When fed ad libitum, ursids can maximize mass gain by selecting mixed diets wherein protein provides 17 ± 4% of digestible energy, relative to carbohydrates or lipids. In the wild, this ability is likely constrained by seasonal food availability, limits of intake rate as body size increases, and competition. By visiting locations of 37 individuals during 274 bear-days, we documented foods consumed by grizzly (Ursus arctos) and black bears (Ursus americanus) in Grand Teton National Park during 2004–2006. Based on published nutritional data, we estimated foods and macronutrients as percentages of daily energy intake. Using principal components and cluster analyses, we identified 14 daily diet types. Only 4 diets, accounting for 21% of days, provided protein levels within the optimal range. Nine diets (75% of days) led to over-consumption of protein, and 1 diet (3% of days) led to under-consumption. Highest protein levels were associated with animal matter (i.e., insects, vertebrates), which accounted for 46–47% of daily energy for both species. As predicted: 1) daily diets dominated by high-energy vertebrates were positively associated with grizzly bears and mean percent protein intake was positively associated with body mass; 2) diets dominated by low-protein fruits were positively associated with smaller-bodied black bears; and 3) mean protein was highest during spring, when high-energy plant foods were scarce, however it was also higher than optimal during summer and fall. Contrary to our prediction: 4) allopatric black bears did not exhibit food selection for high-energy foods similar to grizzly bears. Although optimal gain of body mass was typically constrained, bears usually opted for the energetically superior trade-off of consuming high-energy, high-protein foods. Given protein digestion efficiency similar to obligate carnivores, this choice likely supported mass gain, consistent with studies showing monthly increases in percent body fat among bears in this region.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0153702","usgsCitation":"Costello, C., Cain, S.L., Pils, S.R., Frattaroli, L., Haroldson, M.A., and van Manen, F.T., 2016, Diet and macronutrient optimization in wild ursids: A comparison of grizzly bears with sympatric and allopatric black bears: PLoS ONE, v. 11, no. 5, p. 1-22, https://doi.org/10.1371/journal.pone.0153702.","productDescription":"e0153702; 22 p.","startPage":"1","endPage":"22","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070131","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":470971,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0153702","text":"Publisher Index Page"},{"id":321484,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Grand Teton National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.79437255859375,\n 43.63706904996992\n ],\n [\n -109.90310668945312,\n 43.65594991256823\n ],\n [\n -110.52520751953125,\n 44.36313311380771\n ],\n [\n -111.05529785156249,\n 44.228472525527614\n ],\n [\n -111.05117797851562,\n 44.19205137735955\n ],\n [\n -110.79437255859375,\n 43.63706904996992\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-18","publicationStatus":"PW","scienceBaseUri":"57441b9ce4b07e28b660daba","contributors":{"authors":[{"text":"Costello, Cecily M.","contributorId":145510,"corporation":false,"usgs":false,"family":"Costello","given":"Cecily M.","affiliations":[{"id":5117,"text":"University of Montana, College of Forestry and Conservation, University Hall, Room 309, Missoula, MT 59812, USA","active":true,"usgs":false}],"preferred":false,"id":630012,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cain, Steven L.","contributorId":145511,"corporation":false,"usgs":false,"family":"Cain","given":"Steven","email":"","middleInitial":"L.","affiliations":[{"id":16139,"text":"National Park Service, Grand Teton National Park, Moose, Wyoming 83012, USA","active":true,"usgs":false}],"preferred":false,"id":630013,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pils, Shannon R","contributorId":167609,"corporation":false,"usgs":false,"family":"Pils","given":"Shannon","email":"","middleInitial":"R","affiliations":[{"id":24778,"text":"US Forest Service, Shoshone National Forest, Wapiti Ranger District, 203A Yellowstone Avenue, Cody, WY 82414,USA","active":true,"usgs":false}],"preferred":false,"id":630014,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frattaroli, Leslie","contributorId":169550,"corporation":false,"usgs":false,"family":"Frattaroli","given":"Leslie","email":"","affiliations":[{"id":5124,"text":"Grand Teton National Park, P.O. Box 170, Moose, WY 83012","active":true,"usgs":false}],"preferred":false,"id":630015,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haroldson, Mark A. 0000-0002-7457-7676 mharoldson@usgs.gov","orcid":"https://orcid.org/0000-0002-7457-7676","contributorId":1773,"corporation":false,"usgs":true,"family":"Haroldson","given":"Mark","email":"mharoldson@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":630016,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"van Manen, Frank T. 0000-0001-5340-8489 fvanmanen@usgs.gov","orcid":"https://orcid.org/0000-0001-5340-8489","contributorId":2267,"corporation":false,"usgs":true,"family":"van Manen","given":"Frank","email":"fvanmanen@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":630017,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70170865,"text":"ofr20161046 - 2016 - Algorithms used in the Airborne Lidar Processing System (ALPS)","interactions":[],"lastModifiedDate":"2016-05-23T15:51:47","indexId":"ofr20161046","displayToPublicDate":"2016-05-23T10:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1046","title":"Algorithms used in the Airborne Lidar Processing System (ALPS)","docAbstract":"The Airborne Lidar Processing System (ALPS) analyzes Experimental Advanced Airborne Research Lidar (EAARL) data—digitized laser-return waveforms, position, and attitude data—to derive point clouds of target surfaces. A full-waveform airborne lidar system, the EAARL seamlessly and simultaneously collects mixed environment data, including submerged, sub-aerial bare earth, and vegetation-covered topographies.
ALPS uses three waveform target-detection algorithms to determine target positions within a given waveform: centroid analysis, leading edge detection, and bottom detection using water-column backscatter modeling. The centroid analysis algorithm detects opaque hard surfaces. The leading edge algorithm detects topography beneath vegetation and shallow, submerged topography. The bottom detection algorithm uses water-column backscatter modeling for deeper submerged topography in turbid water.
The report describes slant range calculations and explains how ALPS uses laser range and orientation measurements to project measurement points into the Universal Transverse Mercator coordinate system. Parameters used for coordinate transformations in ALPS are described, as are Interactive Data Language-based methods for gridding EAARL point cloud data to derive digital elevation models. Noise reduction in point clouds through use of a random consensus filter is explained, and detailed pseudocode, mathematical equations, and Yorick source code accompany the report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161046","usgsCitation":"Nagle, David B., and Wright, C. Wayne, 2016, Algorithms used in the Airborne Lidar Processing System (ALPS):\nU.S. Geological Survey Open-File Report, 2016–1046, 45 p., https://dx.doi.org/10.3133/ofr20161046.","productDescription":"x, 45 p.","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-063528","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":321007,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1046/ofr20161046.pdf","text":"Report","size":"1.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1046"},{"id":321006,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1046/coverthb.jpg"}],"contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
(727) 502–8000
http://coastal.er.usgs.gov/
Climate change vulnerability assessments (CCVAs) are valuable tools for assessing species’ vulnerability to climatic changes, yet failure to include measures of adaptive capacity and to account for sources of uncertainty may limit their effectiveness. Here, we provide a more comprehensive CCVA approach that incorporates all three elements used for assessing species’ climate change vulnerability: exposure, sensitivity, and adaptive capacity. We illustrate our approach using case studies of two threatened salmonids with different life histories – anadromous steelhead trout (Oncorhynchus mykiss) and non-anadromous bull trout (Salvelinus confluentus) – within the Columbia River Basin, USA. We identified general patterns of high vulnerability in low-elevation and southernmost habitats for both species. However, vulnerability rankings varied widely depending on the factors (climate, habitat, demographic, and genetic) included in the CCVA and often differed for the two species at locations where they were sympatric. Our findings illustrate that CCVA results are highly sensitive to data inputs and that spatial differences can complicate multi-species conservation. Our results highlight how CCVAs should be considered within a broader conceptual and computational framework for refining hypotheses, guiding research, and comparing plausible scenarios of species’ vulnerability for ongoing and projected climate change.
","language":"English","publisher":"Society for Conservation Biology","publisherLocation":"Malden, MA","doi":"10.1111/cobi.12764","usgsCitation":"Wade, A., Hand, B.K., Kovach, R., Luikart, G., Whited, D., and Muhlfeld, C.C., 2016, Accounting for adaptive capacity and uncertainty in assessments of species’ climate-change vulnerability: Conservation Biology, v. 31, no. 1, p. 136-149, https://doi.org/10.1111/cobi.12764.","productDescription":"14 p.","startPage":"136","endPage":"149","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072886","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":498973,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/cobi.12764","text":"Publisher Index Page"},{"id":326091,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Columbia River Basin","volume":"31","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-27","publicationStatus":"PW","scienceBaseUri":"57a4672ee4b0ebae89b63ca1","contributors":{"authors":[{"text":"Wade, Alisa A.","contributorId":145917,"corporation":false,"usgs":false,"family":"Wade","given":"Alisa A.","affiliations":[{"id":16296,"text":"University of Montana, Polson Montana 59860 USA","active":true,"usgs":false}],"preferred":false,"id":644689,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hand, Brian K.","contributorId":145915,"corporation":false,"usgs":false,"family":"Hand","given":"Brian","email":"","middleInitial":"K.","affiliations":[{"id":16296,"text":"University of Montana, Polson Montana 59860 USA","active":true,"usgs":false}],"preferred":false,"id":644690,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kovach, Ryan 0000-0001-5402-2123 rkovach@usgs.gov","orcid":"https://orcid.org/0000-0001-5402-2123","contributorId":145914,"corporation":false,"usgs":true,"family":"Kovach","given":"Ryan","email":"rkovach@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":644691,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Luikart, Gordon","contributorId":97409,"corporation":false,"usgs":false,"family":"Luikart","given":"Gordon","affiliations":[{"id":6580,"text":"University of Montana, Flathead Lake Biological Station, Polson, Montana 59860, USA","active":true,"usgs":false}],"preferred":false,"id":644692,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whited, Diane","contributorId":126718,"corporation":false,"usgs":false,"family":"Whited","given":"Diane","affiliations":[{"id":6576,"text":"Flathead Lake Biological Station, University of Montana, Polson, MT 59860, USA","active":true,"usgs":false}],"preferred":false,"id":644693,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Muhlfeld, Clint C. 0000-0002-4599-4059 cmuhlfeld@usgs.gov","orcid":"https://orcid.org/0000-0002-4599-4059","contributorId":924,"corporation":false,"usgs":true,"family":"Muhlfeld","given":"Clint","email":"cmuhlfeld@usgs.gov","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":644688,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70168682,"text":"pp1822 - 2016 - Late Holocene volcanism at Medicine Lake Volcano, northern California Cascades","interactions":[],"lastModifiedDate":"2016-05-24T08:43:11","indexId":"pp1822","displayToPublicDate":"2016-05-23T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1822","title":"Late Holocene volcanism at Medicine Lake Volcano, northern California Cascades","docAbstract":"Late Holocene volcanism at Medicine Lake volcano in the southern Cascades arc exhibited widespread and compositionally diverse magmatism ranging from basalt to rhyolite. Nine well-characterized eruptions have taken place at this very large rear-arc volcano since 5,200 years ago, an eruptive frequency greater than nearly all other Cascade volcanoes. The lavas are widely distributed, scattered over an area of ~300 km2 across the >2,000-km2 volcano. The eruptions are radiocarbon dated and the ages are also constrained by paleomagnetic data that provide strong evidence that the volcanic activity occurred in three distinct episodes at ~1 ka, ~3 ka, and ~5 ka. The ~1-ka final episode produced a variety of compositions including west- and north-flank mafic flows interspersed in time with fissure rhyolites erupted tangential to the volcano’s central caldera, including the youngest and most spectacular lava flow at the volcano, the ~950-yr-old compositionally zoned Glass Mountain flow. At ~3 ka, a north-flank basalt eruption was followed by an andesite eruption 27 km farther south that contains quenched basalt inclusions. The ~5-ka episode produced two caldera-focused dacitic eruptions. Quenched magmatic inclusions record evidence of intrusions that did not independently reach the surface. The inclusions are present in five andesitic, dacitic, and rhyolitic host lavas, and were erupted in each of the three episodes. Compositional and mineralogic evidence from mafic lavas and inclusions indicate that both tholeiitic (dry) and calcalkaline (wet) parental magmas were present. Petrologic evidence records the operation of complex, multi-stage processes including fractional crystallization, crustal assimilation, and magma mixing. Experimental evidence suggests that magmas were stored at 3 to 6 km depth prior to eruption, and that both wet and dry parental magmas were involved in generating the more silicic magmas. The broad distribution of eruptive events and the relative accessibility and good exposure of lavas, combined with physical and petrologic evidence for multiple and varied mafic inputs, has created an unusual opportunity to understand the workings of this large magmatic system. A combined total of more than 25 intrusive and extrusive events are indicated for late Holocene time. Plutonic inclusions, some with ages as young as Holocene, were also brought to the surface in five of the eruptions. All eruptions took place along northwest- to northeast-trending alignments of vents, reflecting the overall east-west extensional tectonic environment. The interaction of tectonism and volcanism is a dominant influence at this subduction-related volcano, located where the west edge of the extensional Basin and Range Province impinges on the Cascades arc. Ongoing subsidence focused at the central caldera has been documented along with geophysical evidence for a small magma body. This evidence, combined with the frequency of eruptive and intrusive activity in late Holocene time, an active geothermal system, and intermittent long-period seismic events indicate that the volcano is likely to erupt again.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1822","usgsCitation":"Donnelly-Nolan, J.M., Champion, D.E., and Grove, T.L., 2016, Late Holocene volcanism at Medicine Lake volcano, northern California Cascades: U.S. Geological Survey Professional Paper 1822, 59 p.,\nhttps://dx.doi.org/10.3133/pp1822.","productDescription":"Report: vi, 59 p.; Tables 1-3","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-055982","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":321256,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1822/coverthb.jpg"},{"id":321257,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1822/pp1822.pdf","text":"Report","size":"10.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP1822"},{"id":321258,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/pp/1822/pp1822_table1.xls","text":"Table 1","size":"411 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"PP1822 Table 1"},{"id":321259,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/pp/1822/pp1822_table2.xls","text":"Table 2","size":"63 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"PP1822 Table 2"},{"id":321260,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/pp/1822/pp1822_table3.pdf","text":"Table 3","size":"132 KB","linkFileType":{"id":1,"text":"pdf"},"description":"PP1822 Table 3"}],"country":"United States","state":"California","otherGeospatial":"Medicine Lake Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.74224853515625,\n 41.35413387210046\n ],\n [\n -121.74224853515625,\n 41.71700538790365\n ],\n [\n -121.3385009765625,\n 41.71700538790365\n ],\n [\n -121.3385009765625,\n 41.35413387210046\n ],\n [\n -121.74224853515625,\n 41.35413387210046\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information
Volcano Science Center - Menlo Park
U.S. Geological Survey
345 Middlefield Road, MS 910
Menlo Park, CA 94025
http://volcanoes.usgs.gov/
In this closing chapter, we summarize the advances in biocrust research made during the last 1.5 decades. In the first part of the chapter, we discuss how in some research fields, such as the microbial diversity of fungi, bacteria, and microfauna; the interaction between biocrusts and vascular plants; and in the rehabilitation of biocrusts; particularly large achievements have been made. In other fields, previously established knowledge of overall patterns has been corroborated and refined by additional studies, e.g., in the fields of soil stabilization and disturbance effects. In the second part of the chapter, we outline the research gaps and challenges foreseen by us. We identify multiple knowledge gaps, including many understudied geographic regions, the largely missing link between genetic and morphological species identification data, and the answers to some mechanistic questions, such as the overall role of biocrusts in hydrology and nutrient cycles. With some ideas on promising new research questions and approaches we close this chapter and the overall book.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Ecological studies","language":"English","publisher":"Springer International Publishing","doi":"10.1007/978-3-319-30214-0_25","usgsCitation":"Weber, B., Belnap, J., and Buedel, B., 2016, Synthesis on biological soil crust research, chap. of Ecological studies, p. 527-534, https://doi.org/10.1007/978-3-319-30214-0_25.","productDescription":"8 p.","startPage":"527","endPage":"534","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071476","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":328250,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-22","publicationStatus":"PW","scienceBaseUri":"57cfe8bee4b04836416a0e3a","contributors":{"authors":[{"text":"Weber, Bettina","contributorId":21447,"corporation":false,"usgs":true,"family":"Weber","given":"Bettina","affiliations":[],"preferred":false,"id":647899,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belnap, Jayne 0000-0001-7471-2279 jayne_belnap@usgs.gov","orcid":"https://orcid.org/0000-0001-7471-2279","contributorId":1332,"corporation":false,"usgs":true,"family":"Belnap","given":"Jayne","email":"jayne_belnap@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":647898,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buedel, Burkhard","contributorId":172210,"corporation":false,"usgs":false,"family":"Buedel","given":"Burkhard","email":"","affiliations":[{"id":27000,"text":"Department of Biology, University of Kaiserslautern, Kaiserlautern, Germany","active":true,"usgs":false}],"preferred":false,"id":647900,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70171126,"text":"ofr20161080 - 2016 - Spatially explicit modeling of annual and seasonal habitat for greater sage-grouse (Centrocercus urophasianus) in Nevada and Northeastern California—An updated decision-support tool for management","interactions":[],"lastModifiedDate":"2016-06-23T16:23:54","indexId":"ofr20161080","displayToPublicDate":"2016-05-20T17:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1080","title":"Spatially explicit modeling of annual and seasonal habitat for greater sage-grouse (Centrocercus urophasianus) in Nevada and Northeastern California—An updated decision-support tool for management","docAbstract":"Successful adaptive management hinges largely upon integrating new and improved sources of information as they become available. As a timely example of this tenet, we updated a management decision support tool that was previously developed for greater sage-grouse (Centrocercus urophasianus, hereinafter referred to as “sage-grouse”) populations in Nevada and California. Specifically, recently developed spatially explicit habitat maps derived from empirical data played a key role in the conservation of this species facing listing under the Endangered Species Act. This report provides an updated process for mapping relative habitat suitability and management categories for sage-grouse in Nevada and northeastern California (Coates and others, 2014, 2016). These updates include: (1) adding radio and GPS telemetry locations from sage-grouse monitored at multiple sites during 2014 to the original location dataset beginning in 1998; (2) integrating output from high resolution maps (1–2 m2) of sagebrush and pinyon-juniper cover as covariates in resource selection models; (3) modifying the spatial extent of the analyses to match newly available vegetation layers; (4) explicit modeling of relative habitat suitability during three seasons (spring, summer, winter) that corresponded to critical life history periods for sage-grouse (breeding, brood-rearing, over-wintering); (5) accounting for differences in habitat availability between more mesic sagebrush steppe communities in the northern part of the study area and drier Great Basin sagebrush in more southerly regions by categorizing continuous region-wide surfaces of habitat suitability index (HSI) with independent locations falling within two hydrological zones; (6) integrating the three seasonal maps into a composite map of annual relative habitat suitability; (7) deriving updated land management categories based on previously determined cut-points for intersections of habitat suitability and an updated index of sage-grouse abundance and space-use (AUI); and (8) masking urban footprints and major roadways out of the final map products.
Seasonal habitat maps were generated based on model-averaged resource selection functions (RSF) derived for 10 project areas (813 sage-grouse; 14,085 locations) during the spring season, 10 during the summer season (591 sage-grouse, 11,743 locations), and 7 during the winter season (288 sage-grouse, 4,862 locations). RSF surfaces were transformed to HSIs and averaged in a GIS framework for every pixel for each season. Validation analyses of categorized HSI surfaces using a suite of independent datasets resulted in an agreement of 93–97 percent for habitat versus non-habitat on an annual basis. Spring and summer maps validated similarly well at 94–97 percent, while winter maps validated slightly less accurately at 87–93 percent.
We then provide an updated example of how space use models can be integrated with habitat models to help inform conservation planning. We used updated lek count data to calculate a composite abundance and space use index (AUI) that comprised the combination of probabilistic breeding density with a non-linear probability of occurrence relative to distance to nearest lek. The AUI was then classified into two categories of use (high and low-to-no) and intersected with the HSI categories to create potential management prioritization scenarios based on information about sage-grouse occupancy coupled with habitat suitability. Compared to Coates and others (2014, 2016), the amount of area classified as habitat across the region increased by 6.5 percent (approximately 1,700,000 acres). For management categories, core increased by 7.2 percent (approximately 865,000 acres), priority increased by 9.6 percent (approximately 855,000 acres), and general increased by 9.2 percent (approximately 768,000 acres), while non-habitat decreased (that is, classified non-habitat occurring outside of areas of concentrated use) by 11.9 percent (approximately 2,500,000 acres). Importantly, seasonal and annual maps represent habitat for all age and sex classes of sage-grouse (that is, sample sizes of marked grouse were insufficient to only construct models for reproductive females). This revised sage-grouse habitat mapping product helps improve adaptive application of conservation planning tools based on intersections of spatially explicit habitat suitability, abundance, and space use indices.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161080","collaboration":"Prepared in cooperation with the State of Nevada Sagebrush Ecosystem Program, Bureau of Land Management, Nevada Department of Wildlife, California Department of Fish and Wildlife, and Idaho State University","usgsCitation":"Coates, P.S., Casazza, M.L., Brussee B.E., Ricca, M.A., Gustafson, K.B., Sanchez-Chopitea, E., Mauch, K., Niell, L., Gardner, S., Espinosa, S., and Delehanty, D.J., 2016, Spatially explicit modeling of annual and seasonal habitat for greater sage-grouse (Centrocercus urophasianus) in Nevada and Northeastern California—An updated decision-support tool for management: U.S. Geological Survey Open-File Report 2016-1080, 160 p., https://dx.doi.org/10.3133/ofr20161080.","productDescription":"Report: viii, 160 p.; Dataset","numberOfPages":"172","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-072897","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":322138,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://dx.doi.org/10.5066/F7CC0XRV","text":"USGS data release - Spatially Explicit Modeling of Annual and Seasonal Habitat for Greater Sage-Grouse (Centrocercus urophasianus) in Nevada and Northeastern California - an Updated Decision-Support Tool for Management"},{"id":321471,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1080/coverthb.jpg"},{"id":321472,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1080/ofr20161080.pdf","text":"Report","size":"20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1080 Report PDF"}],"country":"United States","state":"California, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.87158203125,\n 37.50972584293751\n ],\n [\n -120.87158203125,\n 41.96765920367816\n ],\n [\n -114.06005859375,\n 41.96765920367816\n ],\n [\n -114.06005859375,\n 37.50972584293751\n ],\n [\n -120.87158203125,\n 37.50972584293751\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
http://werc.usgs.gov/
Graptemys ernsti, the Escambia Map Turtle, inhabits the Escambia/Conecuh River, the adjacent Yellow River, and the Pea River further to the east, all of which have been distinct drainage systems since the Pleistocene. We used continuous and meristic morphological and genetic data to compare populations of G. ernsti and found evidence of differences among the three drainages. Frequency of occurrence of a nasal trident differed among the three drainages. Yellow River specimens possessed unique mitochondrial haplotypes while the Conecuh and the Pea shared haplotypes. Five microsatellite loci identified the drainages as being distinct, with the strongest differentiation between the Yellow River and the other two drainages. While these differences do not appear great enough to warrant taxonomic recognition, they do suggest that each population has a distinct evolutionary and demographic history and that they should therefore be managed separately.
","language":"English","publisher":"Herpetological Conservation and Biology","usgsCitation":"Ennen, J.R., Godwin, J., Lovich, J.E., Kreiser, B.R., Folt, B., and Hazard, S., 2016, Interdrainage morphological and genetic differentiation in the Escambia Map Turtle, Graptemys ernsti: Herpetological Conservation and Biology, v. 11, no. 1, p. 122-131.","productDescription":"10 p.","startPage":"122","endPage":"131","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063620","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":321443,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":321419,"type":{"id":15,"text":"Index Page"},"url":"https://www.herpconbio.org/contents_vol11_issue1.html"}],"volume":"11","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5740271be4b07e28b65dcfdc","contributors":{"authors":[{"text":"Ennen, Joshua R.","contributorId":83858,"corporation":false,"usgs":true,"family":"Ennen","given":"Joshua","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":629864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Godwin, James","contributorId":81015,"corporation":false,"usgs":true,"family":"Godwin","given":"James","affiliations":[],"preferred":false,"id":629865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":629863,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kreiser, Brian R.","contributorId":47691,"corporation":false,"usgs":true,"family":"Kreiser","given":"Brian","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":629866,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Folt, Brian","contributorId":127807,"corporation":false,"usgs":false,"family":"Folt","given":"Brian","affiliations":[{"id":7160,"text":"Department of Biological Sciences and Auburn University Museum of Natural History, 331 Funchess Hall, Auburn University, Auburn, Alabama 36849; E-mail: brian.folt@gmail.com.","active":true,"usgs":false}],"preferred":false,"id":629867,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hazard, Sarah","contributorId":169519,"corporation":false,"usgs":false,"family":"Hazard","given":"Sarah","email":"","affiliations":[{"id":25549,"text":"Tennessee Aquarium Conservation Institute, 201 Chestnut Street, Chattanooga, TN 37402","active":true,"usgs":false}],"preferred":false,"id":629868,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70171105,"text":"70171105 - 2016 - A possible transoceanic tsunami directed toward the U.S. west coast from the Semidi segment, Alaska convergent margin","interactions":[],"lastModifiedDate":"2018-01-08T12:47:45","indexId":"70171105","displayToPublicDate":"2016-05-20T10:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"A possible transoceanic tsunami directed toward the U.S. west coast from the Semidi segment, Alaska convergent margin","docAbstract":"The Semidi segment of the Alaska convergent margin appears capable of generating a giant tsunami like the one produced along the nearby Unimak segment in 1946. Reprocessed legacy seismic reflection data and a compilation of multibeam bathymetric surveys reveal structures that could generate such a tsunami. A 200 km long ridge or escarpment with crests >1 km high is the surface expression of an active out-of-sequence fault zone, recently referred to as a splay fault. Such faults are potentially tsunamigenic. This type of fault zone separates the relatively rigid rock of the margin framework from the anelastic accreted sediment prism. Seafloor relief of the ridge exceeds that of similar age accretionary prism ridges indicating preferential slip along the splay fault zone. The greater slip may derive from Quaternary subduction of the Patton Murray hot spot ridge that extends 200 km toward the east across the north Pacific. Estimates of tsunami repeat times from paleotsunami studies indicate that the Semidi segment could be near the end of its current inter-seismic cycle. GPS records from Chirikof Island at the shelf edge indicate 90% locking of plate interface faults. An earthquake in the shallow Semidi subduction zone could generate a tsunami that will inundate the US west coast more than the 1946 and 1964 earthquakes because the Semidi continental slope azimuth directs a tsunami southeastward.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2015GC006147","usgsCitation":"von Huene, R.E., Miller, J.J., and Dartnell, P., 2016, A possible transoceanic tsunami directed toward the U.S. west coast from the Semidi segment, Alaska convergent margin: Geochemistry, Geophysics, Geosystems, v. 17, no. 3, p. 645-659, https://doi.org/10.1002/2015GC006147.","productDescription":"15 p.","startPage":"645","endPage":"659","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066716","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470974,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015gc006147","text":"Publisher Index Page"},{"id":321440,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-04","publicationStatus":"PW","scienceBaseUri":"5740271ae4b07e28b65dcfcc","contributors":{"authors":[{"text":"von Huene, Roland E. 0000-0003-1301-3866 rvonhuene@usgs.gov","orcid":"https://orcid.org/0000-0003-1301-3866","contributorId":191070,"corporation":false,"usgs":true,"family":"von Huene","given":"Roland","email":"rvonhuene@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":7065,"text":"USGS emeritus","active":true,"usgs":false}],"preferred":false,"id":629884,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, John J. 0000-0002-9098-0967 jmiller@usgs.gov","orcid":"https://orcid.org/0000-0002-9098-0967","contributorId":3785,"corporation":false,"usgs":true,"family":"Miller","given":"John","email":"jmiller@usgs.gov","middleInitial":"J.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":629885,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dartnell, Peter 0000-0002-9554-729X pdartnell@usgs.gov","orcid":"https://orcid.org/0000-0002-9554-729X","contributorId":2688,"corporation":false,"usgs":true,"family":"Dartnell","given":"Peter","email":"pdartnell@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":629883,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70170967,"text":"ofr20161076 - 2016 - Development of a CE-QUAL-W2 temperature model for Crystal Springs Lake, Portland, Oregon","interactions":[],"lastModifiedDate":"2016-05-19T15:58:47","indexId":"ofr20161076","displayToPublicDate":"2016-05-19T12:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1076","title":"Development of a CE-QUAL-W2 temperature model for Crystal Springs Lake, Portland, Oregon","docAbstract":"During summer 2014, lake level, streamflow, and water temperature in and around Crystal Springs Lake in Portland, Oregon, were measured by the U.S. Geological Survey and the City of Portland Bureau of Environmental Services to better understand the effect of the lake on Crystal Springs Creek and Johnson Creek downstream. Johnson Creek is listed as an impaired water body for temperature by the Oregon Department of Environmental Quality (ODEQ), as required by section 303(d) of the Clean Water Act. A temperature total maximum daily load applies to all streams in the Johnson Creek watershed, including Crystal Springs Creek. Summer water temperatures downstream of Crystal Springs Lake and the Golf Pond regularly exceed the ODEQ numeric criterion of 64.4 °F (18.0 °C) for salmonid rearing and migration. To better understand temperature contributions of this system, the U.S. Geological Survey developed two-dimensional hydrodynamic water temperature models of Crystal Springs Lake and the Golf Pond. Model grids were developed to closely resemble the bathymetry of the lake and pond using data from a 2014 survey. The calibrated models simulated surface water elevations to within 0.06 foot (0.02 meter) and outflow water temperature to within 1.08 °F (0.60 °C). Streamflow, water temperature, and lake elevation data collected during summer 2014 supplied the boundary and reference conditions for the model. Measured discrepancies between outflow and inflow from the lake, assumed to be mostly from unknown and diffuse springs under the lake, accounted for about 46 percent of the total inflow to the lake.
\nModel simulations (scenarios) were run with lower water surface elevations in Crystal Springs Lake and increased shading to the lake to assess the relative effect the lake and pond characteristics have on water temperature. The Golf Pond was unaltered in all scenarios. The models estimated that lower lake elevations would result in cooler water downstream of the Golf Pond and shorter residence times in the lake. Increased shading to the lake would also provide substantial cooling. Most management scenarios resulted in a decrease in 7-day average of daily maximum values by about 2.0– 4.7 °F (1.1 –2.6 °C) for outflow from Crystal Springs Lake during the period of interest. Outflows from the Golf Pond showed a net temperature reduction of 0.5–2.7 °F (0.3–1.5 °C) compared to measured values in 2014 because of solar heating and downstream warming in the Golf Pond resulting from mixing with inflow from Reed Lake.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161076","collaboration":"Prepared in cooperation with City of Portland Bureau of Environmental Services","usgsCitation":"Buccola, N.L., and Stonewall, A.J., 2016, Development of a CE-QUAL-W2 temperature model for Crystal Springs Lake, Portland, Oregon: U.S. Geological Survey Open-File Report 2016‒1076, 26 p.,\nhttps://dx.doi.org/10.3133/ofr20161076.","productDescription":"Report: vi, 26 p.; Tables 1-9","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-060388","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":321392,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2016/1076/ofr20161076_tables1-9.xlsx","text":"Tables 1-9","size":"63 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1076 Tables 1-9"},{"id":321390,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1076/coverthb.jpg"},{"id":321391,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1076/ofr20161076.pdf","text":"Report","size":"1.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1076"}],"country":"United States","state":"Oregon","city":"Portland","otherGeospatial":"Crystal Springs Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.63982295989989,\n 45.47522429601816\n ],\n [\n -122.63982295989989,\n 45.48085140521857\n ],\n [\n -122.63482332229613,\n 45.48085140521857\n ],\n [\n -122.63482332229613,\n 45.47522429601816\n ],\n [\n -122.63982295989989,\n 45.47522429601816\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
http://or.water.usgs.gov
As global temperatures rise, variation in annual climate is also changing, with unknown consequences for forest biomes. Growing forests have the ability to capture atmospheric CO2and thereby slow rising CO2 concentrations. Forests’ ongoing ability to sequester C depends on how tree communities respond to changes in climate variation. Much of what we know about tree and forest response to climate variation comes from tree-ring records. Yet typical tree-ring datasets and models do not capture the diversity of climate responses that exist within and among trees and species. We address this issue using a model that estimates individual tree response to climate variables while accounting for variation in individuals’ size, age, competitive status, and spatially structured latent covariates. Our model allows for inference about variance within and among species. We quantify how variables influence aboveground biomass growth of individual trees from a representative sample of 15 northern or southern tree species growing in a transition zone between boreal and temperate biomes. Individual trees varied in their growth response to fluctuating mean annual temperature and summer moisture stress. The variation among individuals within a species was wider than mean differences among species. The effects of mean temperature and summer moisture stress interacted, such that warm years produced positive responses to summer moisture availability and cool years produced negative responses. As climate models project significant increases in annual temperatures, growth of species likeAcer saccharum, Quercus rubra, and Picea glauca will vary more in response to summer moisture stress than in the past. The magnitude of biomass growth variation in response to annual climate was 92–95% smaller than responses to tree size and age. This means that measuring or predicting the physical structure of current and future forests could tell us more about future C dynamics than growth responses related to climate change alone.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13208","usgsCitation":"Foster, J.R., Finley, A.O., D’Amato, A.W., Bradford, J.B., and Banerjee, S., 2016, Predicting tree biomass growth in the temperate-boreal ecotone: is tree size, age, competition or climate response most important?: Global Change Biology, v. 22, no. 6, p. 2138-2151, https://doi.org/10.1111/gcb.13208.","productDescription":"14 p.","startPage":"2138","endPage":"2151","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069743","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":321401,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Superior National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.48291015625,\n 47.76517619125415\n ],\n [\n -92.48291015625,\n 48.38361810886624\n ],\n [\n -90.02471923828125,\n 48.38361810886624\n ],\n [\n -90.02471923828125,\n 47.76517619125415\n ],\n [\n -92.48291015625,\n 47.76517619125415\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"22","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-03","publicationStatus":"PW","scienceBaseUri":"573ed59ce4b04a3a6a2462e0","chorus":{"doi":"10.1111/gcb.13208","url":"http://dx.doi.org/10.1111/gcb.13208","publisher":"Wiley-Blackwell","authors":"Foster Jane R., Finley Andrew O., D'Amato Anthony W., Bradford John B., Banerjee Sudipto","journalName":"Global Change Biology","publicationDate":"3/3/2016","auditedOn":"6/21/2016"},"contributors":{"authors":[{"text":"Foster, Jane R.","contributorId":27792,"corporation":false,"usgs":true,"family":"Foster","given":"Jane","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":629806,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Finley, Andrew O.","contributorId":39310,"corporation":false,"usgs":true,"family":"Finley","given":"Andrew","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":629807,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"D’Amato, Anthony W.","contributorId":28140,"corporation":false,"usgs":false,"family":"D’Amato","given":"Anthony","email":"","middleInitial":"W.","affiliations":[{"id":6735,"text":"University of Vermont, Rubenstein School of Environment and Natural Resources","active":true,"usgs":false},{"id":13478,"text":"Department of Forest Resources, University of Minnesota, St. Paul, Minnesota (Correspondence to: russellm@umn.edu)","active":true,"usgs":false}],"preferred":false,"id":629808,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradford, John B. 0000-0001-9257-6303 jbradford@usgs.gov","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":611,"corporation":false,"usgs":true,"family":"Bradford","given":"John","email":"jbradford@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":629805,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Banerjee, Sudipto","contributorId":73894,"corporation":false,"usgs":true,"family":"Banerjee","given":"Sudipto","email":"","affiliations":[],"preferred":false,"id":629809,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70174950,"text":"70174950 - 2016 - A partial exponential lumped parameter model to evaluate groundwater age distributions and nitrate trends in long-screened wells","interactions":[],"lastModifiedDate":"2018-08-07T11:51:36","indexId":"70174950","displayToPublicDate":"2016-05-19T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"A partial exponential lumped parameter model to evaluate groundwater age distributions and nitrate trends in long-screened wells","docAbstract":"A partial exponential lumped parameter model (PEM) was derived to determine age distributions and nitrate trends in long-screened production wells. The PEM can simulate age distributions for wells screened over any finite interval of an aquifer that has an exponential distribution of age with depth. The PEM has 3 parameters – the ratio of saturated thickness to the top and bottom of the screen and mean age, but these can be reduced to 1 parameter (mean age) by using well construction information and estimates of the saturated thickness. The PEM was tested with data from 30 production wells in a heterogeneous alluvial fan aquifer in California, USA. Well construction data were used to guide parameterization of a PEM for each well and mean age was calibrated to measured environmental tracer data (3H, 3He, CFC-113, and 14C). Results were compared to age distributions generated for individual wells using advective particle tracking models (PTMs). Age distributions from PTMs were more complex than PEM distributions, but PEMs provided better fits to tracer data, partly because the PTMs did not simulate 14C accurately in wells that captured varying amounts of old groundwater recharged at lower rates prior to groundwater development and irrigation. Nitrate trends were simulated independently of the calibration process and the PEM provided good fits for at least 11 of 24 wells. This work shows that the PEM, and lumped parameter models (LPMs) in general, can often identify critical features of the age distributions in wells that are needed to explain observed tracer data and nonpoint source contaminant trends, even in systems where aquifer heterogeneity and water-use complicate distributions of age. While accurate PTMs are preferable for understanding and predicting aquifer-scale responses to water use and contaminant transport, LPMs can be sensitive to local conditions near individual wells that may be inaccurately represented or missing in an aquifer-scale flow model.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2016.05.011","usgsCitation":"Jurgens, B.C., Bohlke, J.K., Kauffman, L.J., Belitz, K., and Esser, B.K., 2016, A partial exponential lumped parameter model to evaluate groundwater age distributions and nitrate trends in long-screened wells: Journal of Hydrology, v. 543, no. A, p. 109-126, https://doi.org/10.1016/j.jhydrol.2016.05.011.","productDescription":"18 p.","startPage":"109","endPage":"126","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069107","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":325571,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122,\n 37.9\n ],\n [\n -122,\n 37.2\n ],\n [\n -120.2,\n 37.2\n ],\n [\n -120.2,\n 37.9\n ],\n [\n -122,\n 37.9\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"543","issue":"A","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57934440e4b0eb1ce79e8bd2","contributors":{"authors":[{"text":"Jurgens, Bryant C. 0000-0002-1572-113X bjurgens@usgs.gov","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":127842,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","email":"bjurgens@usgs.gov","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":643297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bohlke, John Karl 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":127841,"corporation":false,"usgs":true,"family":"Bohlke","given":"John","email":"jkbohlke@usgs.gov","middleInitial":"Karl","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":false,"id":643298,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kauffman, Leon J. 0000-0003-4564-0362 lkauff@usgs.gov","orcid":"https://orcid.org/0000-0003-4564-0362","contributorId":1094,"corporation":false,"usgs":true,"family":"Kauffman","given":"Leon","email":"lkauff@usgs.gov","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":643299,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Belitz, Kenneth 0000-0003-4481-2345 kbelitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":442,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","email":"kbelitz@usgs.gov","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":643300,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Esser, Bradley K.","contributorId":33161,"corporation":false,"usgs":true,"family":"Esser","given":"Bradley","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":643301,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70171437,"text":"70171437 - 2016 - Bayesian estimation of magma supply, storage, and eruption rates using a multiphysical volcano model: Kīlauea Volcano, 2000–2012","interactions":[],"lastModifiedDate":"2016-06-01T16:09:56","indexId":"70171437","displayToPublicDate":"2016-05-19T02:30:00","publicationYear":"2016","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":"Bayesian estimation of magma supply, storage, and eruption rates using a multiphysical volcano model: Kīlauea Volcano, 2000–2012","docAbstract":"Estimating rates of magma supply to the world's volcanoes remains one of the most fundamental aims of volcanology. Yet, supply rates can be difficult to estimate even at well-monitored volcanoes, in part because observations are noisy and are usually considered independently rather than as part of a holistic system. In this work we demonstrate a technique for probabilistically estimating time-variable rates of magma supply to a volcano through probabilistic constraint on storage and eruption rates. This approach utilizes Bayesian joint inversion of diverse datasets using predictions from a multiphysical volcano model, and independent prior information derived from previous geophysical, geochemical, and geological studies. The solution to the inverse problem takes the form of a probability density function which takes into account uncertainties in observations and prior information, and which we sample using a Markov chain Monte Carlo algorithm. Applying the technique to Kīlauea Volcano, we develop a model which relates magma flow rates with deformation of the volcano's surface, sulfur dioxide emission rates, lava flow field volumes, and composition of the volcano's basaltic magma. This model accounts for effects and processes mostly neglected in previous supply rate estimates at Kīlauea, including magma compressibility, loss of sulfur to the hydrothermal system, and potential magma storage in the volcano's deep rift zones. We jointly invert data and prior information to estimate rates of supply, storage, and eruption during three recent quasi-steady-state periods at the volcano. Results shed new light on the time-variability of magma supply to Kīlauea, which we find to have increased by 35–100% between 2001 and 2006 (from 0.11–0.17 to 0.18–0.28 km3/yr), before subsequently decreasing to 0.08–0.12 km3/yr by 2012. Changes in supply rate directly impact hazard at the volcano, and were largely responsible for an increase in eruption rate of 60–150% between 2001 and 2006, and subsequent decline by as much as 60% by 2012. We also demonstrate the occurrence of temporal changes in the proportion of Kīlauea's magma supply that is stored versus erupted, with the supply “surge” in 2006 associated with increased accumulation of magma at the summit. Finally, we are able to place some constraints on sulfur concentrations in Kīlauea magma and the scrubbing of sulfur by the volcano's hydrothermal system. Multiphysical, Bayesian constraint on magma flow rates may be used to monitor evolving volcanic hazard not just at Kīlauea but at other volcanoes around the world.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2016.04.029","usgsCitation":"Anderson, K.R., and Poland, M.P., 2016, Bayesian estimation of magma supply, storage, and eruption rates using a multiphysical volcano model: Kīlauea Volcano, 2000–2012: Earth and Planetary Science Letters, v. 447, p. 161-171, https://doi.org/10.1016/j.epsl.2016.04.029.","productDescription":"11 p.","startPage":"161","endPage":"171","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071533","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":470983,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2016.04.029","text":"Publisher Index Page"},{"id":322056,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.31784057617188,\n 19.374636239520235\n ],\n [\n -155.31784057617188,\n 19.44652177370614\n ],\n [\n -155.21896362304688,\n 19.44652177370614\n ],\n [\n -155.21896362304688,\n 19.374636239520235\n ],\n [\n -155.31784057617188,\n 19.374636239520235\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"447","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57500734e4b0ee97d51bb3c8","chorus":{"doi":"10.1016/j.epsl.2016.04.029","url":"http://dx.doi.org/10.1016/j.epsl.2016.04.029","publisher":"Elsevier BV","authors":"Anderson Kyle R., Poland Michael P.","journalName":"Earth and Planetary Science Letters","publicationDate":"8/2016"},"contributors":{"authors":[{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":630979,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Poland, Michael P. 0000-0001-5240-6123 mpoland@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":146118,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","email":"mpoland@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":630980,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70174955,"text":"70174955 - 2016 - Sensitivity of Pliocene Arctic climate to orbital forcing, atmospheric CO2 and sea ice albedo parameterisation","interactions":[],"lastModifiedDate":"2016-07-22T16:11:29","indexId":"70174955","displayToPublicDate":"2016-05-19T02:30:00","publicationYear":"2016","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":"Sensitivity of Pliocene Arctic climate to orbital forcing, atmospheric CO2 and sea ice albedo parameterisation","docAbstract":"General circulation model (GCM) simulations of the mid-Pliocene Warm Period (mPWP, 3.264 to 3.025 Myr ago) do not reproduce the magnitude of Northern Hemisphere high latitude surface air and sea surface temperature (SAT and SST) warming that proxy data indicate. There is also large uncertainty regarding the state of sea ice cover in the mPWP. Evidence for both perennial and seasonal mPWP Arctic sea ice is found through analyses of marine sediments, whilst in a multi-model ensemble of mPWP climate simulations, half of the ensemble simulated ice-free summer Arctic conditions. Given the strong influence that sea ice exerts on high latitude temperatures, an understanding of the nature of mPWP Arctic sea ice would be highly beneficial.
\nUsing the HadCM3 GCM, this paper explores the impact of various combinations of potential mPWP orbital forcing, atmospheric CO2 concentrations and minimum sea ice albedo on sea ice extent and high latitude warming. The focus is on the Northern Hemisphere, due to availability of proxy data, and the large data–model discrepancies in this region. Changes in orbital forcings are demonstrated to be sufficient to alter the Arctic sea ice simulated by HadCM3 from perennial to seasonal. However, this occurs only when atmospheric CO2 concentrations exceed 300 ppm. Reduction of the minimum sea ice albedo from 0.5 to 0.2 is also sufficient to simulate seasonal sea ice, with any of the combinations of atmospheric CO2 and orbital forcing. Compared to a mPWP control simulation, monthly mean increases north of 60°N of up to 4.2 °C (SST) and 9.8 °C (SAT) are simulated.
\nWith varying CO2, orbit and sea ice albedo values we are able to reproduce proxy temperature records that lean towards modest levels of high latitude warming, but other proxy data showing greater warming remain beyond the reach of our model. This highlights the importance of additional proxy records at high latitudes and ongoing efforts to compare proxy signals between sites.
","language":"English","publisher":"North-Holland Pub. Co.","doi":"10.1016/j.epsl.2016.02.036","usgsCitation":"Howell, F.W., Haywood, A.M., Dowsett, H.J., and Pickering, S.J., 2016, Sensitivity of Pliocene Arctic climate to orbital forcing, atmospheric CO2 and sea ice albedo parameterisation: Earth and Planetary Science Letters, v. 441, p. 133-142, https://doi.org/10.1016/j.epsl.2016.02.036.","productDescription":"10 p.","startPage":"133","endPage":"142","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073169","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":470984,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2016.02.036","text":"Publisher Index Page"},{"id":325567,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"441","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5793444ae4b0eb1ce79e8c10","chorus":{"doi":"10.1016/j.epsl.2016.02.036","url":"http://dx.doi.org/10.1016/j.epsl.2016.02.036","publisher":"Elsevier BV","authors":"Howell Fergus W., Haywood Alan M., Dowsett Harry J., Pickering Steven J.","journalName":"Earth and Planetary Science Letters","publicationDate":"5/2016"},"contributors":{"authors":[{"text":"Howell, Fergus W.","contributorId":173110,"corporation":false,"usgs":false,"family":"Howell","given":"Fergus","email":"","middleInitial":"W.","affiliations":[{"id":13344,"text":"University of Leeds","active":true,"usgs":false}],"preferred":false,"id":643333,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haywood, Alan M.","contributorId":86663,"corporation":false,"usgs":true,"family":"Haywood","given":"Alan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":643334,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dowsett, Harry J. 0000-0003-1983-7524 hdowsett@usgs.gov","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":949,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry","email":"hdowsett@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":643332,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pickering, Steven J.","contributorId":147378,"corporation":false,"usgs":false,"family":"Pickering","given":"Steven","email":"","middleInitial":"J.","affiliations":[{"id":13344,"text":"University of Leeds","active":true,"usgs":false}],"preferred":false,"id":643335,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170901,"text":"ds998 - 2016 - Groundwater geochemical and selected volatile organic compound data, Operable Unit 1, Naval Undersea Warfare Center, Division Keyport, Washington, July 2015","interactions":[],"lastModifiedDate":"2016-05-19T09:14:07","indexId":"ds998","displayToPublicDate":"2016-05-18T18:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"998","title":"Groundwater geochemical and selected volatile organic compound data, Operable Unit 1, Naval Undersea Warfare Center, Division Keyport, Washington, July 2015","docAbstract":"Previous investigations indicate that concentrations of chlorinated volatile organic compounds (CVOCs) are substantial in groundwater beneath the 9-acre former landfill at Operable Unit 1, Naval Undersea Warfare Center, Division Keyport, Washington. The U.S. Geological Survey has continued to monitor groundwater geochemistry to ensure that conditions remain favorable for contaminant biodegradation as specified in the Record of Decision for the site.
\nThis report presents groundwater geochemical and selected CVOC data collected at Operable Unit 1 by the U.S. Geological Survey during July 6–8 and July 31, 2015 in support of long-term monitoring for natural attenuation. Water samples were collected from 13 wells, 9 piezometers, and 13 shallow groundwater passive-diffusion sampling sites in the nearby marsh. Samples from all wells and piezometers were analyzed for oxidation-reduction (redox) sensitive constituents. Samples from all piezometers and four wells also were analyzed for CVOCs and dissolved gases, as were all samples from the passive-diffusion sampling sites.
\nIn 2015, concentrations of redox-sensitive constituents measured at all wells and piezometers were consistent with those measured in previous years, with dissolved oxygen concentrations all less than 1 milligram per liter; little to no detectable nitrate; abundant dissolved manganese, iron, and methane; and commonly detected sulfide. In the upper aquifer of the northern plantation in 2015, CVOC concentrations at all piezometers were similar to those measured in previous years, and concentrations of the reductive dechlorination byproducts ethane and ethene were equivalent to the concentrations measured in 2014. In the upper aquifer of the southern plantation, CVOC concentrations measured in piezometers during 2015 continued to be variable as in previous years, and often very high, and reductive dechlorination byproducts were detected in one of the three wells and in piezometers. Beneath the marsh adjacent to the southern plantation, CVOC concentrations measured in 2015 continued to vary spatially and temporally, and were high. The total CVOC concentration, at what have been historically the most contaminated passive-diffusion sampler sites (S-4 T, S-4B T, and S-5 T), continued elevated trends, as did one of the new sampler sites (S-9 T) installed in 2015. For the intermediate aquifer in 2015, concentrations of reductive dechlorination byproducts ethane and ethene and CVOCs were consistent with those measured in previous years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds998","collaboration":"Prepared in cooperation with Department of the Navy, Naval Facilities Engineering Command, Northwest","usgsCitation":"Huffman, R.L., 2016, Groundwater geochemical and selected volatile organic compound data, Operable Unit 1, Naval Undersea Warfare Center, Division Keyport, Washington, July 2015: U.S. Geological Survey Data Series 998, 55 p., https://dx.doi.org/10.3133/ds998.","productDescription":"iv, 55 p.","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-074626","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":321393,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0998/coverthb.jpg"},{"id":321394,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0998/ds998.pdf","text":"Report","size":"1.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 998"}],"country":"United States","state":"Washington","otherGeospatial":"Division Keyport","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.88070678710938,\n 47.60986653003798\n ],\n [\n -122.88070678710938,\n 47.803008949806895\n ],\n [\n -122.58682250976562,\n 47.803008949806895\n ],\n [\n -122.58682250976562,\n 47.60986653003798\n ],\n [\n -122.88070678710938,\n 47.60986653003798\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
http://wa.water.usgs.gov
The U.S. Geological Survey’s (USGS) Water Availability and Use Science Program (WAUSP) goals are to provide a more accurate assessment of the status of the water resources of the United States and assist in the determination of the quantity and quality of water that is available for beneficial uses. These assessments would identify long-term trends or changes in water availability since the 1950s in the United States and help to develop the basis for an improved ability to forecast water avail- ability for future economic, energy-production, and environmental uses. The National Water Census (http://water.usgs.gov/watercensus/), a research program of the WAUSP, supports studies to develop new water accounting tools and assess water availability at the regional and national scales. Studies supported by this program target focus areas with identified water availability concerns and topical science themes related to the use of water within a specific type of environmental setting. The topical study described in this fact sheet will focus on understanding the relation between production of unconventional oil and gas (UOG) for energy and the water needed to produce and sustain this type of energy development. This relation applies to the life-cycle of renewable and nonrenewable forms of UOG energy and includes extraction, production, refinement, delivery, and disposal of waste byproducts. Water-use data and models derived from this topical study will be applied to other similar oil and gas plays within the United States to help resource managers assess and account for water used or needed in these areas. Additionally, the results from this topical study will be used to further refine the methods used in compiling water-use data for selected categories (for example, mining, domestic self-supplied, public supply, and wastewater) in the USGS’s 5-year national water-use estimates reports (http://water.usgs.gov/watuse/).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163032","usgsCitation":"Carter, J.M., Macek-Rowland, K.M., Thamke, J.N., Delzer, G.C., 2016, Estimating national water use associated with unconventional oil and gas development: U.S. Geological Survey Fact Sheet 2016–3032, 6 p., https://dx.doi.org/10.3133/fs20163032.","productDescription":"6 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-074182","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":321346,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3032/fs20163032.pdf","text":"Fact Sheet","size":"18.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 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Reston, Virginia 20192
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The Landsat 8 mission provides new opportunities for quantifying the distribution of above-ground carbon at moderate spatial resolution across the globe, and in particular drylands. Furthermore, coupled with structural information from space-based and airborne laser altimetry, Landsat 8 provides powerful capabilities for large-area, long-term studies that quantify temporal and spatial changes in above-ground biomass and cover. With the planned launch of ICESat-2 in 2017 and thus the potential to couple Landsat 8 and ICESat-2 data, we have unprecedented opportunities to address key challenges in drylands, including quantifying fuel loads, habitat quality, biodiversity, carbon cycling, and desertification.
\nIn this study, we explore the strengths of Landsat 8's Operational Land Imager (OLI) in estimating vegetation structure in a dryland ecosystem, and compare these results to Landsat 5's Thematic Mapper (TM). We also demonstrate the potential of OLI when coupled with light detection and ranging (lidar) in estimating vegetation cover and biomass in a dryland ecosystem. The OLI and TM predictions were similarly positive, indicating data from these sensors may be used in tandem for long-term time-series analysis. Results indicate shrub and herbaceous cover are well predicted with multi-temporal OLI data, and a combination of OLI and lidar derivatives improves most of these estimates and reduces uncertainty. For example, significant improvements were made for shrub cover (R2 = 0.64 and 0.78 using OLI only and both OLI and lidar data, respectively). Importantly, a time series of OLI, with some improvement from lidar, provides strong estimates of herbaceous cover (68% of the variance is explained with OLI alone). In contrast, OLI data explain roughly 59% of the variance in total shrub biomass, however approximately 71% of the variance is explained when combined with lidar derivatives.
\nTo estimate the potential synergies of OLI and ICESat-2 we used simulated ICESat-2 photon data to predict vegetation structure. In a shrubland environment with a vegetation mean height of 1 m and mean vegetation cover of 33%, vegetation photons are able to explain nearly 50% of the variance in vegetation height. These results, and those from a comparison site, suggest that a lower detection threshold of ICESat-2 may be in the range of 30% canopy cover and roughly 1 m height in comparable dryland environments and these detection thresholds could be used to combine future ICESat-2 photon data with OLI spectral data for improved vegetation structure. Overall, the synergistic use of Landsat 8 and ICESat-2 may improve estimates of above-ground biomass and carbon storage in drylands that meet these minimum thresholds, increasing our ability to monitor drylands for fuel loading and the potential to sequester carbon.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2016.02.039","usgsCitation":"Glenn, N.F., Neuenschwander, A., Vierling, L.A., Spaete, L., Li, A., Shinneman, D.J., Pilliod, D.S., Arkle, R., and McIlroy, S., 2016, Landsat 8 and ICESat-2: Performance and potential synergies for quantifying dryland ecosystem vegetation cover and biomass: Remote Sensing of Environment, v. 185, p. 233-242, https://doi.org/10.1016/j.rse.2016.02.039.","productDescription":"10 p.","startPage":"233","endPage":"242","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065442","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":321374,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"185","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"573d841ce4b0dae0d5e4c05b","contributors":{"authors":[{"text":"Glenn, Nancy F.","contributorId":95321,"corporation":false,"usgs":true,"family":"Glenn","given":"Nancy","email":"","middleInitial":"F.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":629668,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Neuenschwander, Amy","contributorId":169442,"corporation":false,"usgs":false,"family":"Neuenschwander","given":"Amy","email":"","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":629669,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vierling, Lee A.","contributorId":169443,"corporation":false,"usgs":false,"family":"Vierling","given":"Lee","email":"","middleInitial":"A.","affiliations":[{"id":6711,"text":"University of Idaho, Moscow ID","active":true,"usgs":false}],"preferred":false,"id":629670,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spaete, Lucas","contributorId":169444,"corporation":false,"usgs":false,"family":"Spaete","given":"Lucas","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":629671,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Li, Aihua","contributorId":169445,"corporation":false,"usgs":false,"family":"Li","given":"Aihua","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":629672,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shinneman, Douglas J. 0000-0002-4909-5181 dshinneman@usgs.gov","orcid":"https://orcid.org/0000-0002-4909-5181","contributorId":147745,"corporation":false,"usgs":true,"family":"Shinneman","given":"Douglas","email":"dshinneman@usgs.gov","middleInitial":"J.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":629673,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pilliod, David S. 0000-0003-4207-3518 dpilliod@usgs.gov","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":149254,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","email":"dpilliod@usgs.gov","middleInitial":"S.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":629667,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Arkle, Robert 0000-0003-3021-1389 rarkle@usgs.gov","orcid":"https://orcid.org/0000-0003-3021-1389","contributorId":149893,"corporation":false,"usgs":true,"family":"Arkle","given":"Robert","email":"rarkle@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":629674,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McIlroy, Susan K. 0000-0001-5088-3700 smcilroy@usgs.gov","orcid":"https://orcid.org/0000-0001-5088-3700","contributorId":169446,"corporation":false,"usgs":true,"family":"McIlroy","given":"Susan","email":"smcilroy@usgs.gov","middleInitial":"K.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":629675,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70170633,"text":"ofr20161048 - 2016 - Depth calibration of the Experimental Advanced Airborne Research Lidar, EAARL-B","interactions":[],"lastModifiedDate":"2016-05-18T09:54:00","indexId":"ofr20161048","displayToPublicDate":"2016-05-17T14:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1048","title":"Depth calibration of the Experimental Advanced Airborne Research Lidar, EAARL-B","docAbstract":"The original National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL) was extensively modified to increase the spatial sampling density and to improve performance in water ranging from 3 to 44 meters (m). The new (EAARL-B) sensor features a higher spatial density that was achieved by optically splitting each laser pulse into three pulses spatially separated by 1.6 m along the flight track and 2.0 m across the flight track, on the water surface when flown at a nominal altitude of 300 m (984 feet). The sample spacing can be optionally increased to 1.0 m across the flight track. Improved depth capability was achieved by increasing the total peak laser power by a factor of 10 and by designing a new “deep-water” receiver, which is optimized to exclusively receive refracted and scattered light from deeper water (15–44 m).
\nTwo different clear-water flight missions were conducted over the U.S. Navy's South Florida Testing Facility (SFTF) to determine the EAARL-B calibration coefficients. The SFTF is an established lidar calibration range located in the coastal waters southeast of Fort Lauderdale, Florida. We used 23 selected polygons at 23 distinct depths to compare a reference dataset from this site to determine EAARL-B calibration constants over the depth range of 6.5 to 34 m.
\nWe also conducted a near-simultaneous single-beam jet-ski-based sonar survey of selected transects ranging from 1 to 33 m depth in the same area. The near-concurrent jet ski data were used to evaluate the EAARL-B performance over the depth range from 0.9 to 10 m. The more timely jet ski data were necessary because the primary reference dataset was 9 years old, and areas shallower than 6.5 m are dominated by shifting sand. We determined the jet ski data were not useful as a calibration reference in water deeper than 10 m due to large uncertainty in the vertical measurement introduced by the lack of any sensor orientation data, that is, for pitch, roll, and heading to correct the measured slant range to a vertical measurement.
\nThe resulting calibrated EAARL-B data were then analyzed and compared with the original reference dataset, the jet-ski-based dataset from the same Fort Lauderdale site, as well as the depth-accuracy requirements of the International Hydrographic Organization (IHO). We do not claim to meet all of the IHO requirements and standards. The IHO minimum depth-accuracy requirements were used as a reference only and we do not address the other IHO requirements such as “ Full Seafloor Search”. Our results show good agreement between the calibrated EAARL-B data and all reference datasets, with results that are within the 95 percent depth accuracy of the IHO Order 1 (a and b) depth-accuracy requirements.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161048","usgsCitation":"Wright, C.W., Kranenburg, C.J., Troche, R.J., Mitchell, R.W., and, Nagle, D.B., 2016, Depth calibration of the experimental advanced airborne research lidar, EAARL-B: U.S. Geological Survey Open-File Report 2016–1048, 23 p., https://dx.doi.org/10.3133/ofr20161048.","productDescription":"Report: vi, 22 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-061552","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":320937,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1048/coverthb.jpg"},{"id":320951,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://dx.doi.org/10.5066/F79S1P4S","text":"Data Release"},{"id":320938,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1048/ofr20161048.pdf","text":"Report","size":"1.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1048"}],"contact":"Director, St. Petersburg Coastal and Marine Science Center
600 4th Street South
St. Petersburg, FL 33701
(727) 502-8000
http://coastal.er.usgs.gov/
Geolocators are useful for tracking movements of long-distance migrants, but potential negative effects on birds have not been well studied. We tested for effects of geolocators (0.8–2.0 g total, representing 0.1–3.9 % of mean body mass) on 16 species of migratory shorebirds, including five species with 2–4 subspecies each for a total of 23 study taxa. Study species spanned a range of body sizes (26–1091 g) and eight genera, and were tagged at 23 breeding and eight nonbreeding sites. We compared breeding performance and return rates of birds with geolocators to control groups while controlling for potential confounding variables.
\nWe detected negative effects of tags for three small-bodied species. Geolocators reduced annual return rates for two of 23 taxa: by 63 % for semipalmated sandpipers and by 43 % for the arcticola subspecies of dunlin. High resighting effort for geolocator birds could have masked additional negative effects. Geolocators were more likely to negatively affect return rates if the total mass of geolocators and color markers was 2.5–5.8 % of body mass than if tags were 0.3–2.3 % of body mass. Carrying a geolocator reduced nest success by 42 % for semipalmated sandpipers and tripled the probability of partial clutch failure in semipalmated and western sandpipers. Geolocators mounted perpendicular to the leg on a flag had stronger negative effects on nest success than geolocators mounted parallel to the leg on a band. However, parallel-band geolocators were more likely to reduce return rates and cause injuries to the leg. No effects of geolocators were found on breeding movements or changes in body mass. Among-site variation in geolocator effect size was high, suggesting that local factors were important.
\nNegative effects of geolocators occurred only for three of the smallest species in our dataset, but were substantial when present. Future studies could mitigate impacts of tags by reducing protruding parts and minimizing use of additional markers. Investigators could maximize recovery of tags by strategically deploying geolocators on males, previously marked individuals, and successful breeders, though targeting subsets of a population could bias the resulting migratory movement data in some species.
\nRates of oxygen and nitrate reduction are key factors in determining the chemical evolution of groundwater. Little is known about how these rates vary and covary in regional groundwater settings, as few studies have focused on regional datasets with multiple tracers and methods of analysis that account for effects of mixed residence times on apparent reaction rates. This study provides insight into the characteristics of residence times and rates of O2 reduction and denitrification (NO3− reduction) by comparing reaction rates using multi-model analytical residence time distributions (RTDs) applied to a data set of atmospheric tracers of groundwater age and geochemical data from 141 well samples in the Central Eastern San Joaquin Valley, CA. The RTD approach accounts for mixtures of residence times in a single sample to provide estimates of in-situ rates. Tracers included SF6, CFCs, 3H, He from 3H (tritiogenic He),14C, and terrigenic He. Parameter estimation and multi-model averaging were used to establish RTDs with lower error variances than those produced by individual RTD models. The set of multi-model RTDs was used in combination with NO3− and dissolved gas data to estimate zero order and first order rates of O2 reduction and denitrification. Results indicated that O2 reduction and denitrification rates followed approximately log-normal distributions. Rates of O2 and NO3− reduction were correlated and, on an electron milliequivalent basis, denitrification rates tended to exceed O2 reduction rates. Estimated historical NO3− trends were similar to historical measurements. Results show that the multi-model approach can improve estimation of age distributions, and that relatively easily measured O2 rates can provide information about trends in denitrification rates, which are more difficult to estimate.
","language":"English","publisher":"European Geophysical Society","doi":"10.1016/j.jhydrol.2016.05.018","usgsCitation":"Green, C.T., Jurgens, B.C., Zhang, Y., Starn, J., Singleton, M.J., and Esser, B.K., 2016, Regional oxygen reduction and denitrification rates in groundwater from multi-model residence time distributions, San Joaquin Valley, USA: Journal of Hydrology, v. 145, p. 47-55, https://doi.org/10.1016/j.jhydrol.2016.05.018.","productDescription":"9 p.","startPage":"47","endPage":"55","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067486","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":470992,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2016.05.018","text":"Publisher Index Page"},{"id":321295,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Joaquin Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.5,\n 37\n ],\n [\n -121.5,\n 38\n ],\n [\n -120,\n 38\n ],\n [\n -120,\n 37\n ],\n [\n -121.5,\n 37\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"145","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"574d566fe4b07e28b667f7a0","contributors":{"authors":[{"text":"Green, Christopher T. 0000-0002-6480-8194 ctgreen@usgs.gov","orcid":"https://orcid.org/0000-0002-6480-8194","contributorId":1343,"corporation":false,"usgs":true,"family":"Green","given":"Christopher","email":"ctgreen@usgs.gov","middleInitial":"T.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":629354,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X bjurgens@usgs.gov","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":127842,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","email":"bjurgens@usgs.gov","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":629355,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Yong","contributorId":19029,"corporation":false,"usgs":true,"family":"Zhang","given":"Yong","affiliations":[],"preferred":false,"id":629356,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Starn, Jeffrey jjstarn@usgs.gov","contributorId":149231,"corporation":false,"usgs":true,"family":"Starn","given":"Jeffrey","email":"jjstarn@usgs.gov","affiliations":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"preferred":true,"id":629357,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Singleton, Michael J.","contributorId":44400,"corporation":false,"usgs":true,"family":"Singleton","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":629358,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Esser, Bradley K.","contributorId":33161,"corporation":false,"usgs":true,"family":"Esser","given":"Bradley","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":629359,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70169143,"text":"sir20165033 - 2016 - Effects of variations in flow characteristics through W.P. Franklin Lock and Dam on downstream water quality in the Caloosahatchee River Estuary and in McIntyre Creek in the J.N. “Ding” Darling National Wildlife Refuge, southern Florida, 2010–13","interactions":[],"lastModifiedDate":"2016-05-18T08:50:38","indexId":"sir20165033","displayToPublicDate":"2016-05-17T00:00:00","publicationYear":"2016","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":"2016-5033","title":"Effects of variations in flow characteristics through W.P. Franklin Lock and Dam on downstream water quality in the Caloosahatchee River Estuary and in McIntyre Creek in the J.N. “Ding” Darling National Wildlife Refuge, southern Florida, 2010–13","docAbstract":"The U.S. Geological Survey studied water-quality trends at the mouth of McIntyre Creek, an entry point to the J.N. “Ding” Darling National Wildlife Refuge, to investigate correlations between flow rates and volumes through the W.P. Franklin Lock and Dam and water-quality constituents inside the refuge from March 2010 to December 2013. Outflow from Lake Okeechobee, and flows from Franklin Lock, tributaries to the Caloosahatchee River Estuary, and the Cape Coral canal system were examined to determine the sources and quantity of water to the study area. Salinity, temperature, dissolved-oxygen concentration, pH, turbidity, and chromophoric dissolved organic matter fluorescence (FDOM) were measured during moving-boat surveys and at a fixed location in McIntyre Creek. Chlorophyll fluorescence was also recorded in McIntyre Creek. Water-quality surveys were completed on 20 dates between 2011 and 2014 using moving-boat surveys.
Franklin Lock contributed the majority of flow to the Caloosahatchee River. Between 2010 and 2013, the monthly mean flow rate at Franklin Lock ranged from 29 cubic feet per second in May 2011 to 10,650 cubic feet per second in August 2013. Instantaneous near-surface salinity in McIntyre Creek ranged from 12.9 parts per thousand on September 26, 2013, to 37.9 parts per thousand on June 27, 2011. Salinity in McIntyre Creek decreased with increasing flow rate through Franklin Lock. Flow rates through Franklin Lock explained 61 percent of the variation in salinity in McIntyre Creek. Salinity data from moving-boat surveys also indicate that an increase in flow rate at Franklin Lock decreases salinity in the Caloosahatchee River Estuary, and a reduction or elimination in flow increases salinity. The FDOM in McIntyre Creek was positively correlated with flow at Franklin Lock, and 54 percent of the variation in FDOM can be attributed to the flow rate through Franklin Lock. Data from moving-boat surveys indicate that FDOM increases when flow volume from Franklin Lock increases. The highest FDOM recorded during a survey was at Billy’s Creek. Chlorophyll fluorescence was positively correlated with flow at Franklin Lock, with 23 percent of the variation explained by the flow rate at Franklin Lock. An increase in flow rate at Franklin Lock resulted in a decrease in pH (21 percent of variation explained by flow rates). Data from the pH surveys indicate an increase in pH with distance from Franklin Lock. Turbidity and dissolved oxygen near the surface in McIntyre Creek were not correlated with flow rate at Franklin Lock. Moving-boat surveys did not document a change in turbidity or dissolved oxygen with a change in distance from the Franklin Lock. Correlations between Franklin Lock flow rate and water quality in McIntyre Creek indicate that releases at Franklin Lock affect water quality in the Caloosahatchee River Estuary and Ding Darling Refuge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165033","collaboration":"Prepared as part of the Greater Everglades Priority Ecosystems Science Initiative and in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Booth, A.C., Soderqvist, L.E., and Knight, T.M., 2016, Effects of variations in flow characteristics through W.P. Franklin Lock and Dam on downstream water quality in the Caloosahatchee River Estuary and in McIntyre Creek in the J.N. “Ding” Darling National Wildlife Refuge, southern Florida, 2010–13: U.S. Geological Survey Scientific Investigations Report 2016–5033, 33 p., https://dx.doi.org/10.3133/sir20165033.","productDescription":"Report: vii, 33 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-063026","costCenters":[{"id":269,"text":"FLWSC-Ft. Lauderdale","active":true,"usgs":true}],"links":[{"id":321251,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5033/coverthb.jpg"},{"id":321252,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5033/sir20165033.pdf","text":"Report","size":"11.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5033"},{"id":321253,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://dx.doi.org/10.5066/F70863BC","text":"Data Release","description":"Data Release"}],"country":"United States","state":"Florida","otherGeospatial":"Caloosahatchee River Estuary, J.N. “Ding” Darling National Wildlife Refuge, McIntyre Creek,","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.1942138671875,\n 26.40417061185344\n ],\n [\n -82.1942138671875,\n 26.831423660953195\n ],\n [\n -81.24938964843749,\n 26.831423660953195\n ],\n [\n -81.24938964843749,\n 26.40417061185344\n ],\n [\n -82.1942138671875,\n 26.40417061185344\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Industrial practices at the Naval Industrial Reserve Ordnance Plant, in Fridley, Minnesota, caused soil and groundwater contamination. Some volatile organic compounds from the plant might have discharged to the Mississippi River, forced by the natural hydraulic gradient in the surficial aquifer system. The U.S. Environmental Protection Agency included the Naval Industrial Reserve Ordnance Plant on the Superfund National Priorities List in 1989.
\nThis report describes a preliminary characterization of trichloroethene transport in the surficial and Cambrian-Ordovician aquifer systems at the Naval Industrial Reserve Ordnance Plant. The characterization first involved simulation of 2001 conditions using a model, followed by an application of this 2001 simulator to 2011 conditions.
\nThe U.S. Geological Survey, in cooperation with the U.S. Department of the Navy, used a steady-state, uniform-density groundwater flow model to simulate measured potentiometric heads in aquifer systems on August 20, 2001, and a single-phase, conservative, non-reactive, miscible transport model to simulate trichloroethene concentrations in aquifer systems measured in 2001. The U.S. Department of the Navy furnished trichloroethene source areas and trichloroethene source area concentrations to the U.S. Geological Survey for this model simulation. Furnished delineations were postulated and informed by data collected from 1995 to 2011. The groundwater flow simulation of August 20, 2001, was superior to the trichloroethene transport simulation at replicating measurements; simulated potentiometric heads matched 90 percent of measured potentiometric heads on August 20, within 2 feet at selected locations whereas simulated trichloroethene concentration contours of 3, 10, 100, 1000, and 10,000 micrograms per liter (µg/L) correctly bounded 52 percent of measured concentrations in 2001 at selected locations. The degree to which the simulated trichloroethene plume does not match trichloroethene measurements in the surficial aquifer system during the 2001 simulation may suggest that furnished trichloroethene source areas and trichloroethene source area concentrations did not accurately represent all trichloroethene sources in the hydrogeologic system.
\nDuring the model simulation of 2001, trichloroethene discharged to the Mississippi River. A simulated 900-foot-long zone of benthic trichloroethene discharge flux existed in the shallow flow zone, across which simulated trichloroethene discharged from the surficial aquifer system to the Mississippi River at simulated trichloroethene concentrations that ranged from 3 µg/L to more than 100 µg/L. The Mississippi River was not sampled for volatile organic compounds in Fridley, Minn., from 1999 to 2016 (the publication of this report). Trichloroethene concentrations were measured in wells close to the Mississippi River in the surficial aquifer system on the downgradient side of the Naval Industrial Reserve Ordnance Plant groundwater flow field; for example, at well MS–43 in the shallow flow zone of the surficial aquifer system 280 feet east of the Mississippi River between December 1999 and August 2012, trichloroethene concentrations ranged from 130 to 220 µg/L. The 220-µg/L maximum concentration was reached in March 2003 and October 2006. The August 2012 concentration was 140 µg/L.
\nThe August 20, 2001, groundwater flow model simulator and the 2001 trichloroethene transport simulator were applied to a groundwater extraction and treatment system that existed in 2011. Furnished trichloroethene source areas and concentrations in the 2001 simulator were replaced with different, furnished, hypothetical source areas and concentrations. Forcing in 2001 was replaced with forcing in 2011. No trichloroethene concentrations greater than 3 µg/L were simulated as discharging to the Mississippi River during applications of the 2001 simulator to the 2011 groundwater extraction and treatment system. These applications were not intended to represent historical conditions. Differences between furnished and actual trichloroethene sources may explain differences between measurements and simulation results for the 2001 trichloroethene transport simulator. Causes of differences between furnished and actual trichloroethene sources may cause differences between hypothetical application results and the performance of the actual U.S. Department of the Navy groundwater extraction and treatment system at the Naval Industrial Reserve Ordnance Plant. Other limitations may also cause differences between application results and performance.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161066","collaboration":"Prepared in cooperation with the U.S. Department of the Navy, Naval Facilities Engineering Command","usgsCitation":"King, J.N., and Davis, J.H., 2016, Preliminary investigation of groundwater flow and trichloroethene transport in the surficial aquifer system, Naval Industrial Reserve Ordnance Plant, Fridley, Minnesota: U.S. Geological Survey Open File Report 2016–1066, 120 p., https://dx.doi.org/10.3133/ofr20161066.","productDescription":"Report: x, 120 p.; Metadata","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-039553","costCenters":[{"id":269,"text":"FLWSC-Ft. Lauderdale","active":true,"usgs":true}],"links":[{"id":321042,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://dx.doi.org/10.5066/F798853M","text":"Data Release","linkFileType":{"id":5,"text":"html"},"description":"OFR 2016-1066"},{"id":321040,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1066/coverthb.jpg"},{"id":321041,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1066/ofr20161066.pdf","text":"Report","size":"12,1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1066"}],"country":"United States","state":"Minnesota","city":"Fridley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.38172912597656,\n 45.09582203415993\n ],\n [\n -93.34877014160155,\n 45.03228854011639\n ],\n [\n -93.27735900878906,\n 45.02986219868277\n ],\n [\n -92.96905517578125,\n 45.180584858570136\n ],\n [\n -93.043212890625,\n 45.25652199219273\n ],\n [\n -93.38172912597656,\n 45.09582203415993\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Minnesota Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
(763) 783-3100
http://mn.water.usgs.gov/
Wind power generation is rapidly expanding. Although wind power is a low-carbon source of energy, it can impact negatively birds and bats, either directly through fatality or indirectly by displacement or habitat loss. Pre-construction risk assessment at wind facilities within the United States is usually required only on public lands. When conducted, it generally involves a 3-tier process, with each step leading to more detailed and rigorous surveys. Preliminary site assessment (U.S. Fish and Wildlife Service, Tier 1) is usually conducted remotely and involves evaluation of existing databases and published materials. If potentially at-risk wildlife are present and the developer wishes to continue the development process, then on-site surveys are conducted (Tier 2) to verify the presence of those species and to assess site-specific features (e.g., topography, land cover) that may influence risk from turbines. The next step in the process (Tier 3) involves quantitative or scientific studies to assess the potential risk of the proposed project to wildlife. Typical Tier-3 research may involve acoustic, aural, observational, radar, capture, tracking, or modeling studies, all designed to understand details of risk to specific species or groups of species at the given site. Our review highlights several features lacking from many risk assessments, particularly the paucity of before-and-after-control- impact (BACI) studies involving modeling and a lack of understanding of cumulative effects of wind facilities on wildlife. Both are essential to understand effective designs for pre-construction monitoring and both would help expand risk assessment beyond eagles.
","language":"English","publisher":"Berryman Institute","doi":"10.26077/phxc-zh11","usgsCitation":"Katzner, T., Bennett, V., Miller, T., Duerr, A.E., Braham, M., and Hale, A., 2016, Wind energy development: Methods for assessing risks to birds and bats pre-construction: Human-Wildlife Interactions, v. 10, no. 1, p. 42-52, https://doi.org/10.26077/phxc-zh11.","productDescription":"11 p.","startPage":"42","endPage":"52","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063881","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":321232,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"574d567fe4b07e28b667f7bf","contributors":{"authors":[{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":5979,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":629316,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bennett, Victoria","contributorId":169316,"corporation":false,"usgs":false,"family":"Bennett","given":"Victoria","affiliations":[{"id":25471,"text":"Texas Christian University","active":true,"usgs":false}],"preferred":false,"id":629317,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Tricia A.","contributorId":64790,"corporation":false,"usgs":true,"family":"Miller","given":"Tricia A.","affiliations":[],"preferred":false,"id":629318,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duerr, Adam E.","contributorId":102324,"corporation":false,"usgs":true,"family":"Duerr","given":"Adam","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":629319,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Braham, Melissa A.","contributorId":140127,"corporation":false,"usgs":false,"family":"Braham","given":"Melissa A.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":629320,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hale, Amanda","contributorId":169317,"corporation":false,"usgs":false,"family":"Hale","given":"Amanda","affiliations":[{"id":25471,"text":"Texas Christian University","active":true,"usgs":false}],"preferred":false,"id":629321,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70175784,"text":"70175784 - 2016 - Novel insights from NMR spectroscopy into seasonal changes in the composition of dissolved organic matter exported to the Bering Sea by the Yukon River","interactions":[],"lastModifiedDate":"2016-08-19T10:23:58","indexId":"70175784","displayToPublicDate":"2016-05-15T14:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Novel insights from NMR spectroscopy into seasonal changes in the composition of dissolved organic matter exported to the Bering Sea by the Yukon River","docAbstract":"Seasonal (spring freshet, summer–autumn, and winter) variability in the chemical composition of dissolved organic matter (DOM) from the Yukon River was determined using advanced one- and two-dimensional (2D) solid-state NMR spectroscopy, coupled with isotopic measurements and UV–visible spectroscopy. Analyses were performed on two major DOM fractions, the hydrophobic organic acid (HPOA) and transphilic organic acid (TPIA) fractions obtained using XAD resins. Together these two fractions comprised 64–74% of the total DOM. Carboxyl-rich alicyclic molecules (CRAM) accounted for the majority of carbon atoms in the HPOA (63–77%) and TPIA (54–78%) samples, and more so in winter and summer than in spring samples. 2D and selective NMR data revealed association of abundant nonprotonated O-alkyl and quaternary alkyl C (OCnp, OCnpO and Cq, 13–17% of HPOA and 15–20% of TPIA) and isolated O–CH structures with CRAM, which were not recognized in previous studies. Spectral editing and 2D NMR allowed for the discrimination of carbohydrate-like O-alkyl C from non-carbohydrate O-alkyl C. Whereas two spring freshet TPIA samples contained carbohydrate clusters such as carboxylated carbohydrates (16% and 26%), TPIA samples from other seasons or HPOA samples mostly had small amounts (<8%) of sugar rings dispersed in a nonpolar alkyl environment. Though nonprotonated aromatic C represented the largest fraction of aromatic C in all HPOA/TPIA isolates, only a small fraction (∼5% in HPOA and 3% in TPIA) was possibly associated with dissolved black carbon. Our results imply a relatively stable portion of DOM exported by the Yukon River across different seasons, due to the predominance of CRAM and their associated nonprotonated C–O and O–C–O structures, and elevated reactivity (bio- and photo-lability) of spring DOM due to the presence of terrestrial inputs enriched in carbohydrates and aromatic structures.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2016.02.029","usgsCitation":"Cao, X., Aiken, G.R., Spencer, R., Butler, K.D., Mao, J., and Schmidt-Rohr, K., 2016, Novel insights from NMR spectroscopy into seasonal changes in the composition of dissolved organic matter exported to the Bering Sea by the Yukon River: Geochimica et Cosmochimica Acta, v. 181, p. 72-88, https://doi.org/10.1016/j.gca.2016.02.029.","productDescription":"16 p.","startPage":"72","endPage":"88","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073317","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":470996,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gca.2016.02.029","text":"Publisher Index Page"},{"id":326925,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, Yukon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.2568359375,\n 63.05495931065107\n ],\n [\n -164.00390625,\n 63.35212928507874\n ],\n [\n -164.8828125,\n 63.31268278043484\n ],\n [\n -165.322265625,\n 62.83508901142283\n ],\n [\n -165.76171875,\n 62.32920841458002\n ],\n [\n -165.9814453125,\n 61.95961583829658\n ],\n [\n -166.376953125,\n 61.64816245852389\n ],\n [\n -166.376953125,\n 61.270232790000634\n ],\n [\n -165.5419921875,\n 60.973107109199404\n ],\n [\n -164.53125,\n 60.88770004207789\n ],\n [\n -162.4658203125,\n 61.10078883158897\n ],\n [\n -159.78515624999997,\n 61.81466389468391\n ],\n [\n -157.67578125,\n 62.36999628130772\n ],\n [\n -156.4453125,\n 62.79493487887006\n ],\n [\n -152.05078125,\n 63.27318217465046\n ],\n [\n -150.0732421875,\n 63.35212928507874\n ],\n [\n -147.216796875,\n 63.35212928507874\n ],\n [\n -145.5908203125,\n 63.194018438087845\n ],\n [\n -144.8876953125,\n 62.22699603631975\n ],\n [\n -142.91015625,\n 61.41775026352099\n ],\n [\n -138.9111328125,\n 60.413852350464914\n ],\n [\n -135.966796875,\n 60.10867046303597\n ],\n [\n -132.4951171875,\n 59.66774058164963\n ],\n [\n -130.9130859375,\n 58.859223547066584\n ],\n [\n -126.474609375,\n 58.97266715450153\n ],\n [\n -125.1123046875,\n 59.99898612060444\n ],\n [\n -124.23339843749999,\n 61.10078883158897\n ],\n [\n -124.3212890625,\n 62.22699603631975\n ],\n [\n -125.20019531249999,\n 64.16810689799152\n ],\n [\n -126.73828125,\n 64.9607663214987\n ],\n [\n -129.5947265625,\n 65.49474141843486\n ],\n [\n -131.5283203125,\n 65.49474141843486\n ],\n [\n -133.8134765625,\n 65.18303007291382\n ],\n [\n -140.80078125,\n 66.08936427047088\n ],\n [\n -141.6796875,\n 67.7094454829218\n ],\n [\n -141.1962890625,\n 68.72044056989829\n ],\n [\n -141.240234375,\n 69.3493386397765\n ],\n [\n -144.580078125,\n 69.79413569863112\n ],\n [\n -148.53515625,\n 69.39578308847753\n ],\n [\n -149.4140625,\n 69.14692017504962\n ],\n [\n -150.9521484375,\n 68.64055504059381\n ],\n [\n -153.80859375,\n 68.39918004344189\n ],\n [\n -155.56640625,\n 68.28565146003619\n ],\n [\n -157.5,\n 67.69277095059344\n ],\n [\n -157.2802734375,\n 66.86108230224612\n ],\n [\n -157.8076171875,\n 66.28453710088559\n ],\n [\n -160.4443359375,\n 65.67638148211446\n ],\n [\n -160.3125,\n 65.14611484756372\n ],\n [\n -160.26855468749997,\n 64.49172504435471\n ],\n [\n -160.576171875,\n 63.58767529470318\n ],\n [\n -161.806640625,\n 63.48976680530999\n ],\n [\n -162.333984375,\n 63.84066844285508\n ],\n [\n -162.5537109375,\n 63.48976680530999\n ],\n [\n -163.2568359375,\n 63.05495931065107\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"181","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57b82de0e4b03fd6b7da395f","chorus":{"doi":"10.1016/j.gca.2016.02.029","url":"http://dx.doi.org/10.1016/j.gca.2016.02.029","publisher":"Elsevier BV","authors":"Cao Xiaoyan, Aiken George R., Spencer Robert G.M., Butler Kenna, Mao Jingdong, Schmidt-Rohr Klaus","journalName":"Geochimica et Cosmochimica Acta","publicationDate":"5/2016"},"contributors":{"authors":[{"text":"Cao, Xiaoyan","contributorId":86238,"corporation":false,"usgs":true,"family":"Cao","given":"Xiaoyan","affiliations":[],"preferred":false,"id":646348,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aiken, George R. 0000-0001-8454-0984 graiken@usgs.gov","orcid":"https://orcid.org/0000-0001-8454-0984","contributorId":1322,"corporation":false,"usgs":true,"family":"Aiken","given":"George","email":"graiken@usgs.gov","middleInitial":"R.","affiliations":[{"id":5044,"text":"National Research Program - 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M.","contributorId":139731,"corporation":false,"usgs":false,"family":"Spencer","given":"Robert G. M.","affiliations":[{"id":12894,"text":"Department of Land, Air, and Water Resources, University of California, One Shields Avenue, Davis, CA, 95616, USA","active":true,"usgs":false}],"preferred":false,"id":646349,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Butler, Kenna D. kebutler@usgs.gov","contributorId":3283,"corporation":false,"usgs":true,"family":"Butler","given":"Kenna","email":"kebutler@usgs.gov","middleInitial":"D.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":646350,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mao, Jingdong","contributorId":79373,"corporation":false,"usgs":true,"family":"Mao","given":"Jingdong","affiliations":[],"preferred":false,"id":646351,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schmidt-Rohr, Klaus","contributorId":173865,"corporation":false,"usgs":false,"family":"Schmidt-Rohr","given":"Klaus","email":"","affiliations":[{"id":27307,"text":"Dept. of Chemistry, Brandeis University, Waltham, MA","active":true,"usgs":false}],"preferred":false,"id":646352,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70199499,"text":"70199499 - 2016 - Calorific value and compositional ultimate analysis with a case study of a Texas lignite","interactions":[],"lastModifiedDate":"2018-09-20T10:52:43","indexId":"70199499","displayToPublicDate":"2016-05-15T10:52:21","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Calorific value and compositional ultimate analysis with a case study of a Texas lignite","docAbstract":"Measurements to determine coal quality as fuel include proximate analysis, ultimate analysis and calorific value. The latter is an attribute taking non-negative real values, so a simple transformation is sufficient for its spatial modeling applying geostatistics. The analyses, however, involve proportions that follow the properties of compositional data, thus requiring special preprocessing for an adequate modeling already described in a previous publication for the case of proximate analysis data.1 Here we model the results of calorific value and ultimate analysis. We propose to use two different binary partitions, one per analysis, map the corresponding isometric logratio transformations, and backtransform the results. The methodology is illustrated using the same coal bed in the previous paper modeling proximate analysis data. Results are summarized using probability maps that, in the case of this deposit, show a prominent channel crossing the deposit and separating the best quality coal from that of lower quality.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2016.05.005","usgsCitation":"Olea, R., Luppens, J., Egozcue, J.J., and Pawlowsky-Glahn, V., 2016, Calorific value and compositional ultimate analysis with a case study of a Texas lignite: International Journal of Coal Geology, v. 162, p. 27-33, https://doi.org/10.1016/j.coal.2016.05.005.","productDescription":"7 p.","startPage":"27","endPage":"33","ipdsId":"IP-071169","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":357542,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"162","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bc0335ae4b0fc368eb53a80","contributors":{"authors":[{"text":"Olea, Ricardo A. 0000-0003-4308-0808","orcid":"https://orcid.org/0000-0003-4308-0808","contributorId":120616,"corporation":false,"usgs":true,"family":"Olea","given":"Ricardo A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":745594,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Luppens, James 0000-0001-7607-8750","orcid":"https://orcid.org/0000-0001-7607-8750","contributorId":208009,"corporation":false,"usgs":true,"family":"Luppens","given":"James","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":745595,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Egozcue, Juan J.","contributorId":208010,"corporation":false,"usgs":false,"family":"Egozcue","given":"Juan","email":"","middleInitial":"J.","affiliations":[{"id":37677,"text":"Dept. Civil and Environmental Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain","active":true,"usgs":false}],"preferred":false,"id":745596,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pawlowsky-Glahn, Vera","contributorId":208011,"corporation":false,"usgs":false,"family":"Pawlowsky-Glahn","given":"Vera","email":"","affiliations":[{"id":37678,"text":"Dept. Informatics, Applied Matematics and Statistics, Universitat de Girona, Spain","active":true,"usgs":false}],"preferred":false,"id":745597,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191096,"text":"70191096 - 2016 - Nature, distribution, and origin of Titan’s Undifferentiated Plains","interactions":[],"lastModifiedDate":"2017-09-26T13:45:06","indexId":"70191096","displayToPublicDate":"2016-05-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1963,"text":"Icarus","active":true,"publicationSubtype":{"id":10}},"title":"Nature, distribution, and origin of Titan’s Undifferentiated Plains","docAbstract":"The Undifferentiated Plains on Titan, first mapped by Lopes et al. (Lopes, R.M.C. et al., 2010. Icarus, 205, 540–588), are vast expanses of terrains that appear radar-dark and fairly uniform in Cassini Synthetic Aperture Radar (SAR) images. As a result, these terrains are often referred to as “blandlands”. While the interpretation of several other geologic units on Titan – such as dunes, lakes, and well-preserved impact craters – has been relatively straightforward, the origin of the Undifferentiated Plains has remained elusive. SAR images show that these “blandlands” are mostly found at mid-latitudes and appear relatively featureless at radar wavelengths, with no major topographic features. Their gradational boundaries and paucity of recognizable features in SAR data make geologic interpretation particularly challenging. We have mapped the distribution of these terrains using SAR swaths up to flyby T92 (July 2013), which cover >50% of Titan’s surface. We compared SAR images with other data sets where available, including topography derived from the SARTopo method and stereo DEMs, the response from RADAR radiometry, hyperspectral imaging data from Cassini’s Visual and Infrared Mapping Spectrometer (VIMS), and near infrared imaging from the Imaging Science Subsystem (ISS). We examined and evaluated different formation mechanisms, including (i) cryovolcanic origin, consisting of overlapping flows of low relief or (ii) sedimentary origins, resulting from fluvial/lacustrine or aeolian deposition, or accumulation of photolysis products created in the atmosphere. Our analysis indicates that the Undifferentiated Plains unit is consistent with a composition predominantly containing organic rather than icy materials and formed by depositional and/or sedimentary processes. We conclude that aeolian processes played a major part in the formation of the Undifferentiated Plains; however, other processes (fluvial, deposition of photolysis products) are likely to have contributed, possibly in differing proportions depending on location.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.icarus.2015.11.034","usgsCitation":"Lopes, R., Malaska, M., Solomonidou, A., Le, G.A., Janssen, M., Neish, C.D., Turtle, E.P., Birch, S.P., Hayes, A., Radebaugh, J., Coustenis, A., Schoenfeld, A., Stiles, B., Kirk, R.L., Mitchell, K.L., Stofan, E.R., Lawrence, K.J., and Cassini RADAR Team, 2016, Nature, distribution, and origin of Titan’s Undifferentiated Plains: Icarus, v. 270, p. 162-182, https://doi.org/10.1016/j.icarus.2015.11.034.","productDescription":"21 p.","startPage":"162","endPage":"182","ipdsId":"IP-079696","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":346096,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"270","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59cb6734e4b017cf3141c6a1","contributors":{"authors":[{"text":"Lopes, Rosaly","contributorId":50280,"corporation":false,"usgs":true,"family":"Lopes","given":"Rosaly","affiliations":[],"preferred":false,"id":711166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Malaska, M. 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