lithologic control of chemical weathering mechanisms and rates.
","language":"English","publisher":"Geochemical Society, Meteoritical Society","publisherLocation":"Amsterdam","doi":"10.1016/j.gca.2016.09.038","usgsCitation":"Buss, H.L., Lara, M.C., Moore, O., Kurtz, A.C., Schulz, M., and White, A.F., 2017, Lithological influences on contemporary and long-term regolith weathering at the Luquillo Critical Zone Observatory: Geochimica et Cosmochimica Acta, v. 196, p. 224-251, https://doi.org/10.1016/j.gca.2016.09.038.","productDescription":"28 p.","startPage":"224","endPage":"251","ipdsId":"IP-072854","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":470071,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://hdl.handle.net/1983/931dd02e-8852-4ce1-9deb-527305408c12","text":"Publisher Index Page"},{"id":335558,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Puerto Rico","otherGeospatial":"Bisley watersheds","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -65.75,\n 18.333333\n ],\n [\n -65.733333,\n 18.333333\n ],\n [\n -65.733333,\n 18.3\n ],\n [\n -65.75,\n 18.3\n ],\n [\n -65.75,\n 18.333333\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"196","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58a576bbe4b057081a24ed1c","contributors":{"authors":[{"text":"Buss, Heather L. 0000-0002-1852-3657","orcid":"https://orcid.org/0000-0002-1852-3657","contributorId":15478,"corporation":false,"usgs":true,"family":"Buss","given":"Heather","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":648469,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lara, Maria Chapela","contributorId":174514,"corporation":false,"usgs":false,"family":"Lara","given":"Maria","email":"","middleInitial":"Chapela","affiliations":[{"id":7172,"text":"University of Bristol, U.K. and University of Oregon, Eugene","active":true,"usgs":false}],"preferred":false,"id":648470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moore, Oliver","contributorId":174515,"corporation":false,"usgs":false,"family":"Moore","given":"Oliver","email":"","affiliations":[{"id":7172,"text":"University of Bristol, U.K. and University of Oregon, Eugene","active":true,"usgs":false}],"preferred":false,"id":648471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kurtz, Andrew C.","contributorId":174516,"corporation":false,"usgs":false,"family":"Kurtz","given":"Andrew","email":"","middleInitial":"C.","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":648472,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schulz, Marjorie S. 0000-0001-5597-6447 mschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-5597-6447","contributorId":3720,"corporation":false,"usgs":true,"family":"Schulz","given":"Marjorie S.","email":"mschulz@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":648468,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"White, Arthur F. afwhite@usgs.gov","contributorId":3718,"corporation":false,"usgs":true,"family":"White","given":"Arthur","email":"afwhite@usgs.gov","middleInitial":"F.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":648473,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70181999,"text":"70181999 - 2017 - Water quality data for national-scale aquatic research: The Water Quality Portal","interactions":[],"lastModifiedDate":"2017-03-29T15:05:03","indexId":"70181999","displayToPublicDate":"2017-02-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Water quality data for national-scale aquatic research: The Water Quality Portal","docAbstract":"Aquatic systems are critical to food, security, and society. But, water data are collected by hundreds of research groups and organizations, many of which use nonstandard or inconsistent data descriptions and dissemination, and disparities across different types of water observation systems represent a major challenge for freshwater research. To address this issue, the Water Quality Portal (WQP) was developed by the U.S. Environmental Protection Agency, the U.S. Geological Survey, and the National Water Quality Monitoring Council to be a single point of access for water quality data dating back more than a century. The WQP is the largest standardized water quality data set available at the time of this writing, with more than 290 million records from more than 2.7 million sites in groundwater, inland, and coastal waters. The number of data contributors, data consumers, and third-party application developers making use of the WQP is growing rapidly. Here we introduce the WQP, including an overview of data, the standardized data model, and data access and services; and we describe challenges and opportunities associated with using WQP data. We also demonstrate through an example the value of the WQP data by characterizing seasonal variation in lake water clarity for regions of the continental U.S. The code used to access, download, analyze, and display these WQP data as shown in the figures is included as supporting information.
","language":"English","publisher":"Wiley","doi":"10.1002/2016WR019993","usgsCitation":"Read, E.K., Carr, L., DeCicco, L.A., Dugan, H., Hanson, P.C., Hart, J.A., Kreft, J., Read, J.S., and Winslow, L., 2017, Water quality data for national-scale aquatic research: The Water Quality Portal: Water Resources Research, v. 53, no. 2, p. 1735-1745, https://doi.org/10.1002/2016WR019993.","productDescription":"11 p.","startPage":"1735","endPage":"1745","ipdsId":"IP-082664","costCenters":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"links":[{"id":470070,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016wr019993","text":"Publisher Index Page"},{"id":335451,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-12","publicationStatus":"PW","scienceBaseUri":"58a576bae4b057081a24ed19","contributors":{"authors":[{"text":"Read, Emily K. 0000-0002-9617-9433 eread@usgs.gov","orcid":"https://orcid.org/0000-0002-9617-9433","contributorId":5815,"corporation":false,"usgs":true,"family":"Read","given":"Emily","email":"eread@usgs.gov","middleInitial":"K.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"preferred":false,"id":669232,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carr, Lindsay 0000-0002-5799-6297 lcarr@usgs.gov","orcid":"https://orcid.org/0000-0002-5799-6297","contributorId":181732,"corporation":false,"usgs":true,"family":"Carr","given":"Lindsay","email":"lcarr@usgs.gov","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":669233,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeCicco, Laura A. 0000-0002-3915-9487 ldecicco@usgs.gov","orcid":"https://orcid.org/0000-0002-3915-9487","contributorId":174716,"corporation":false,"usgs":true,"family":"DeCicco","given":"Laura","email":"ldecicco@usgs.gov","middleInitial":"A.","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":669234,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dugan, Hilary","contributorId":150191,"corporation":false,"usgs":false,"family":"Dugan","given":"Hilary","affiliations":[{"id":17938,"text":"Center for Limnology University of Wisconsin, Madison, WI 53706, US","active":true,"usgs":false}],"preferred":false,"id":669235,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hanson, Paul C.","contributorId":35634,"corporation":false,"usgs":false,"family":"Hanson","given":"Paul","email":"","middleInitial":"C.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":669236,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hart, Julia A. 0000-0002-0183-8070","orcid":"https://orcid.org/0000-0002-0183-8070","contributorId":181733,"corporation":false,"usgs":false,"family":"Hart","given":"Julia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":669237,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kreft, James 0000-0001-8088-7788 jkreft@usgs.gov","orcid":"https://orcid.org/0000-0001-8088-7788","contributorId":181734,"corporation":false,"usgs":true,"family":"Kreft","given":"James","email":"jkreft@usgs.gov","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":669238,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Read, Jordan S. 0000-0002-3888-6631 jread@usgs.gov","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":4453,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","email":"jread@usgs.gov","middleInitial":"S.","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":669239,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Winslow, Luke 0000-0002-8602-5510 lwinslow@usgs.gov","orcid":"https://orcid.org/0000-0002-8602-5510","contributorId":168947,"corporation":false,"usgs":true,"family":"Winslow","given":"Luke","email":"lwinslow@usgs.gov","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":669240,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70175400,"text":"70175400 - 2017 - Source modeling of the 2015 Mw 7.8 Nepal (Gorkha) earthquake sequence: Implications for geodynamics and earthquake hazards","interactions":[],"lastModifiedDate":"2017-10-08T11:26:20","indexId":"70175400","displayToPublicDate":"2017-02-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3525,"text":"Tectonophysics","active":true,"publicationSubtype":{"id":10}},"title":"Source modeling of the 2015 Mw 7.8 Nepal (Gorkha) earthquake sequence: Implications for geodynamics and earthquake hazards","docAbstract":"The Gorkha earthquake on April 25th, 2015 was a long anticipated, low-angle thrust-faulting event on the shallow décollement between the India and Eurasia plates. We present a detailed multiple-event hypocenter relocation analysis of the Mw 7.8 Gorkha Nepal earthquake sequence, constrained by local seismic stations, and a geodetic rupture model based on InSAR and GPS data. We integrate these observations to place the Gorkha earthquake sequence into a seismotectonic context and evaluate potential earthquake hazard.
Major results from this study include (1) a comprehensive catalog of calibrated hypocenters for the Gorkha earthquake sequence; (2) the Gorkha earthquake ruptured a ~ 150 × 60 km patch of the Main Himalayan Thrust (MHT), the décollement defining the plate boundary at depth, over an area surrounding but predominantly north of the capital city of Kathmandu (3) the distribution of aftershock seismicity surrounds the mainshock maximum slip patch; (4) aftershocks occur at or below the mainshock rupture plane with depths generally increasing to the north beneath the higher Himalaya, possibly outlining a 10–15 km thick subduction channel between the overriding Eurasian and subducting Indian plates; (5) the largest Mw 7.3 aftershock and the highest concentration of aftershocks occurred to the southeast the mainshock rupture, on a segment of the MHT décollement that was positively stressed towards failure; (6) the near surface portion of the MHT south of Kathmandu shows no aftershocks or slip during the mainshock. Results from this study characterize the details of the Gorkha earthquake sequence and provide constraints on where earthquake hazard remains high, and thus where future, damaging earthquakes may occur in this densely populated region. Up-dip segments of the MHT should be considered to be high hazard for future damaging earthquakes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.tecto.2016.08.004","usgsCitation":"McNamara, D.E., Yeck, W.L., Barnhart, W.D., Schulte-Pelkum, V., Bergman, E., Adhikari, L.B., Dixit, A., Hough, S., Benz, H.M., and Earle, P.S., 2017, Source modeling of the 2015 Mw 7.8 Nepal (Gorkha) earthquake sequence: Implications for geodynamics and earthquake hazards: Tectonophysics, v. 714-715, p. 21-30, https://doi.org/10.1016/j.tecto.2016.08.004.","productDescription":"10 p.","startPage":"21","endPage":"30","ipdsId":"IP-078438","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":470069,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.tecto.2016.08.004","text":"Publisher Index Page"},{"id":335585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Nepal","otherGeospatial":"Gorkha","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 85,\n 26\n ],\n [\n 88,\n 26\n ],\n [\n 88,\n 30\n ],\n [\n 85,\n 30\n ],\n [\n 85,\n 26\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"714-715","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58a576bfe4b057081a24ed33","contributors":{"authors":[{"text":"McNamara, Daniel E. 0000-0001-6860-0350 mcnamara@usgs.gov","orcid":"https://orcid.org/0000-0001-6860-0350","contributorId":402,"corporation":false,"usgs":true,"family":"McNamara","given":"Daniel","email":"mcnamara@usgs.gov","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":645060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":645061,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnhart, William D. wbarnhart@usgs.gov","contributorId":5299,"corporation":false,"usgs":true,"family":"Barnhart","given":"William","email":"wbarnhart@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":645062,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schulte-Pelkum, V.","contributorId":173550,"corporation":false,"usgs":false,"family":"Schulte-Pelkum","given":"V.","affiliations":[{"id":6709,"text":"University of Colorado, Denver","active":true,"usgs":false}],"preferred":false,"id":669379,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bergman, E.","contributorId":84289,"corporation":false,"usgs":true,"family":"Bergman","given":"E.","affiliations":[],"preferred":false,"id":645064,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Adhikari, L. B.","contributorId":147569,"corporation":false,"usgs":false,"family":"Adhikari","given":"L.","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":669380,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dixit, Amod","contributorId":150708,"corporation":false,"usgs":false,"family":"Dixit","given":"Amod","email":"","affiliations":[{"id":18073,"text":"National Society for Earthquake Technology","active":true,"usgs":false}],"preferred":false,"id":669381,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hough, S. E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":7316,"corporation":false,"usgs":true,"family":"Hough","given":"S. E.","affiliations":[],"preferred":false,"id":669382,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Benz, Harley M. 0000-0002-6860-2134 benz@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-2134","contributorId":794,"corporation":false,"usgs":true,"family":"Benz","given":"Harley","email":"benz@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":645065,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Earle, Paul S. 0000-0002-3500-017X pearle@usgs.gov","orcid":"https://orcid.org/0000-0002-3500-017X","contributorId":173551,"corporation":false,"usgs":true,"family":"Earle","given":"Paul","email":"pearle@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":645066,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70191586,"text":"70191586 - 2017 - Lithospheric density structure beneath the Tarim basin and surroundings, northwestern China, from the joint inversion of gravity and topography","interactions":[],"lastModifiedDate":"2017-10-18T10:28:04","indexId":"70191586","displayToPublicDate":"2017-02-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Lithospheric density structure beneath the Tarim basin and surroundings, northwestern China, from the joint inversion of gravity and topography","docAbstract":"Intraplate strain generally focuses in discrete zones, but despite the profound impact of this partitioning on global tectonics, geodynamics, and seismic hazard, the processes by which deformation becomes localized are not well understood. Such heterogeneous intraplate strain is exemplified in central Asia, where the Indo-Eurasian collision has caused widespread deformation while the Tarim block has experienced minimal Cenozoic shortening. The apparent stability of Tarim may arise either because strain is dominantly accommodated by pre-existing faults in the continental suture zones that bound it—essentially discretizing Eurasia into microplates—or because the lithospheric-scale strength (i.e., viscosity) of the Tarim block is greater than its surroundings. Here, we jointly analyze seismic velocity, gravity, topography, and temperature to develop a 3-D density model of the crust and upper mantle in this region. The Tarim crust is characterized by high density, vs, vp, and vp/vs, consistent with a dominantly mafic composition and with the presence of an oceanic plateau beneath Tarim. Low-density but high-velocity mantle lithosphere beneath southern (southwestern) Tarim underlies a suite of Permian plume-related mafic intrusions and A-type granites sourced in previously depleted mantle lithosphere; we posit that this region was further depleted, dehydrated, and strengthened by Permian plume magmatism. The actively deforming western and southern margins of Tarim—the Tien Shan, Kunlun Shan, and Altyn Tagh fault—are underlain by buoyant upper mantle with low velocity; we hypothesize that this material has been hydrated by mantle-derived fluids that have preferentially migrated along Paleozoic continental sutures. Such hydrous material should be weak, and herein strain focuses there because of lithospheric-scale variations in rheology rather than the pre-existence of faults in the brittle crust. Thus this world-class example of strain partitioning arises not simply from the pre-existence of brittle faults but from the thermo-chemical and therefore rheological variations inherited from prior tectonism.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2016.10.051","usgsCitation":"Deng, Y., Levandowski, W.B., and Kusky, T., 2017, Lithospheric density structure beneath the Tarim basin and surroundings, northwestern China, from the joint inversion of gravity and topography: Earth and Planetary Science Letters, v. 460, p. 244-254, https://doi.org/10.1016/j.epsl.2016.10.051.","productDescription":"11 p.","startPage":"244","endPage":"254","ipdsId":"IP-081717","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":461733,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2016.10.051","text":"Publisher Index Page"},{"id":346829,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 76,\n 36\n ],\n [\n 92,\n 36\n ],\n [\n 92,\n 44\n ],\n [\n 76,\n 44\n ],\n [\n 76,\n 36\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"460","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59e86837e4b05fe04cd4d20a","contributors":{"authors":[{"text":"Deng, Yangfan","contributorId":197188,"corporation":false,"usgs":false,"family":"Deng","given":"Yangfan","email":"","affiliations":[],"preferred":false,"id":712816,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Levandowski, William Brower 0000-0003-4903-5012 wlevandowski@usgs.gov","orcid":"https://orcid.org/0000-0003-4903-5012","contributorId":5729,"corporation":false,"usgs":true,"family":"Levandowski","given":"William","email":"wlevandowski@usgs.gov","middleInitial":"Brower","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":712818,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kusky, Tim","contributorId":197189,"corporation":false,"usgs":false,"family":"Kusky","given":"Tim","email":"","affiliations":[],"preferred":false,"id":712817,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70182056,"text":"70182056 - 2017 - Fire and the distribution and uncertainty of carbon sequestered as above-ground tree biomass in Yosemite and Sequoia & Kings Canyon National Parks","interactions":[],"lastModifiedDate":"2017-02-15T15:12:59","indexId":"70182056","displayToPublicDate":"2017-02-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2596,"text":"Land","active":true,"publicationSubtype":{"id":10}},"title":"Fire and the distribution and uncertainty of carbon sequestered as above-ground tree biomass in Yosemite and Sequoia & Kings Canyon National Parks","docAbstract":"Fire is one of the principal agents changing forest carbon stocks and landscape level distributions of carbon, but few studies have addressed how accurate carbon accounting of fire-killed trees is or can be. We used a large number of forested plots (1646), detailed selection of species-specific and location-specific allometric equations, vegetation type maps with high levels of accuracy, and Monte Carlo simulation to model the amount and uncertainty of aboveground tree carbon present in tree species (hereafter, carbon) within Yosemite and Sequoia & Kings Canyon National Parks. We estimated aboveground carbon in trees within Yosemite National Park to be 25 Tg of carbon (C) (confidence interval (CI): 23–27 Tg C), and in Sequoia & Kings Canyon National Park to be 20 Tg C (CI: 18–21 Tg C). Low-severity and moderate-severity fire had little or no effect on the amount of carbon sequestered in trees at the landscape scale, and high-severity fire did not immediately consume much carbon. Although many of our data inputs were more accurate than those used in similar studies in other locations, the total uncertainty of carbon estimates was still greater than ±10%, mostly due to potential uncertainties in landscape-scale vegetation type mismatches and trees larger than the ranges of existing allometric equations. If carbon inventories are to be meaningfully used in policy, there is an urgent need for more accurate landscape classification methods, improvement in allometric equations for tree species, and better understanding of the uncertainties inherent in existing carbon accounting methods.","language":"English","publisher":"MDPI","doi":"10.3390/land6010010","usgsCitation":"Lutz, J.A., Matchett, J.R., Tarnay, L.W., Smith, D., Becker, K.M., Furniss, T.J., and Brooks, M.L., 2017, Fire and the distribution and uncertainty of carbon sequestered as above-ground tree biomass in Yosemite and Sequoia & Kings Canyon National Parks: Land, v. 6, no. 1, Article 10; 24 p., https://doi.org/10.3390/land6010010.","productDescription":"Article 10; 24 p.","ipdsId":"IP-066486","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":461737,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/land6010010","text":"Publisher Index Page"},{"id":335622,"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","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.827880859375,\n 35.40248356426937\n ],\n [\n -117.61962890624999,\n 35.40248356426937\n ],\n [\n -117.61962890624999,\n 37.18657859524883\n ],\n [\n -119.827880859375,\n 37.18657859524883\n ],\n [\n -119.827880859375,\n 35.40248356426937\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-27","publicationStatus":"PW","scienceBaseUri":"58a576b8e4b057081a24ed0e","contributors":{"authors":[{"text":"Lutz, James A.","contributorId":139178,"corporation":false,"usgs":false,"family":"Lutz","given":"James","email":"","middleInitial":"A.","affiliations":[{"id":12682,"text":"Utah State University, Logan, UT","active":true,"usgs":false}],"preferred":false,"id":669414,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":669415,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tarnay, Leland W.","contributorId":139179,"corporation":false,"usgs":false,"family":"Tarnay","given":"Leland","email":"","middleInitial":"W.","affiliations":[{"id":12683,"text":"National Park Service, Yosemite National Park, El Portal, CA","active":true,"usgs":false}],"preferred":false,"id":669416,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Douglas F.","contributorId":181753,"corporation":false,"usgs":false,"family":"Smith","given":"Douglas F.","affiliations":[],"preferred":false,"id":669417,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Becker, Kendall M.L.","contributorId":139180,"corporation":false,"usgs":false,"family":"Becker","given":"Kendall","email":"","middleInitial":"M.L.","affiliations":[{"id":12682,"text":"Utah State University, Logan, UT","active":true,"usgs":false}],"preferred":false,"id":669418,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Furniss, Tucker J.","contributorId":181754,"corporation":false,"usgs":false,"family":"Furniss","given":"Tucker","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":669419,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":669413,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}