Partial migration is a common life-history strategy among ungulates living in seasonal environments. The decision to migrate or remain on a seasonal range may be influenced strongly by access to high-quality habitat. We evaluated the influence of access to winter habitat of high quality on the probability of a female white-tailed deer (Odocoileus virginianus) migrating to a separate summer range and the effects of this decision on survival. We hypothesized that deer with home ranges of low quality in winter would have a high probability of migrating, and that survival of an individual in winter would be influenced by the quality of their home range in winter. We radiocollared 67 female white-tailed deer in 2012 and 2013 in eastern Washington, United States. We estimated home range size in winter using a kernel density estimator; we assumed the size of the home range was inversely proportional to its quality and the proportion of crop land within the home range was proportional to its quality. Odds of migrating from winter ranges increased by 3.1 per unit increase in home range size and decreased by 0.29 per unit increase in the proportion of crop land within a home range. Annual survival rate for migrants was 0.85 (SD = 0.05) and 0.84 (SD = 0.09) for residents. Our finding that an individual with a low-quality home range in winter is likely to migrate to a separate summer range accords with the hypothesis that competition for a limited amount of home ranges of high quality should result in residents having home ranges of higher quality than migrants in populations experiencing density dependence. We hypothesize that density-dependent competition for high-quality home ranges in winter may play a leading role in the selection of migration strategy by female white-tailed deer.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/jmammal/gyx148","usgsCitation":"Henderson, C.R., Mitchell, M.S., Myers, W.L., Lukacs, P.M., and Nelson, G.P., 2018, Attributes of seasonal home range influence choice of migratory strategy in white-tailed deer: Journal of Mammalogy, v. 99, no. 1, p. 89-96, https://doi.org/10.1093/jmammal/gyx148.","productDescription":"8 p.","startPage":"89","endPage":"96","ipdsId":"IP-076163","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":469063,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jmammal/gyx148","text":"Publisher Index Page"},{"id":354400,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"99","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-22","publicationStatus":"PW","scienceBaseUri":"5b155db9e4b092d9651e1b81","contributors":{"authors":[{"text":"Henderson, Charles R. Jr.","contributorId":205132,"corporation":false,"usgs":false,"family":"Henderson","given":"Charles","suffix":"Jr.","email":"","middleInitial":"R.","affiliations":[{"id":37028,"text":"Montana Cooperative Wildlife Research Unit, University of Montana, Natural Sciences, Missoula, MT","active":true,"usgs":false}],"preferred":false,"id":736180,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mitchell, Michael S. 0000-0002-0773-6905 mmitchel@usgs.gov","orcid":"https://orcid.org/0000-0002-0773-6905","contributorId":3716,"corporation":false,"usgs":true,"family":"Mitchell","given":"Michael","email":"mmitchel@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":736179,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Myers, Woodrow L.","contributorId":200876,"corporation":false,"usgs":false,"family":"Myers","given":"Woodrow","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":736181,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lukacs, Paul M.","contributorId":101240,"corporation":false,"usgs":true,"family":"Lukacs","given":"Paul","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":736232,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nelson, Gerald P.","contributorId":205134,"corporation":false,"usgs":false,"family":"Nelson","given":"Gerald","email":"","middleInitial":"P.","affiliations":[{"id":37030,"text":"Wildlife Program, Washington Department of Fish and Wildlife, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":736182,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70195393,"text":"70195393 - 2018 - On factors influencing air-water gas exchange in emergent wetlands","interactions":[],"lastModifiedDate":"2018-02-23T11:13:10","indexId":"70195393","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2319,"text":"Journal of Geophysical Research G: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"On factors influencing air-water gas exchange in emergent wetlands","docAbstract":"Knowledge of gas exchange in wetlands is important in order to determine fluxes of climatically and biogeochemically important trace gases and to conduct mass balances for metabolism studies. Very few studies have been conducted to quantify gas transfer velocities in wetlands, and many wind speed/gas exchange parameterizations used in oceanographic or limnological settings are inappropriate under conditions found in wetlands. Here six measurements of gas transfer velocities are made with SF6 tracer release experiments in three different years in the Everglades, a subtropical peatland with surface water flowing through emergent vegetation. The experiments were conducted under different flow conditions and with different amounts of emergent vegetation to determine the influence of wind, rain, water flow, waterside thermal convection, and vegetation on air-water gas exchange in wetlands. Measured gas transfer velocities under the different conditions ranged from 1.1 cm h−1 during baseline conditions to 3.2 cm h−1 when rain and water flow rates were high. Commonly used wind speed/gas exchange relationships would overestimate the gas transfer velocity by a factor of 1.2 to 6.8. Gas exchange due to thermal convection was relatively constant and accounted for 14 to 51% of the total measured gas exchange. Differences in rain and water flow among the different years were responsible for the variability in gas exchange, with flow accounting for 37 to 77% of the gas exchange, and rain responsible for up to 40%.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017JG004299","usgsCitation":"Ho, D.T., Engel, V.C., Ferron, S., Hickman, B., Choi, J., and Harvey, J.W., 2018, On factors influencing air-water gas exchange in emergent wetlands: Journal of Geophysical Research G: Biogeosciences, v. 123, no. 1, p. 178-192, https://doi.org/10.1002/2017JG004299.","productDescription":"15 p.","startPage":"178","endPage":"192","ipdsId":"IP-093042","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":469055,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017jg004299","text":"Publisher Index Page"},{"id":351532,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.63,\n 25.84\n ],\n [\n -80.60,\n 25.84\n ],\n [\n -80.60,\n 25.87\n ],\n [\n -80.63,\n 25.87\n ],\n [\n -80.63,\n 25.84\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"123","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-24","publicationStatus":"PW","scienceBaseUri":"5afee743e4b0da30c1bfc205","contributors":{"authors":[{"text":"Ho, David T.","contributorId":202425,"corporation":false,"usgs":false,"family":"Ho","given":"David","email":"","middleInitial":"T.","affiliations":[{"id":36430,"text":"University of Hawaii, Honolulu, Hawaii","active":true,"usgs":false}],"preferred":false,"id":728385,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Engel, Victor C. 0000-0002-3858-7308","orcid":"https://orcid.org/0000-0002-3858-7308","contributorId":202426,"corporation":false,"usgs":false,"family":"Engel","given":"Victor","email":"","middleInitial":"C.","affiliations":[{"id":36431,"text":"U.S. Forest Service, Fort Collins","active":true,"usgs":false}],"preferred":false,"id":728386,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ferron, Sara","contributorId":199936,"corporation":false,"usgs":false,"family":"Ferron","given":"Sara","email":"","affiliations":[],"preferred":false,"id":728387,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hickman, Benjamin","contributorId":202427,"corporation":false,"usgs":false,"family":"Hickman","given":"Benjamin","email":"","affiliations":[{"id":36430,"text":"University of Hawaii, Honolulu, Hawaii","active":true,"usgs":false}],"preferred":false,"id":728388,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Choi, Jay jchoi@usgs.gov","contributorId":4731,"corporation":false,"usgs":true,"family":"Choi","given":"Jay","email":"jchoi@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":728389,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Harvey, Judson W. 0000-0002-2654-9873 jwharvey@usgs.gov","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":1796,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","email":"jwharvey@usgs.gov","middleInitial":"W.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":728384,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70194989,"text":"70194989 - 2018 - Comparative analyses of hydrological responses of two adjacent watersheds to climate variability and change using the SWAT model","interactions":[],"lastModifiedDate":"2018-02-02T10:29:37","indexId":"70194989","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Comparative analyses of hydrological responses of two adjacent watersheds to climate variability and change using the SWAT model","docAbstract":"Water quality problems in the Chesapeake Bay Watershed (CBW) are expected to be exacerbated by climate variability and change. However, climate impacts on agricultural lands and resultant nutrient loads into surface water resources are largely unknown. This study evaluated the impacts of climate variability and change on two adjacent watersheds in the Coastal Plain of the CBW, using the Soil and Water Assessment Tool (SWAT) model. We prepared six climate sensitivity scenarios to assess the individual impacts of variations in CO2concentration (590 and 850 ppm), precipitation increase (11 and 21 %), and temperature increase (2.9 and 5.0 °C), based on regional general circulation model (GCM) projections. Further, we considered the ensemble of five GCM projections (2085–2098) under the Representative Concentration Pathway (RCP) 8.5 scenario to evaluate simultaneous changes in CO2, precipitation, and temperature. Using SWAT model simulations from 2001 to 2014 as a baseline scenario, predicted hydrologic outputs (water and nitrate budgets) and crop growth were analyzed. Compared to the baseline scenario, a precipitation increase of 21 % and elevated CO2 concentration of 850 ppm significantly increased streamflow and nitrate loads by 50 and 52 %, respectively, while a temperature increase of 5.0 °C reduced streamflow and nitrate loads by 12 and 13 %, respectively. Crop biomass increased with elevated CO2 concentrations due to enhanced radiation- and water-use efficiency, while it decreased with precipitation and temperature increases. Over the GCM ensemble mean, annual streamflow and nitrate loads showed an increase of ∼ 70 % relative to the baseline scenario, due to elevated CO2 concentrations and precipitation increase. Different hydrological responses to climate change were observed from the two watersheds, due to contrasting land use and soil characteristics. The watershed with a larger percent of croplands demonstrated a greater increased rate of 5.2 kg N ha−1 in nitrate yield relative to the watershed with a lower percent of croplands as a result of increased export of nitrate derived from fertilizer. The watershed dominated by poorly drained soils showed increased nitrate removal due do enhanced denitrification compared to the watershed dominated by well-drained soils. Our findings suggest that increased implementation of conservation practices would be necessary for this region to mitigate increased nitrate loads associated with predicted changes in future climate.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/hess-22-689-2018","usgsCitation":"Lee, S., Yeo, I., Sadeghi, A.M., McCarty, G.W., Hively, W., Lang, M.W., and Sharifi, A., 2018, Comparative analyses of hydrological responses of two adjacent watersheds to climate variability and change using the SWAT model: Hydrology and Earth System Sciences, v. 22, p. 689-708, https://doi.org/10.5194/hess-22-689-2018.","productDescription":"10 p.","startPage":"689","endPage":"708","ipdsId":"IP-090233","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":469071,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-22-689-2018","text":"Publisher Index Page"},{"id":350956,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Greensboro Watershed, Tuckahoe Creek Watershed","volume":"22","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-25","publicationStatus":"PW","scienceBaseUri":"5a7586d6e4b00f54eb1d81d4","contributors":{"authors":[{"text":"Lee, Sangchul","contributorId":201237,"corporation":false,"usgs":false,"family":"Lee","given":"Sangchul","email":"","affiliations":[],"preferred":false,"id":726400,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yeo, In-Young","contributorId":131145,"corporation":false,"usgs":false,"family":"Yeo","given":"In-Young","email":"","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":726402,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sadeghi, Ali M.","contributorId":131147,"corporation":false,"usgs":false,"family":"Sadeghi","given":"Ali","email":"","middleInitial":"M.","affiliations":[{"id":7262,"text":"USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705","active":true,"usgs":false}],"preferred":false,"id":726401,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCarty, Gregory W.","contributorId":192367,"corporation":false,"usgs":false,"family":"McCarty","given":"Gregory","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":726403,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hively, Wells whively@usgs.gov","contributorId":201563,"corporation":false,"usgs":true,"family":"Hively","given":"Wells","email":"whively@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":726399,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lang, Megan W.","contributorId":196284,"corporation":false,"usgs":false,"family":"Lang","given":"Megan","email":"","middleInitial":"W.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":726404,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sharifi, Amir","contributorId":201564,"corporation":false,"usgs":false,"family":"Sharifi","given":"Amir","email":"","affiliations":[{"id":18168,"text":"USDA ARS","active":true,"usgs":false}],"preferred":false,"id":726405,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70196629,"text":"70196629 - 2018 - Integrating future scenario‐based crop expansion and crop conditions to map switchgrass biofuel potential in eastern Nebraska, USA","interactions":[],"lastModifiedDate":"2018-04-23T10:01:33","indexId":"70196629","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1718,"text":"GCB Bioenergy","active":true,"publicationSubtype":{"id":10}},"title":"Integrating future scenario‐based crop expansion and crop conditions to map switchgrass biofuel potential in eastern Nebraska, USA","docAbstract":"Switchgrass (Panicum virgatum) has been evaluated as one potential source for cellulosic biofuel feedstocks. Planting switchgrass in marginal croplands and waterway buffers can reduce soil erosion, improve water quality, and improve regional ecosystem services (i.e. it serves as a potential carbon sink). In previous studies, we mapped high risk marginal croplands and highly erodible cropland buffers that are potentially suitable for switchgrass development, which would improve ecosystem services and minimally impact food production. In this study, we advance our previous study results and integrate future crop expansion information to develop a switchgrass biofuel potential ensemble map for current and future croplands in eastern Nebraska. The switchgrass biomass productivity and carbon benefits (i.e. NEP: net ecosystem production) for the identified biofuel potential ensemble areas were quantified. The future scenario‐based (‘A1B’) land use and land cover map for 2050, the US Geological Survey crop type and Compound Topographic Index (CTI) maps, and long‐term (1981–2010) averaged annual precipitation data were used to identify future crop expansion regions that are suitable for switchgrass development. Results show that 2528 km2 of future crop expansion regions (~3.6% of the study area) are potentially suitable for switchgrass development. The total estimated biofuel potential ensemble area (including cropland buffers, marginal croplands, and future crop expansion regions) is 4232 km2 (~6% of the study area), potentially producing 3.52 million metric tons of switchgrass biomass per year. Converting biofuel ensemble regions to switchgrass leads to potential carbon sinks (the total NEP for biofuel potential areas is 0.45 million metric tons C) and is environmentally sustainable. Results from this study improve our understanding of environmental conditions and ecosystem services of current and future cropland systems in eastern Nebraska and provide useful information to land managers to make land use decisions regarding switchgrass development.
","language":"English","publisher":"Wiley","doi":"10.1111/gcbb.12468","usgsCitation":"Gu, Y., and Wylie, B., 2018, Integrating future scenario‐based crop expansion and crop conditions to map switchgrass biofuel potential in eastern Nebraska, USA: GCB Bioenergy, v. 10, no. 2, p. 76-83, https://doi.org/10.1111/gcbb.12468.","productDescription":"8 p.","startPage":"76","endPage":"83","ipdsId":"IP-087756","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":469053,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcbb.12468","text":"Publisher Index Page"},{"id":353641,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.1845703125,\n 40.01078714046552\n ],\n [\n -95.30639648437499,\n 40.01078714046552\n ],\n [\n -95.30639648437499,\n 42.99661231842139\n ],\n [\n -99.1845703125,\n 42.99661231842139\n ],\n [\n -99.1845703125,\n 40.01078714046552\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-12","publicationStatus":"PW","scienceBaseUri":"5afee740e4b0da30c1bfc1d7","contributors":{"authors":[{"text":"Gu, Yingxin 0000-0002-3544-1856 ygu@usgs.gov","orcid":"https://orcid.org/0000-0002-3544-1856","contributorId":139586,"corporation":false,"usgs":true,"family":"Gu","given":"Yingxin","email":"ygu@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":733834,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":197161,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce K.","email":"wylie@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":733835,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70195941,"text":"70195941 - 2018 - Malassezia vespertilionis sp. nov.: A new cold-tolerant species of yeast isolated from bats","interactions":[],"lastModifiedDate":"2018-03-08T12:19:48","indexId":"70195941","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5648,"text":"Persoonia - Molecular Phylogeny and Evolution of Fungi","active":true,"publicationSubtype":{"id":10}},"title":"Malassezia vespertilionis sp. nov.: A new cold-tolerant species of yeast isolated from bats","docAbstract":"Malassezia is a genus of medically-important, lipid-dependent yeasts that live on the skin of warm-blooded animals. The 17 described species have been documented primarily on humans and domestic animals, but few studies have examined Malassezia species associated with more diverse host groups such as wildlife. While investigating the skin mycobiota of healthy bats, we isolated a Malassezia sp. that exhibited only up to 92 % identity with other known species in the genus for the portion of the DNA sequence of the internal transcribed spacer region that could be confidently aligned. The Malassezia sp. was cultured from the skin of nine species of bats in the subfamily Myotinae; isolates originated from bats sampled in both the eastern and western United States. Physiological features and molecular characterisation at seven additional loci (D1/D2 region of 26S rDNA, 18S rDNA, chitin synthase, second largest subunit of RNA polymerase II, β-tubulin, translation elongation factor EF-1α, and minichromosome maintenance complex component 7) indicated that all of the bat Malasseziaisolates likely represented a single species distinct from other named taxa. Of particular note was the ability of the Malassezia sp. to grow over a broad range of temperatures (7–40 °C), with optimal growth occurring at 24 °C. These thermal growth ranges, unique among the described Malassezia, may be an adaptation by the fungus to survive on bats during both the host's hibernation and active seasons. The combination of genetic and physiological differences provided compelling evidence that this lipid-dependent yeast represents a novel species described herein as Malassezia vespertilionis sp. nov. Whole genome sequencing placed the new species as a basal member of the clade containing the species M. furfur, M. japonica, M. obtusa, and M. yamatoensis. The genetic and physiological uniqueness of Malassezia vespertilionis among its closest relatives may make it important in future research to better understand the evolution, life history, and pathogenicity of the Malasseziayeasts.
","language":"English","publisher":"Naturalis Biodiversity Center","doi":"10.3767/persoonia.2018.41.04","usgsCitation":"Lorch, J.M., Palmer, J.M., Vanderwolf, K.J., Schmidt, K.Z., Verant, M.L., Weller, T.J., and Blehert, D.S., 2018, Malassezia vespertilionis sp. nov.: A new cold-tolerant species of yeast isolated from bats: Persoonia - Molecular Phylogeny and Evolution of Fungi, v. 41, p. 56-70, https://doi.org/10.3767/persoonia.2018.41.04.","productDescription":"15 p.","startPage":"56","endPage":"70","ipdsId":"IP-091447","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":461065,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3767/persoonia.2018.41.04","text":"Publisher Index Page"},{"id":352336,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"41","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee742e4b0da30c1bfc1f5","contributors":{"authors":[{"text":"Lorch, Jeffrey M. 0000-0003-2239-1252 jlorch@usgs.gov","orcid":"https://orcid.org/0000-0003-2239-1252","contributorId":5565,"corporation":false,"usgs":true,"family":"Lorch","given":"Jeffrey","email":"jlorch@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":730601,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palmer, Jonathan M.","contributorId":172601,"corporation":false,"usgs":false,"family":"Palmer","given":"Jonathan","email":"","middleInitial":"M.","affiliations":[{"id":27066,"text":"Center for Forest Mycology Research, Northern Research Station, US Forest Service, Madison, Wisconsin, USAb","active":true,"usgs":false}],"preferred":false,"id":730602,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vanderwolf, Karen J. 0000-0003-0963-3093","orcid":"https://orcid.org/0000-0003-0963-3093","contributorId":203200,"corporation":false,"usgs":true,"family":"Vanderwolf","given":"Karen","email":"","middleInitial":"J.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":730603,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmidt, Katie Z.","contributorId":176251,"corporation":false,"usgs":false,"family":"Schmidt","given":"Katie","email":"","middleInitial":"Z.","affiliations":[],"preferred":false,"id":730604,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Verant, Michelle L.","contributorId":201556,"corporation":false,"usgs":false,"family":"Verant","given":"Michelle","email":"","middleInitial":"L.","affiliations":[{"id":36202,"text":"School of Veterinary Medicine, University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":730605,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Weller, Theodore J.","contributorId":105961,"corporation":false,"usgs":false,"family":"Weller","given":"Theodore","email":"","middleInitial":"J.","affiliations":[{"id":13261,"text":"USDA Forest Service, Pacific Southwest Research Station, Davis, California","active":true,"usgs":false}],"preferred":false,"id":730606,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Blehert, David S. 0000-0002-1065-9760 dblehert@usgs.gov","orcid":"https://orcid.org/0000-0002-1065-9760","contributorId":140392,"corporation":false,"usgs":true,"family":"Blehert","given":"David","email":"dblehert@usgs.gov","middleInitial":"S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":false,"id":730607,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70197788,"text":"70197788 - 2018 - Variabilities in probabilistic seismic hazard maps for natural and induced seismicity in the central and eastern United States","interactions":[],"lastModifiedDate":"2018-06-20T10:54:13","indexId":"70197788","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3568,"text":"The Leading Edge","active":true,"publicationSubtype":{"id":10}},"title":"Variabilities in probabilistic seismic hazard maps for natural and induced seismicity in the central and eastern United States","docAbstract":"Probabilistic seismic hazard analysis (PSHA) characterizes ground-motion hazard from earthquakes. Typically, the time horizon of a PSHA forecast is long, but in response to induced seismicity related to hydrocarbon development, the USGS developed one-year PSHA models. In this paper, we present a display of the variability in USGS hazard curves due to epistemic uncertainty in its informed submodel using a simple bootstrapping approach. We find that variability is highest in low-seismicity areas. On the other hand, areas of high seismic hazard, such as the New Madrid seismic zone or Oklahoma, exhibit relatively lower variability simply because of more available data and a better understanding of the seismicity. Comparing areas of high hazard, New Madrid, which has a history of large naturally occurring earthquakes, has lower forecast variability than Oklahoma, where the hazard is driven mainly by suspected induced earthquakes since 2009. Overall, the mean hazard obtained from bootstrapping is close to the published model, and variability increased in the 2017 one-year model relative to the 2016 model. Comparing the relative variations caused by individual logic-tree branches, we find that the highest hazard variation (as measured by the 95% confidence interval of bootstrapping samples) in the final model is associated with different ground-motion models and maximum magnitudes used in the logic tree, while the variability due to the smoothing distance is minimal. It should be pointed out that this study is not looking at the uncertainty in the hazard in general, but only as it is represented in the USGS one-year models.
","language":"English","publisher":"Society of Exploration Geophysicists","doi":"10.1190/tle37020141a1.1","usgsCitation":"Mousavi, S.M., Beroza, G.C., and Hoover, S.M., 2018, Variabilities in probabilistic seismic hazard maps for natural and induced seismicity in the central and eastern United States: The Leading Edge, v. 37, no. 2, p. 141a1-141a9, https://doi.org/10.1190/tle37020141a1.1.","productDescription":"9 p.","startPage":"141a1","endPage":"141a9","ipdsId":"IP-093220","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":355202,"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 -115,\n 25\n ],\n [\n -65,\n 25\n ],\n [\n -65,\n 50\n ],\n [\n -115,\n 50\n ],\n [\n -115,\n 25\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e5d3e4b060350a15d21c","contributors":{"authors":[{"text":"Mousavi, S. Mostafa","contributorId":205790,"corporation":false,"usgs":false,"family":"Mousavi","given":"S.","email":"","middleInitial":"Mostafa","affiliations":[{"id":37167,"text":"Department of Geophysics, Stanford University, Stanford, CA","active":true,"usgs":false}],"preferred":false,"id":738494,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beroza, Gregory C.","contributorId":191201,"corporation":false,"usgs":false,"family":"Beroza","given":"Gregory","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":738495,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoover, Susan M. 0000-0002-8682-6668 shoover@usgs.gov","orcid":"https://orcid.org/0000-0002-8682-6668","contributorId":5715,"corporation":false,"usgs":true,"family":"Hoover","given":"Susan","email":"shoover@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":738496,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70194519,"text":"sim3392 - 2018 - Quaternary sediment thickness and bedrock topography of the glaciated United States east of the Rocky Mountains","interactions":[],"lastModifiedDate":"2018-01-29T09:58:24","indexId":"sim3392","displayToPublicDate":"2018-01-26T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3392","title":"Quaternary sediment thickness and bedrock topography of the glaciated United States east of the Rocky Mountains","docAbstract":"Beginning roughly 2.6 million years ago, global climate entered a cooling phase known as the Pleistocene Epoch. As snow in northern latitudes compacted into ice several kilometers thick, it flowed as glaciers southward across the North American continent. These glaciers extended across the northern United States, dramatically altering the landscape they covered. East of the Rocky Mountains, the ice coalesced into continental glaciers (called the Laurentide Ice Sheet) that at times blanketed much of the north-central and northeastern United States. To the west of the Laurentide Ice Sheet, glaciers formed in the mountains of western Canada and the United States and coalesced into the Cordilleran ice sheet; this relatively smaller ice mass extended into the conterminous United States in the northernmost areas of western Montana, Idaho, and Washington. Throughout the Pleistocene, landscape alteration occurred by (1) glacial erosion of the rocks and sediments; (2) redeposition of the eroded earth materials in a form substantially different from their source rocks, in terms of texture and overall character; and (3) disruption of preexisting drainage patterns by the newly deposited sediments. In many cases, pre-glacial drainage systems (including, for example, the Mississippi River) were rerouted because their older drainage courses became blocked with glacial sediment.
The continental glaciers advanced and retreated many times across those areas. During each ice advance, or glaciation, erosion and deposition occurred, and the landscape was again altered. Through successive glaciations, the landscape and the bedrock surface gradually came to resemble their present configurations. As continental ice sheets receded and the Pleistocene ended, erosion and deposition of sediment (for example in stream valleys) continued to shape the landscape up to the present day (albeit to a lesser extent than during glaciation). The interval of time since the last recession of the glaciers is called the Holocene and, together with the Pleistocene, constitutes the Quaternary Period of geologic time; this publication characterizes the three-dimensional geometry of the Quaternary sediments and the bedrock surface that lies beneath.
The pre-glacial landscape was underlain mostly by weathered bedrock generally similar in nature to that found in many areas of the non-glaciated United States. Glacial erosion and redeposition of earth materials produced a young, mineral-rich soil that formed the basis for the highly productive agricultural economy in the U.S. midcontinent. Extensive buried sands and gravels within the glacial deposits also provided a stimulus to other economic sectors by serving as high-quality aquifers supplying groundwater to the region’s industry and cities. An understanding of the three-dimensional distribution of these glacial sediments has direct utility for addressing various societal issues including groundwater quality and supply, and landscape and soil response to earthquake-induced shaking.
The Quaternary sediment thickness map and bedrock topographic map shown here provide a regional overview and are intended to supplement the more detailed work on which they are based. Detailed mapping is particularly useful in populated areas for site-specific planning. In contrast, regional maps such as these serve to place local, detailed mapping in context; to permit the extrapolation of data into unmapped areas; and to depict large-scale regional geologic features and patterns that are beyond the scope of local, detailed mapping. They also can enhance the reader’s general understanding of the region’s landscape and geologic history and provide a source of information for regional decision making that could benefit by improved predictability of bedrock depth beneath the unconsolidated Quaternary sediments. To enable these maps to be analyzed in conjunction with other types of information, this publication also includes the map data in GIS compatible format.
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\"}}]}","contact":"National Cooperative Geologic Mapping Program
U.S. Geological Survey
12201 Sunrise Valley Drive Mail Stop 908
Reston, VA 20192
Fax: (703) 648-6937
Pleistocene glaciations and late Cenozoic offset on the Teton fault have played central roles in shaping the scenic landscapes of the Teton Range and Jackson Hole area in Wyoming. The Teton Range harbored a system of mountain-valley glaciers that produced the striking geomorphic features in these mountains. However, the comparatively much larger southern sector of the Greater Yellowstone glacial system (GYGS) is responsible for creating the more expansive glacial landforms and deposits that dominate Jackson Hole. The glacial history is also inextricably associated with the Yellowstone hotspot, which caused two conditions that have fostered extensive glaciation: (1) uplift and consequent cold temperatures in greater Yellowstone; and (2) the lowland track of the hotspot (eastern Snake River Plain) that funneled moisture to the Yellowstone Plateau and the Yellowstone Crescent of High Terrain (YCHT).
The penultimate (Bull Lake) glaciation filled all of Jackson Hole with glacial ice. Granitic boulders on moraines beyond the south end of Jackson Hole have cosmogenic 10Be exposure ages of ~150 thousand years ago (ka) and correlate with Marine Isotope Stage 6. A thick loess mantle subdues the topography of Bull Lake moraines and caps Bull Lake outwash terraces with a reddish buried soil near the base of the loess having a Bk horizon that extends down into the outwash gravel. The Bull Lake glaciation of Jackson Hole extended 48 kilometers (km) farther south than the Pinedale, representing the largest separation of these two glacial positions in the Western United States. The Bull Lake is also more extensive than the Pinedale on the west (22 km) and southwest (23 km) margins of the GYGS but not on the north and east. This pattern is explained by uplift and subsidence on the leading and trailing “bow-wave” of the YCHT, respectively.
During the last (Pinedale) glaciation, mountain-valley glaciers of the Teton Range extended to the western edge of Jackson Hole and built bouldery moraines that commonly enclose lakes. On the southern margin of the GYGS, prominent glacial outwash terraces define three phases of the Pinedale glaciation in Jackson Hole: Pinedale-1 (Pd-1) by Antelope Flats with subdued channel patterns on the east side of Jackson Hole; Pinedale-2 (Pd-2) by a large outwash fan that includes Baseline Flat on the west side of Jackson Hole with well-defined channel patterns; and Pinedale-3 (Pd-3) by The Potholes and other outwash fans farther up the Snake River in central Jackson Hole. During Pinedale glaciation, three glacial lobes of the GYGS fed into Jackson Hole, and the relative importance of these lobes changed dramatically through time. During the Pd-1 glaciation, the eastern Buffalo Fork lobe dominated whereas in Pd-2 and Pd-3 time the northern Snake River lobe dominated. This is consistent with migration of the GYGS center of ice mass westward and southward as glaciers built up towards the moisture source provided by storms moving northeastward up the eastern Snake River Plain. The recession of the eastern Buffalo Fork lobe in Pd-2 and Pd-3 times is consistent with an enlarged ice mass on the Yellowstone Plateau that placed the eastern part of the GYGS in a precipitation or snow shadow.
In Pd-1 time, the Buffalo Fork lobe reached its maximum extent and was joined by the Pacific Creek lobe. This culmination may correlate with the ~21–18 ka ages of moraines in the Teton Range and nearby ranges. Three subdivisions of Pd-1 glaciation built moraines that are nearly or entirely covered by outwash almost 100 meters thick. In Pd-2 time, the Snake River lobe joined with the Pacific Creek lobe and built a large outwash fan south of the present-day Jackson Lake. Boulders on a moraine at the head of this fan are dated to 15.5 ± 0.5 ka. The relation between Teton glaciers and those of the GYGS is indicated by outwash from these Pd-2 moraines that partly buries outer Jenny Lake moraines dated to 15.2 ± 0.7 ka. East of the large outwash fan, Pd-2 ice advanced across the glacial-age Triangle X-2 lake sediments, perhaps in a surge. The Buffalo Fork lobe retreated more than 20 km up valley from its Pd-1 position and Pd-2 ice of the Snake River and Pacific Creek lobes advanced into the area previously occupied by the Buffalo Fork lobe. The Pd-3 position flanks the margin of Jackson Lake and represents a retreat to a stable position after the Pd-2 7-km advance that may have been a surge across the Triangle X-2 lake sediments. The Potholes and South Landing outwash fans were built in the area deglaciated by the retreat from Pd-2 to Pd-3 time. The Spalding Bay outwash fan continued to incise and a meltwater stream flowed just outside the Teton glacier that filled the present Jenny Lake and deposited the 14.4 ± 0.8 ka inner Jenny Lake moraines.
Glacial outwash terraces increase in slope toward their respective moraines of the GYGS and are complex in both north-south and east-west directions. The Pd-1 terrace slopes to the west where it is buried by the Pd-2 outwash. The post-depositional tilting of the Pd-1 outwash terrace is an order of magnitude smaller than the original westward depositional slope. The Pd-1, 2, and 3 terraces have a shingle-like geometry such that the highest terrace decreases in age down valley, and in southern Jackson Hole, the Pd-3 terrace is only 3–5 m above the Snake River.
In Pd-1 time the combined Buffalo Fork and Pacific Creek lobes scoured out four basins: (1) Emma Matilda Lake; (2) Two Ocean Lake; (3) a deep basin from lower Pacific Creek to beneath the Oxbows and Jackson Lake Dam; and (4) the largest basin from the lower Buffalo Fork to Deadmans Bar of the Snake River. These basins are largely filled with fine-grained sediment and are now marked by moist lowlands or lakes. In Pd-2 and Pd-3 time the Snake River lobe scoured the present 120-m deep Jackson Lake and possibly the 120-m deeper sediment-filled basin. Subglacial erosion of the Jackson Lake basin by confined water jets is supported by eskers that climb up to the head of the South Landing outwash fan.
Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS-480
Denver, CO 80225
Digital flood-inundation maps for a 9.5-mile reach of the Patoka River in and near the city of Jasper, southwestern Indiana (Ind.), from the streamgage near County Road North 175 East, downstream to State Road 162, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. The flood-inundation 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 Patoka River at Jasper, Ind. (station number 03375500). The Patoka streamgage is located at the upstream end of the 9.5-mile river reach. 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 Advanced Hydrologic Prediction Service at http://water.weather.gov/ahps/, although flood forecasts and stages for action and minor, moderate, and major flood stages are not currently (2017) available at this site (JPRI3).
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 Patoka River at Jasper, Ind., streamgage and the documented high-water marks from the flood of April 30, 2017. The calibrated hydraulic model was then used to compute five water-surface profiles for flood stages referenced to the streamgage datum ranging from 15 feet (ft), or near bankfull, to 19 ft. 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 ft vertical accuracy and 4.9 ft horizontal resolution) to delineate the area flooded at each water level.
The availability of these flood-inundation maps, along with real-time stage from the USGS streamgage at the Patoka River at Jasper, Ind., 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 postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175138","collaboration":"Prepared in cooperation with the Indiana Department of Transportation","usgsCitation":"Fowler, K.K., 2018, Flood-inundation maps for the Patoka River in and near Jasper, southwestern Indiana: U.S. Geological Survey Scientific Investigations Report 2017–5138, 11 p., https://doi.org/10.3133/sir20175138.","productDescription":"Report: vii, 11 p.; Data Release","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-086512","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":350479,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5138/coverthb.jpg"},{"id":350480,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5138/sir20175138.pdf","text":"Report","size":"34.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5138"},{"id":350481,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7862DX0","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial Datasets and Surface-Water Hydraulic Model for the Patoka River in and near Jasper, Southwest Indiana, Flood-inundation Study"}],"country":"United States","state":"Indiana","city":"Jasper","otherGeospatial":"Patoka River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.95,\n 38.360839624761944\n ],\n [\n -86.875,\n 38.360839624761944\n ],\n [\n -86.875,\n 38.425\n ],\n [\n -86.95,\n 38.425\n ],\n [\n -86.95,\n 38.360839624761944\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd
Indianapolis, IN 46278
We apply linear deconvolution methods to derive mineral and glass proportions for eight field sample training sites at seven dune fields: (1) Algodones, California; (2) Big Dune, Nevada; (3) Bruneau, Idaho; (4) Great Kobuk Sand Dunes, Alaska; (5) Great Sand Dunes National Park and Preserve, Colorado; (6) Sunset Crater, Arizona; and (7) White Sands National Monument, New Mexico. These dune fields were chosen because they represent a wide range of mineral grain mixtures and allow us to gauge a better understanding of both compositional and sorting effects within terrestrial and extraterrestrial dune systems. We also use actual ASTER TIR emissivity imagery to map the spatial distribution of these minerals throughout the seven dune fields and evaluate the effects of degraded spectral resolution on the accuracy of mineral abundances retrieved. Our results show that hyperspectral data convolutions of our laboratory emissivity spectra outperformed multispectral data convolutions of the same data with respect to the mineral, glass and lithic abundances derived. Both the number and wavelength position of spectral bands greatly impacts the accuracy of linear deconvolution retrieval of feldspar proportions (e.g. K-feldspar vs. plagioclase) especially, as well as the detection of certain mafic and carbonate minerals. In particular, ASTER mapping results show that several of the dune sites display patterns such that less dense minerals typically have higher abundances near the center of the active and most evolved dunes in the field, while more dense minerals and glasses appear to be more abundant along the margins of the active dune fields.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.aeolia.2017.12.001","usgsCitation":"Hubbard, B.E., Hooper, D.M., Solano, F., and Mars, J., 2018, Determining mineralogical variations of aeolian deposits using thermal infrared emissivity and linear deconvolution methods: Aeolian Research, v. 30, p. 54-96, https://doi.org/10.1016/j.aeolia.2017.12.001.","productDescription":"43 p.","startPage":"54","endPage":"96","ipdsId":"IP-080975","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":461075,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.aeolia.2017.12.001","text":"Publisher Index Page"},{"id":438055,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7MS3QWM","text":"USGS data release","linkHelpText":"Visible, Near Infrared, Shortwave Infrared and Thermal Infrared Laboratory Spectra of Samples of Compositionally Variable Dune Fields in the Western United States and Alaska"},{"id":438054,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7CC0XTR","text":"USGS data release","linkHelpText":"Linear Deconvolution Mineral Maps of Compositionally Variable Dune Fields in the Western United States and Alaska"},{"id":350459,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, Arizona, California, Colorado, Idaho, Nevada, New Mexico","otherGeospatial":"Algodones, Big Dune, Bruneau, Great Kobuk Sand Dunes, Great Sand Dunes National Park and Preserve, Sunset Crater, White Sands National Monument","volume":"30","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60e451e4b06e28e9c14067","contributors":{"authors":[{"text":"Hubbard, Bernard E. 0000-0002-9315-2032 bhubbard@usgs.gov","orcid":"https://orcid.org/0000-0002-9315-2032","contributorId":2342,"corporation":false,"usgs":true,"family":"Hubbard","given":"Bernard","email":"bhubbard@usgs.gov","middleInitial":"E.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":725517,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooper, Donald M.","contributorId":197205,"corporation":false,"usgs":false,"family":"Hooper","given":"Donald","email":"","middleInitial":"M.","affiliations":[{"id":35997,"text":"Southwest Research Institute, San Antonio, TX","active":true,"usgs":false},{"id":35998,"text":"WEX Foundation","active":true,"usgs":false}],"preferred":false,"id":725518,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Solano, Federico 0000-0002-0308-5850 fsolanoc@usgs.gov","orcid":"https://orcid.org/0000-0002-0308-5850","contributorId":4302,"corporation":false,"usgs":true,"family":"Solano","given":"Federico","email":"fsolanoc@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":725519,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mars, John C. jmars@usgs.gov","contributorId":127493,"corporation":false,"usgs":true,"family":"Mars","given":"John C.","email":"jmars@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":725520,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196290,"text":"70196290 - 2018 - International migration patterns of Red-throated Loons (Gavia stellata) from four breeding populations in Alaska","interactions":[],"lastModifiedDate":"2018-03-30T14:00:16","indexId":"70196290","displayToPublicDate":"2018-01-10T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"displayTitle":"International migration patterns of Red-throated Loons (Gavia stellata) from four breeding populations in Alaska","title":"International migration patterns of Red-throated Loons (Gavia stellata) from four breeding populations in Alaska","docAbstract":"Identifying post-breeding migration and wintering distributions of migratory birds is important for understanding factors that may drive population dynamics. Red-throated Loons (Gavia stellata) are widely distributed across Alaska and currently have varying population trends, including some populations with recent periods of decline. To investigate population differentiation and the location of migration pathways and wintering areas, which may inform population trend patterns, we used satellite transmitters (n = 32) to describe migration patterns of four geographically separate breeding populations of Red-throated Loons in Alaska. On average (± SD) Red-throated Loons underwent long (6,288 ± 1,825 km) fall and spring migrations predominantly along coastlines. The most northern population (Arctic Coastal Plain) migrated westward to East Asia and traveled approximately 2,000 km farther to wintering sites than the three more southerly populations (Seward Peninsula, Yukon-Kuskokwim Delta, and Copper River Delta) which migrated south along the Pacific coast of North America. These migration paths are consistent with the hypothesis that Red-throated Loons from the Arctic Coastal Plain are exposed to contaminants in East Asia. The three more southerly breeding populations demonstrated a chain migration pattern in which the more northerly breeding populations generally wintered in more northerly latitudes. Collectively, the migration paths observed in this study demonstrate that some geographically distinct breeding populations overlap in wintering distribution while others use highly different wintering areas. Red-throated Loon population trends in Alaska may therefore be driven by a wide range of effects throughout the annual cycle.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0189954","usgsCitation":"McCloskey, S., Uher-Koch, B.D., Schmutz, J.A., and Fondell, T., 2018, International migration patterns of Red-throated Loons (Gavia stellata) from four breeding populations in Alaska: PLoS ONE, v. 13, no. 1, p. 1-15, https://doi.org/10.1371/journal.pone.0189954.","productDescription":"e0189954; 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-090249","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":469099,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0189954","text":"Publisher Index Page"},{"id":438057,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TH8KVH","text":"USGS data release","linkHelpText":" Tracking data for Red-throated Loons (Gavia stellata)"},{"id":353018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","volume":"13","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-10","publicationStatus":"PW","scienceBaseUri":"5afee751e4b0da30c1bfc22c","contributors":{"authors":[{"text":"McCloskey, Sarah E. smccloskey@usgs.gov","contributorId":4850,"corporation":false,"usgs":true,"family":"McCloskey","given":"Sarah E.","email":"smccloskey@usgs.gov","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":732175,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Uher-Koch, Brian D. 0000-0002-1885-0260 buher-koch@usgs.gov","orcid":"https://orcid.org/0000-0002-1885-0260","contributorId":5117,"corporation":false,"usgs":true,"family":"Uher-Koch","given":"Brian","email":"buher-koch@usgs.gov","middleInitial":"D.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":732174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmutz, Joel A. 0000-0002-6516-0836 jschmutz@usgs.gov","orcid":"https://orcid.org/0000-0002-6516-0836","contributorId":1805,"corporation":false,"usgs":true,"family":"Schmutz","given":"Joel","email":"jschmutz@usgs.gov","middleInitial":"A.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":732176,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fondell, Thomas F. tfondell@usgs.gov","contributorId":139310,"corporation":false,"usgs":true,"family":"Fondell","given":"Thomas F.","email":"tfondell@usgs.gov","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":732177,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227940,"text":"70227940 - 2018 - River otter distribution in Nebraska","interactions":[],"lastModifiedDate":"2022-02-02T16:47:55.910735","indexId":"70227940","displayToPublicDate":"2018-01-07T10:44:40","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"River otter distribution in Nebraska","docAbstract":"The river otter (Lontra canadensis) was extirpated from Nebraska, USA, in the early 1900s and reintroduced starting in 1986. Information is needed regarding the distribution of river otters in Nebraska before decisions can be made regarding its conservation status. Understanding distribution of a species is critically important for effective management. We investigated river otter distribution in Nebraska with occupancy modeling and maximum entropy (Maxent) modeling using 190 otter sign observations on Nebraska's navigable rivers and 380 historical otter records from November 1977 to April 2014. Both methods identified the Platte River, Elkhorn River, central and eastern Niobrara River, and southern Loup River system as core areas within the distribution of otters in Nebraska. The Maxent model provided more liberal estimates of site occupancy and identified some smaller rivers as being within the distribution of otters in Nebraska, which were not identified using occupancy modeling. We recommend that multiple data sets and analysis methods be used to estimate species distribution because this allows for the broadest geographical coverage and decreases the likelihood of overlooking areas with fewer animal records. If further reintroduction efforts or translocation efforts are to take place in the future, we recommend focusing on areas with high modeled occupancy but few historical and survey records
","language":"English","publisher":"Wildlife Society","doi":"10.1002/wsb.843","usgsCitation":"Bieber, N.R., Wilson, S.P., and Allen, C.R., 2018, River otter distribution in Nebraska: Wildlife Society Bulletin, v. 42, no. 1, p. 136-143, https://doi.org/10.1002/wsb.843.","productDescription":"8 p.","startPage":"136","endPage":"143","ipdsId":"IP-094064","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":500069,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/08bde403cf22458cbb0e4cf09a97b49e","text":"External Repository"},{"id":395281,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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P.","contributorId":273159,"corporation":false,"usgs":false,"family":"Wilson","given":"S.","email":"","middleInitial":"P.","affiliations":[{"id":56368,"text":"Nebraska Game and Parks","active":true,"usgs":false}],"preferred":false,"id":832636,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allen, Craig R. 0000-0001-8655-8272 allencr@usgs.gov","orcid":"https://orcid.org/0000-0001-8655-8272","contributorId":1979,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"allencr@usgs.gov","middleInitial":"R.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":832637,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70263443,"text":"70263443 - 2018 - River otter distribution in Nebraska","interactions":[],"lastModifiedDate":"2025-02-11T15:28:17.418219","indexId":"70263443","displayToPublicDate":"2018-01-07T09:25:34","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"River otter distribution in Nebraska","docAbstract":"The river otter (Lontra canadensis) was extirpated from Nebraska, USA, in the early 1900s and reintroduced starting in 1986. Information is needed regarding the distribution of river otters in Nebraska before decisions can be made regarding its conservation status. Understanding distribution of a species is critically important for effective management. We investigated river otter distribution in Nebraska with occupancy modeling and maximum entropy (Maxent) modeling using 190 otter sign observations on Nebraska's navigable rivers and 380 historical otter records from November 1977 to April 2014. Both methods identified the Platte River, Elkhorn River, central and eastern Niobrara River, and southern Loup River system as core areas within the distribution of otters in Nebraska. The Maxent model provided more liberal estimates of site occupancy and identified some smaller rivers as being within the distribution of otters in Nebraska, which were not identified using occupancy modeling. We recommend that multiple data sets and analysis methods be used to estimate species distribution because this allows for the broadest geographical coverage and decreases the likelihood of overlooking areas with fewer animal records. If further reintroduction efforts or translocation efforts are to take place in the future, we recommend focusing on areas with high modeled occupancy but few historical and survey records.
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\"}}]}","volume":"42","issue":"1","noUsgsAuthors":false,"publicationDate":"2018-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Bieber, N.R.","contributorId":350797,"corporation":false,"usgs":false,"family":"Bieber","given":"N.R.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":927011,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, S.P.","contributorId":341215,"corporation":false,"usgs":false,"family":"Wilson","given":"S.P.","email":"","affiliations":[{"id":17640,"text":"Nebraska Game and Parks Commission","active":true,"usgs":false}],"preferred":false,"id":927012,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allen, Craig R. 0000-0001-8655-8272 allencr@usgs.gov","orcid":"https://orcid.org/0000-0001-8655-8272","contributorId":1979,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"allencr@usgs.gov","middleInitial":"R.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":927013,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188422,"text":"70188422 - 2018 - Extreme-event geoelectric hazard maps: Chapter 9","interactions":[],"lastModifiedDate":"2018-03-22T10:37:03","indexId":"70188422","displayToPublicDate":"2018-01-05T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Extreme-event geoelectric hazard maps: Chapter 9","docAbstract":"Maps of geoelectric amplitude covering about half the continental United States are presented that will be exceeded, on average, once per century in response to an extreme-intensity geomagnetic disturbance. These maps are constructed using an empirical parameterization of induction: convolving latitude-dependent statistical maps of extreme-value geomagnetic disturbances, obtained from decades of 1-minute magnetic observatory data, with local estimates of Earth-surface impedance obtained at discrete geographic sites from magnetotelluric surveys. Geoelectric amplitudes are estimated for geomagnetic waveforms having a 240-s (and 1200-s) sinusoidal period and amplitudes over 10 min (1 h) that exceed a once-per-century threshold. As a result of the combination of geographic differences in geomagnetic variation and Earth-surface impedance, once-per-century geoelectric amplitudes span more than two orders of magnitude and are a highly granular function of location. Specifically for north-south 240-s induction, once-per-century geoelectric amplitudes across large parts of the United States have a median value of 0.34 V/km; for east-west variation, they have a median value of 0.23 V/km. In Northern Minnesota, amplitudes exceed 14.00 V/km for north-south geomagnetic variation (23.34 V/km for east-west variation), while just over 100 km away, amplitudes are only 0.08 V/km (0.02 V/km). At some sites in the northern-central United States, once-per-century geoelectric amplitudes exceed the 2 V/km realized in Québec during the March 1989 storm.
Technological improvements in remote sensing and geographic information systems have demonstrated the abundance of artificially constructed water bodies across the landscape. Although research has shown the ubiquity of small ponds globally, and in the southeastern United States in particular, their cumulative impact in terms of evaporative alteration is less well quantified. The objectives of this study are to examine the hydrologic and evaporative importance of small artificial water bodies in the Upper Oconee watershed in the northern Georgia Piedmont, USA, by mapping their locations and modelling these small reservoirs using the Soil Water Assessment Tool. Comparative Soil Water Assessment Tool models were run with and without the inclusion of small reservoir surface area and volume. The models used meteorological inputs from 1990–2013 to represent years with drought, high precipitation, and moderate precipitation for both the calibration and evaluation periods. Statistical comparison of streamflow indicated that the calibration methodology produced results where the default model simulation without reservoirs fit observed flows more closely than the modified model with small reservoirs included (e.g., Nash–Sutcliffe efficiency of 0.72 vs. 0.64, r2 of 0.73 vs. 0.66, and percent bias of 11.4 vs. 21.6). In addition, Penman–Monteith, Hargreaves, and Priestley–Taylor evapotranspiration equations were used to estimate actual evaporation from 2,219 small water bodies identified throughout the 1,936.8 km2 watershed. Depending on the evaporation equation used, water bodies evaporated an average of 0.03–0.036 km3/year for the period 2003–2013. Using Penman–Monteith further, if the reservoirs were not considered and average actual evapotranspiration rates from the rest of the basin were applied, only 0.016 km3 of water would have left the basin as a result of evapotranspiration. This finding suggests construction of small reservoirs increased evaporation by an average of 0.017 km3 per year (approximately 46,500 m3/day). As the construction of small reservoirs continues and high resolution image data used to map these water bodies becomes increasingly available, watershed models that evolve to address the cumulative impacts of small water bodies on evaporation and other hydrologic processes will have greater potential to benefit the water resource management community.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.11398","usgsCitation":"Ignatius, A., and Jones, J., 2018, High resolution water body mapping for SWAT evaporative modelling in the Upper Oconee watershed of Georgia, USA: Hydrological Processes, v. 32, no. 1, p. 51-65, https://doi.org/10.1002/hyp.11398.","productDescription":"15 p.","startPage":"51","endPage":"65","ipdsId":"IP-073606","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":469108,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.11398","text":"Publisher Index Page"},{"id":358190,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Upper Oconee watershed","volume":"32","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-18","publicationStatus":"PW","scienceBaseUri":"5bc0304de4b0fc368eb539ec","contributors":{"authors":[{"text":"Ignatius, Amber R. 0000-0002-2636-836X","orcid":"https://orcid.org/0000-0002-2636-836X","contributorId":193407,"corporation":false,"usgs":false,"family":"Ignatius","given":"Amber R.","affiliations":[],"preferred":false,"id":747475,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":747474,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70198374,"text":"70198374 - 2018 - Barataria and Terrebonne Bays: Chapter F in Emergent wetlands status and trends in the northern Gulf of Mexico: 1950-2010","interactions":[],"lastModifiedDate":"2018-08-31T12:28:47","indexId":"70198374","displayToPublicDate":"2018-01-01T12:01:32","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"displayTitle":"Barataria and Terrebonne Bays: Chapter F in Emergent wetlands status and trends in the northern Gulf of Mexico: 1950-2010","title":"Barataria and Terrebonne Bays: Chapter F in Emergent wetlands status and trends in the northern Gulf of Mexico: 1950-2010","docAbstract":"The study area included in the Barataria and Terrebonne Bays vignette of\nsoutheastern Louisiana spans eastward from Terrebonne Bay to Barataria Bay (Figure 1)\nand includes portions of Terrebonne, Lafourche, St. Charles, Jefferson, Orleans,\nPlaquemines, and St. Bernard Parishes. This area falls between the Mississippi River on\nthe east and northeast, extends down through the western shore of Lake Salvador and the\nDixie Delta Canal, then runs west to Houma and follows Louisiana Route 315 to the\ncoast; Barataria and Terrebonne Bays are separated from each other by Bayou Lafourche.","largerWorkTitle":"Emergent Wetlands Status and Trends in the Northern Gulf of Mexico: 1950-2010 report","conferenceTitle":"2013 Gulf of Mexico Alliance (GOMA) All Hands Meeting","conferenceDate":"June 25-27, 2013","language":"English","usgsCitation":"Lawrence Handley, Spear, K.A., Zapletal, M., Thatcher, C.A., Jones, W.R., and Wilson, S.A., 2018, Barataria and Terrebonne Bays: Chapter F in Emergent wetlands status and trends in the northern Gulf of Mexico: 1950-2010, 25 p.","productDescription":"25 p.","startPage":"1","endPage":"24","ipdsId":"IP-096196","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":357000,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":356093,"type":{"id":15,"text":"Index Page"},"url":"https://gom.usgs.gov/web/documents/Chapter_F_BaratariaTerrebonneBays.pdf"}],"country":"United States","state":"Louisiana ","otherGeospatial":"Barataria Bay; Terrebonne Bay","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b98a317e4b0702d0e843028","contributors":{"authors":[{"text":"Lawrence Handley","contributorId":206612,"corporation":false,"usgs":false,"family":"Lawrence Handley","affiliations":[{"id":7065,"text":"USGS emeritus","active":true,"usgs":false}],"preferred":false,"id":741279,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spear, Kathryn A. 0000-0001-8942-2856 speark@usgs.gov","orcid":"https://orcid.org/0000-0001-8942-2856","contributorId":1949,"corporation":false,"usgs":true,"family":"Spear","given":"Kathryn","email":"speark@usgs.gov","middleInitial":"A.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":741278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zapletal, Mirka","contributorId":206613,"corporation":false,"usgs":false,"family":"Zapletal","given":"Mirka","email":"","affiliations":[{"id":25340,"text":"Cherokee Nation Technologies","active":true,"usgs":false}],"preferred":false,"id":741280,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thatcher, Cindy A. 0000-0003-0331-071X thatcherc@usgs.gov","orcid":"https://orcid.org/0000-0003-0331-071X","contributorId":2868,"corporation":false,"usgs":true,"family":"Thatcher","given":"Cindy","email":"thatcherc@usgs.gov","middleInitial":"A.","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":false,"id":741281,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, William R. 0000-0002-5493-4138 jonesb@usgs.gov","orcid":"https://orcid.org/0000-0002-5493-4138","contributorId":463,"corporation":false,"usgs":true,"family":"Jones","given":"William","email":"jonesb@usgs.gov","middleInitial":"R.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":741282,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wilson, Scott A. 0000-0001-8055-8618 wilsons@usgs.gov","orcid":"https://orcid.org/0000-0001-8055-8618","contributorId":2360,"corporation":false,"usgs":true,"family":"Wilson","given":"Scott","email":"wilsons@usgs.gov","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":741283,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70194660,"text":"70194660 - 2018 - Strain partitioning in southeastern Alaska: Is the Chatham Strait Fault active?","interactions":[],"lastModifiedDate":"2018-03-29T16:02:02","indexId":"70194660","displayToPublicDate":"2018-01-01T00:00:00","publicationYear":"2018","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":"Strain partitioning in southeastern Alaska: Is the Chatham Strait Fault active?","docAbstract":"A 1200 km-long transform plate boundary passes through southeastern Alaska and northwestern British Columbia and represents one of the most seismically active, but poorly understood continental margins of North America. Although most of the plate motion is accommodated by the right-lateral Queen Charlotte–Fairweather Fault (QCFF) System, which has produced at least six M > 7 earthquakes since 1920, seismic hazard assessments also include the Chatham Strait Fault (CSF) as a potentially active, 400 km-long strike slip fault that cuts northward through southeastern Alaska, connecting with the Eastern Denali Fault. Nearly the entire length of the CSF is submerged beneath Chatham Strait and Lynn Canal and has never been systematically imaged using high-resolution marine geophysical approaches. In this study we present an integrated analysis of new marine seismic reflectiondata acquired across Lynn Canal and tectonic block modeling constrained by data from continuous and campaign GPS sites. Seismic profiles cross the CSF at twelve locations spanning ∼50 km of fault length; they reveal thick (up to 300 m) packages of glaciomarine sedimentary facies emplaced on an unconformity surface that formed during the Last Glacial Maximum (LGM). Localized warping of post-LGM stratigraphy (∼13.9 kyr B.P. to present) appears to correlate with sediment drape on basement topography and current-controlled deposition. There is no evidence for an active fault along the axis of Lynn Canal in the seismic reflection data. Crustal block models constrained by GPS data allow, but do not require, a maximum slip rate of 2–3 mm/yr along the CSF; higher slip rates on the CSF result in significant misfit to GPS data in the surrounding region. Based on the combined marine geophysical and GPS observations, it is plausible that the CSF has not generated resolvable coseismic deformation in the last ∼13 ka and that the modern slip-rate is <1 mm/yr. We propose that models for strain transfer between the QCFF and the Denali Fault, and seismic hazard maps in general, may need to be reevaluated.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2017.10.017","usgsCitation":"Brothers, D.S., Elliott, J.L., Conrad, J.E., Haeussler, P.J., and Kluesner, J.W., 2018, Strain partitioning in southeastern Alaska: Is the Chatham Strait Fault active?: Earth and Planetary Science Letters, v. 481, p. 362-371, https://doi.org/10.1016/j.epsl.2017.10.017.","productDescription":"10 p.","startPage":"362","endPage":"371","ipdsId":"IP-081661","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":469122,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2017.10.017","text":"Publisher Index Page"},{"id":352971,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","volume":"481","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee755e4b0da30c1bfc267","contributors":{"authors":[{"text":"Brothers, Daniel S. 0000-0001-7702-157X dbrothers@usgs.gov","orcid":"https://orcid.org/0000-0001-7702-157X","contributorId":167089,"corporation":false,"usgs":true,"family":"Brothers","given":"Daniel","email":"dbrothers@usgs.gov","middleInitial":"S.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":724809,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elliott, Julie L.","contributorId":201260,"corporation":false,"usgs":false,"family":"Elliott","given":"Julie","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":724810,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conrad, James E. 0000-0001-6655-694X jconrad@usgs.gov","orcid":"https://orcid.org/0000-0001-6655-694X","contributorId":2316,"corporation":false,"usgs":true,"family":"Conrad","given":"James","email":"jconrad@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":724811,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haeussler, Peter J. 0000-0002-1503-6247 pheuslr@usgs.gov","orcid":"https://orcid.org/0000-0002-1503-6247","contributorId":503,"corporation":false,"usgs":true,"family":"Haeussler","given":"Peter","email":"pheuslr@usgs.gov","middleInitial":"J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":724813,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kluesner, Jared W. 0000-0003-1701-8832 jkluesner@usgs.gov","orcid":"https://orcid.org/0000-0003-1701-8832","contributorId":201261,"corporation":false,"usgs":true,"family":"Kluesner","given":"Jared","email":"jkluesner@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":724812,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70193307,"text":"70193307 - 2018 - Lead and strontium isotopes as monitors of anthropogenic contaminants in the surficial environment","interactions":[],"lastModifiedDate":"2020-08-20T17:00:35.524226","indexId":"70193307","displayToPublicDate":"2017-12-08T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"12","title":"Lead and strontium isotopes as monitors of anthropogenic contaminants in the surficial environment","docAbstract":"Isotopic discrimination can be an effective tool in establishing a direct link between sources of Pb contamination and the presence of anomalously high concentrations of Pb in waters, soils, and organisms. Residential wells supplying water containing up to 1600 ppb Pb to houses built on the former Mohr orchards commercial site, near Allentown, Pennsylvania, United States, were evaluated to discern anthropogenic from geogenic sources. Pb and Sr isotopic data and REE data were determined for waters from residential wells, test wells (drilled for this study), and surface waters from pond and creeks. Local soils, sediments, bedrock, Zn-Pb mineralization and coal were also analyzed, together with locally used Pb-As pesticide. Pb isotope data for residential wells, test wells, and surface waters show substantial overlap with Pb data reflecting anthropogenic actions (e.g., burning fossil fuels, industrial and urban processing activities). Limited contributions of Pb from bedrock, soils, and pesticides are evident. High Pb concentrations in the residential waters are likely related to Pb in groundwater accumulating in sediment in the residential water tanks. The Pb isotope features of waters in underlying shallow aquifers that supply residential wells in the region are best interpreted as reflecting a legacy of anthropogenic Pb rather than geogenic Pb.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Environmental Geochemistry","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-444-63763-5.00013-6","usgsCitation":"Ayuso, R.A., and Foley, N.K., 2018, Lead and strontium isotopes as monitors of anthropogenic contaminants in the surficial environment, chap. 12 of Environmental Geochemistry, p. 307-362, https://doi.org/10.1016/B978-0-444-63763-5.00013-6.","productDescription":"56 p.","startPage":"307","endPage":"362","ipdsId":"IP-082091","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":349929,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fad4e4b06e28e9c22765","contributors":{"authors":[{"text":"Ayuso, Robert A. 0000-0002-8496-9534 rayuso@usgs.gov","orcid":"https://orcid.org/0000-0002-8496-9534","contributorId":2654,"corporation":false,"usgs":true,"family":"Ayuso","given":"Robert","email":"rayuso@usgs.gov","middleInitial":"A.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":718623,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foley, Nora K. 0000-0003-0124-3509 nfoley@usgs.gov","orcid":"https://orcid.org/0000-0003-0124-3509","contributorId":4010,"corporation":false,"usgs":true,"family":"Foley","given":"Nora","email":"nfoley@usgs.gov","middleInitial":"K.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":718624,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70194558,"text":"sir20175109 - 2018 - Sequence stratigraphy, seismic stratigraphy, and seismic structures of the lower intermediate confining unit and most of the Floridan aquifer system, Broward County, Florida","interactions":[],"lastModifiedDate":"2018-01-25T09:03:53","indexId":"sir20175109","displayToPublicDate":"2017-12-08T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5109","title":"Sequence stratigraphy, seismic stratigraphy, and seismic structures of the lower intermediate confining unit and most of the Floridan aquifer system, Broward County, Florida","docAbstract":"Deep well injection and disposal of treated wastewater into the highly transmissive saline Boulder Zone in the lower part of the Floridan aquifer system began in 1971. The zone of injection is a highly transmissive hydrogeologic unit, the Boulder Zone, in the lower part of the Floridan aquifer system. Since the 1990s, however, treated wastewater injection into the Boulder Zone in southeastern Florida has been detected at three treated wastewater injection utilities in the brackish upper part of the Floridan aquifer system designated for potential use as drinking water. At a time when usage of the Boulder Zone for treated wastewater disposal is increasing and the utilization of the upper part of the Floridan aquifer system for drinking water is intensifying, there is an urgency to understand the nature of cross-formational fluid flow and identify possible fluid pathways from the lower to upper zones of the Floridan aquifer system. To better understand the hydrogeologic controls on groundwater movement through the Floridan aquifer system in southeastern Florida, the U.S. Geological Survey and the Broward County Environmental Planning and Community Resilience Division conducted a 3.5-year cooperative study from July 2012 to December 2015. The study characterizes the sequence stratigraphy, seismic stratigraphy, and seismic structures of the lower part of the intermediate confining unit aquifer and most of the Floridan aquifer system.
Data obtained to meet the study objective include 80 miles of high-resolution, two-dimensional (2D), seismic-reflection profiles acquired from canals in eastern Broward County. These profiles have been used to characterize the sequence stratigraphy, seismic stratigraphy, and seismic structures in a 425-square-mile study area. Horizon mapping of the seismic-reflection profiles and additional data collection from well logs and cores or cuttings from 44 wells were focused on construction of three-dimensional (3D) visualizations of eight sequence stratigraphic cycles that compose the Eocene to Miocene Oldsmar, Avon Park, and Arcadia Formations. The mapping of these seismic-reflection and well data has produced a refined Cenozoic sequence stratigraphic, seismic stratigraphic, and hydrogeologic framework of southeastern Florida. The upward transition from the Oldsmar Formation to the Avon Park Formation and the Arcadia Formation embodies the evolution from (1) a tropical to subtropical, shallow-marine, carbonate platform, represented by the Oldsmar and Avon Park Formations, to (2) a broad, temperate, mixed carbonate-siliciclastic shallow marine shelf, represented by the lower part of the Arcadia Formation, and to (3) a temperate, distally steepened carbonate ramp represented by the upper part of the Arcadia Formation.
In the study area, the depositional sequences and seismic sequences have a direct correlation with hydrogeologic units. The approximate upper boundary of four principal permeable units of the Floridan aquifer system (Upper Floridan aquifer, Avon Park permeable zone, uppermost major permeable zone of the Lower Floridan aquifer, and Boulder Zone) have sequence stratigraphic and seismic-reflection signatures that were identified on cross sections, mapped, or both, and therefore the sequence stratigraphy and seismic stratigraphy were used to guide the development of a refined spatial representation of these hydrogeologic units. In all cases, the permeability of the four permeable units is related to stratiform megaporosity generated by ancient dissolution of carbonate rock associated with subaerial exposure and unconformities at the upper surfaces of carbonate depositional cycles of several hierarchical scales ranging from high-frequency cycles to depositional sequences. Additionally, interparticle porosity also contributes substantially to the stratiform permeability in much of the Upper Floridan aquifer. Information from seismic stratigraphy allowed 3D geomodeling of hydrogeologic units—an approach never before applied to this area. Notably, the 3D geomodeling provided 3D visualizations and geocellular models of the depositional sequences, hydrostratigraphy, and structural features. The geocellular data could be used to update the hydrogeologic structure inherent to groundwater flow simulations that are designed to address the sustainability of the water resources of the Floridan aquifer system.
Two kinds of pathways that could enable upward cross-formational flow of injected treated wastewater from the Boulder Zone have been identified in the 80 miles of high-resolution seismic data collected for this study: a near-vertical reverse fault and karst collapse structures. The single reverse fault, inferred to be of tectonic origin, is in extreme northeastern Broward County and has an offset of about 19 feet at the level of the Arcadia Formation. Most of the 17 karst collapse structures identified manifest as columniform, vertically stacked sagging seismic reflections that span early Eocene to Miocene age rocks equivalent to much of the Floridan aquifer system and the lower part of the overlying intermediate confining unit. In some cases, the seismic-sag structures extend upward into strata of Pliocene age. The seismic-sag structures are interpreted to have a semicircular shape in plan view on the basis of comparison to (1) other seismic-sag structures in southeastern Florida mapped with two 2D seismic cross lines or 3D data, (2) comparison to these structures located in other carbonate provinces, and (3) plausible extensional ring faults detected with multi-attribute analysis. The seismic-sag structures in the study area have heights as great as 2,500 vertical feet, though importantly, one spans about 7,800 feet. Both multi-attribute analysis and visual detection of offset of seismic reflections within the seismic-sag structures indicate faults and fractures are associated with many of the structures. Multi-attribute analysis highlighting chimney fluid pathways also indicates that the seismic-sag structures have a high probability for potential vertical cross-formational fluid flow along the faulted and fractured structures. A collapse of the seismic-sag structures within a deep burial setting evokes an origin related to hypogenic karst processes by ascending flow of subsurface fluids. In addition, paleo-epigenic karst related to major regional subaerial unconformities within the Florida Platform generated collapse structures (paleo-sinkholes) that are much smaller in scale than the cross-formational seismic-sag structures.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175109","collaboration":"Prepared in cooperation with Broward County Environmental Planning and Community Resilience Division, Florida","usgsCitation":"Cunningham, K.J., Kluesner, J.W., Westcott, R.L., Robinson, Edward, Walker, Cameron, and Khan, S.A., 2018, Sequence stratigraphy, seismic stratigraphy, and seismic structures of the lower intermediate confining unit and most of the Floridan aquifer system, Broward County, Florida (ver. 1.1, January 2018): U.S. Geological Survey Scientific Investigations Report 2017–5109, 71 p., 21 pls., https://doi.org/10.3133/sir20175109.","productDescription":"Report: ix, 71 p.; 21 Plates; 2 Data Releases","numberOfPages":"86","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-066339","costCenters":[{"id":269,"text":"FLWSC-Ft. 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U.S. Geological Survey
4446 Pet Lane, Suite 108
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The salt marshes of Jamaica Bay serve as a recreational outlet for New York City residents, mitigate wave impacts during coastal storms, and provide habitat for critical wildlife species. Hurricanes have been recognized as one of the critical drivers of coastal wetland morphology due to their effects on hydrodynamics and sediment transport, deposition, and erosion processes. In this study, the Delft3D modeling suite was utilized to examine the effects of Hurricane Sandy (2012) on salt marsh morphology in Jamaica Bay. Observed marsh elevation change and accretion from rod Surface Elevation Tables and feldspar Marker Horizons (SET-MH) and hydrodynamic measurements during Hurricane Sandy were used to calibrate and validate the wind-waves-surge-sediment transport-morphology coupled model. The model results agreed well with in situ field measurements. The validated model was then used to detect salt marsh morphological change due to Sandy across Jamaica Bay. Model results indicate that the island-wide morphological changes in the bay's salt marshes due to Sandy were in the range of −30 mm (erosion) to +15 mm (deposition), and spatially complex and heterogeneous. The storm generated paired deposition and erosion patches at local scales. Salt marshes inside the west section of the bay showed erosion overall while marshes inside the east section showed deposition from Sandy. The net sediment amount that Sandy brought into the bay is only about 1% of the total amount of reworked sediment within the bay during the storm. Numerical experiments show that waves and vegetation played a critical role in sediment transport and associated wetland morphological change in Jamaica Bay. Furthermore, without the protection of vegetation, the marsh islands of Jamaica Bay would experience both more erosion and less accretion in coastal storms.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2017.11.001","usgsCitation":"Hu, K., Chen, Q., Wang, H., Hartig, E., and Orton, P.M., 2018, Numerical modeling of salt marsh morphological change induced by Hurricane Sandy: Coastal Engineering, v. 132, p. 63-81, https://doi.org/10.1016/j.coastaleng.2017.11.001.","productDescription":"19 p.","startPage":"63","endPage":"81","ipdsId":"IP-083439","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":469146,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coastaleng.2017.11.001","text":"Publisher Index Page"},{"id":349863,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Jamaica Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.81782531738281,\n 40.65147128144057\n ],\n [\n -73.85181427001953,\n 40.648085029646715\n ],\n [\n -73.87687683105469,\n 40.64079098062354\n ],\n [\n -73.90193939208984,\n 40.627763910481185\n ],\n [\n -73.91189575195312,\n 40.60092013543081\n ],\n [\n -73.89644622802734,\n 40.577977105192225\n ],\n [\n -73.86932373046875,\n 40.57093618838665\n ],\n [\n -73.81473541259766,\n 40.58814601026153\n ],\n [\n -73.76667022705078,\n 40.595706501568905\n ],\n [\n -73.75980377197266,\n 40.622291783092706\n ],\n [\n -73.81782531738281,\n 40.65147128144057\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"132","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fad5e4b06e28e9c2276e","contributors":{"authors":[{"text":"Hu, Kelin","contributorId":177218,"corporation":false,"usgs":false,"family":"Hu","given":"Kelin","email":"","affiliations":[],"preferred":false,"id":724671,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chen, Q. 0000-0002-6540-8758","orcid":"https://orcid.org/0000-0002-6540-8758","contributorId":56532,"corporation":false,"usgs":false,"family":"Chen","given":"Q.","affiliations":[{"id":38331,"text":"Northeastern University","active":true,"usgs":false}],"preferred":true,"id":724672,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Hongqing 0000-0002-2977-7732 wangh@usgs.gov","orcid":"https://orcid.org/0000-0002-2977-7732","contributorId":140432,"corporation":false,"usgs":true,"family":"Wang","given":"Hongqing","email":"wangh@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":724670,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hartig, Ellen K.","contributorId":179351,"corporation":false,"usgs":false,"family":"Hartig","given":"Ellen K.","affiliations":[],"preferred":false,"id":724673,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Orton, Philip M.","contributorId":179354,"corporation":false,"usgs":false,"family":"Orton","given":"Philip","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":724674,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208983,"text":"70208983 - 2018 - Mapping of compositional properties of coal using isometric log-ratio transformation and sequential Gaussian simulation – A comparative study for spatial ultimate analyses data","interactions":[],"lastModifiedDate":"2020-03-10T06:30:24","indexId":"70208983","displayToPublicDate":"2017-12-05T06:24:07","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2302,"text":"Journal of Geochemical Exploration","active":true,"publicationSubtype":{"id":10}},"title":"Mapping of compositional properties of coal using isometric log-ratio transformation and sequential Gaussian simulation – A comparative study for spatial ultimate analyses data","docAbstract":"Chemical properties of coal largely determine coal handling, processing, beneficiation methods, and design of coal-fired power plants. Furthermore, these properties impact coal strength, coal blending during mining, as well as coal's gas content, which is important for mining safety. In order for these processes and quantitative predictions to be successful, safer, and economically feasible, it is important to determine and map chemical properties of coals accurately in order to infer these properties prior to mining.
Ultimate analysis quantifies principal chemical elements in coal. These elements are C, H, N, S, O, and, depending on the basis, ash, and/or moisture. The basis for the data is determined by the condition of the sample at the time of analysis, with an “as-received” basis being the closest to sampling conditions and thus to the in-situ conditions of the coal. The parts determined or calculated as the result of ultimate analyses are compositions, reported in weight percent, and pose the challenges of statistical analyses of compositional data. The treatment of parts using proper compositional methods may be even more important in mapping them, as most mapping methods carry uncertainty due to partial sampling as well.
In this work, we map the ultimate analyses parts of the Springfield coal from an Indiana section of the Illinois basin, USA, using sequential Gaussian simulation of isometric log-ratio transformed compositions. We compare the results with those of direct simulations of compositional parts. We also compare the implications of these approaches in calculating other properties using correlations to identify the differences and consequences. Although the study here is for coal, the methods described in the paper are applicable to any situation involving compositional data and its mapping.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gexplo.2017.11.022","usgsCitation":"Karacan, C.O., and Olea, R.A., 2018, Mapping of compositional properties of coal using isometric log-ratio transformation and sequential Gaussian simulation – A comparative study for spatial ultimate analyses data: Journal of Geochemical Exploration, v. 186, p. 36-49, https://doi.org/10.1016/j.gexplo.2017.11.022.","productDescription":"14 p.","startPage":"36","endPage":"49","ipdsId":"IP-085076","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":469151,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/5743214","text":"External Repository"},{"id":373033,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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U.S. Geological Survey
Mail Stop 956
12201 Sunrise Valley Drive
Reston, VA 20192
https://energy.usgs.gov/
Identifying areas where deteriorating sewer infrastructure is in close proximity to surface waterways is needed to map likely connections between sewers and streams. We present a method to estimate sewer installation year and deterioration status using historical maps of the sewer network, parcel-scale property assessment data, and pipe material. Areas where streams were likely buried into the sewer system were mapped by intersecting the historical stream network derived from a 10-m resolution digital elevation model with sewer pipe locations. Potential sewer leakage hotspots were mapped by identifying where aging sewer pipes are in close proximity (50-m) to surface waterways. Results from Pittsburgh, Pennsylvania (USA), indicated 41% of the historical stream length was lost or buried and the potential interface between sewers and streams is great. The co-location of aging sewer infrastructure (>75 years old) near stream channels suggests that 42% of existing streams are located in areas with a high potential for sewer leakage if sewer infrastructure fails. Mapping the sewer-stream interface provides an approach to better understand areas were failing sewers may contribute a disproportional amount of nutrients and other pathogens to surface waterways.
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