Rejuvenation of previously intruded silicic magma is an important process leading to effusive rhyolite, which is the most common product of volcanism at calderas with protracted histories of eruption and unrest such as Yellowstone, Long Valley, and Valles, USA. Although orders of magnitude smaller in volume than rare caldera-forming super-eruptions, these relatively frequent effusions of rhyolite are comparable to the largest eruptions of the 20th century and pose a considerable volcanic hazard. However, the physical pathway from rejuvenation to eruption of silicic magma is unclear particularly because the time between reheating of a subvolcanic intrusion and eruption is poorly quantified. This study uses geospeedometry of trace element profiles with nanometer resolution in sanidine crystals to reveal that Yellowstone’s most recent volcanic cycle began when remobilization of a near- or sub-solidus silicic magma occurred less than 10 months prior to eruption, following a 220,000 year period of volcanic repose. Our results reveal a geologically rapid timescale for rejuvenation and effusion of ~3 km3 of high-silica rhyolite lava even after protracted cooling of the subvolcanic system, which is consistent with recent physical modeling that predict a timescale of several years or less. Future renewal of rhyolitic volcanism at Yellowstone is likely to require an energetic intrusion of mafic or silicic magma into the shallow subvolcanic reservoir and could rapidly generate an eruptible rhyolite on timescales similar to those documented here.
","language":"English","publisher":"Geological Society of America","publisherLocation":"Boulder, CO","doi":"10.1130/G36862.1","usgsCitation":"Till, C.B., Vazquez, J.A., and Boyce, J., 2015, Months between rejuvenation and volcanic eruption at Yellowstone caldera, Wyoming: Geology, v. 43, no. 8, p. 695-698, https://doi.org/10.1130/G36862.1.","productDescription":"4 p.","startPage":"695","endPage":"698","numberOfPages":"5","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063317","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":306656,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone caldera","volume":"43","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-01","publicationStatus":"PW","scienceBaseUri":"55cdb1ace4b08400b1fe13ab","contributors":{"authors":[{"text":"Till, Christy B. cbtill@usgs.gov","contributorId":4394,"corporation":false,"usgs":true,"family":"Till","given":"Christy","email":"cbtill@usgs.gov","middleInitial":"B.","affiliations":[],"preferred":true,"id":567389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vazquez, Jorge A. 0000-0003-2754-0456 jvazquez@usgs.gov","orcid":"https://orcid.org/0000-0003-2754-0456","contributorId":4458,"corporation":false,"usgs":true,"family":"Vazquez","given":"Jorge","email":"jvazquez@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":567388,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyce, Jeremy W","contributorId":146313,"corporation":false,"usgs":false,"family":"Boyce","given":"Jeremy W","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":567390,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155863,"text":"70155863 - 2015 - Lilac and honeysuckle phenology data 1956–2014","interactions":[],"lastModifiedDate":"2015-09-16T10:25:32","indexId":"70155863","displayToPublicDate":"2015-08-13T12:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Lilac and honeysuckle phenology data 1956–2014","docAbstract":"The dataset is comprised of leafing and flowering data collected across the continental United States from 1956 to 2014 for purple common lilac (Syringa vulgaris), a cloned lilac cultivar (S. x chinensis ‘Red Rothomagensis’) and two cloned honeysuckle cultivars (Lonicera tatarica ‘Arnold Red’ and L. korolkowii ‘Zabeli’). Applications of this observational dataset range from detecting regional weather patterns to understanding the impacts of global climate change on the onset of spring at the national scale. While minor changes in methods have occurred over time, and some documentation is lacking, outlier analyses identified fewer than 3% of records as unusually early or late. Lilac and honeysuckle phenology data have proven robust in both model development and climatic research.
","language":"English","publisher":"Nature Publishing Group","publisherLocation":"London, UK","doi":"10.1038/sdata.2015.38","usgsCitation":"Rosemartin, A.H., Denny, E.G., Weltzin, J., Marsh, R.L., Wilson, B.E., Mehdipoor, H., Zurita-Milla, R., and Schwartz, M., 2015, Lilac and honeysuckle phenology data 1956–2014: Scientific Data, v. 2, 150038; 8 p., https://doi.org/10.1038/sdata.2015.38.","productDescription":"150038; 8 p.","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"1956-01-01","temporalEnd":"2014-12-31","ipdsId":"IP-061549","costCenters":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"links":[{"id":471877,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/sdata.2015.38","text":"Publisher Index Page"},{"id":308175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-21","publicationStatus":"PW","scienceBaseUri":"55fa92c1e4b05d6c4e501aa1","contributors":{"authors":[{"text":"Rosemartin, Alyssa H.","contributorId":30910,"corporation":false,"usgs":true,"family":"Rosemartin","given":"Alyssa","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":566620,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Denny, Ellen G.","contributorId":79803,"corporation":false,"usgs":true,"family":"Denny","given":"Ellen","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":566621,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weltzin, Jake F. jweltzin@usgs.gov","contributorId":296,"corporation":false,"usgs":true,"family":"Weltzin","given":"Jake F.","email":"jweltzin@usgs.gov","affiliations":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"preferred":false,"id":566618,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marsh, R. Lee","contributorId":146211,"corporation":false,"usgs":false,"family":"Marsh","given":"R.","email":"","middleInitial":"Lee","affiliations":[{"id":16629,"text":"USA National Phenology Network, SNRE University of Arizona","active":true,"usgs":false}],"preferred":false,"id":566622,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, Bruce E.","contributorId":94944,"corporation":false,"usgs":true,"family":"Wilson","given":"Bruce","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":566623,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mehdipoor, Hamed","contributorId":146212,"corporation":false,"usgs":false,"family":"Mehdipoor","given":"Hamed","email":"","affiliations":[{"id":16630,"text":"Department of Geo-Information Processing, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":566624,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zurita-Milla, Raul","contributorId":146213,"corporation":false,"usgs":false,"family":"Zurita-Milla","given":"Raul","email":"","affiliations":[{"id":16630,"text":"Department of Geo-Information Processing, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":566625,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schwartz, Mark D.","contributorId":11092,"corporation":false,"usgs":true,"family":"Schwartz","given":"Mark D.","affiliations":[],"preferred":false,"id":566619,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70155814,"text":"70155814 - 2015 - Natural recharge estimation and uncertainty analysis of an adjudicated groundwater basin using a regional-scale flow and subsidence model (Antelope Valley, California, USA)","interactions":[],"lastModifiedDate":"2015-08-13T10:33:01","indexId":"70155814","displayToPublicDate":"2015-08-13T11:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Natural recharge estimation and uncertainty analysis of an adjudicated groundwater basin using a regional-scale flow and subsidence model (Antelope Valley, California, USA)","docAbstract":"Groundwater has provided 50–90 % of the total water supply in Antelope Valley, California (USA). The associated groundwater-level declines have led the Los Angeles County Superior Court of California to recently rule that the Antelope Valley groundwater basin is in overdraft, i.e., annual pumpage exceeds annual recharge. Natural recharge consists primarily of mountain-front recharge and is an important component of the total groundwater budget in Antelope Valley. Therefore, natural recharge plays a major role in the Court’s decision. The exact quantity and distribution of natural recharge is uncertain, with total estimates from previous studies ranging from 37 to 200 gigaliters per year (GL/year). In order to better understand the uncertainty associated with natural recharge and to provide a tool for groundwater management, a numerical model of groundwater flow and land subsidence was developed. The transient model was calibrated using PEST with water-level and subsidence data; prior information was incorporated through the use of Tikhonov regularization. The calibrated estimate of natural recharge was 36 GL/year, which is appreciably less than the value used by the court (74 GL/year). The effect of parameter uncertainty on the estimation of natural recharge was addressed using the Null-Space Monte Carlo method. A Pareto trade-off method was also used to portray the reasonableness of larger natural recharge rates. The reasonableness of the 74 GL/year value and the effect of uncertain pumpage rates were also evaluated. The uncertainty analyses indicate that the total natural recharge likely ranges between 34.5 and 54.3 GL/year.
","language":"English","publisher":"Springer","publisherLocation":"Heidelberg, Germany","doi":"10.1007/s10040-015-1281-y","usgsCitation":"Siade, A.J., Nishikawa, T., and Martin, P., 2015, Natural recharge estimation and uncertainty analysis of an adjudicated groundwater basin using a regional-scale flow and subsidence model (Antelope Valley, California, USA): Hydrogeology Journal, v. 23, no. 6, p. 1267-1291, https://doi.org/10.1007/s10040-015-1281-y.","productDescription":"25 p.","startPage":"1267","endPage":"1291","numberOfPages":"25","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-037195","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":471880,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-015-1281-y","text":"Publisher Index Page"},{"id":306633,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Antelope Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.19503784179688,\n 35.16819542676796\n ],\n [\n -118.90228271484374,\n 34.84536693184099\n ],\n [\n -118.91189575195312,\n 34.78222760653013\n ],\n [\n -117.45620727539062,\n 34.30260622622907\n ],\n [\n -117.54959106445312,\n 35.163704834815874\n ],\n [\n -118.19503784179688,\n 35.16819542676796\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","issue":"6","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-24","publicationStatus":"PW","scienceBaseUri":"55cdb1ade4b08400b1fe13b1","contributors":{"authors":[{"text":"Siade, Adam J. asiade@usgs.gov","contributorId":1533,"corporation":false,"usgs":true,"family":"Siade","given":"Adam","email":"asiade@usgs.gov","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566453,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nishikawa, Tracy 0000-0002-7348-3838 tnish@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-3838","contributorId":1515,"corporation":false,"usgs":true,"family":"Nishikawa","given":"Tracy","email":"tnish@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566455,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Peter pmmartin@usgs.gov","contributorId":799,"corporation":false,"usgs":true,"family":"Martin","given":"Peter","email":"pmmartin@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566454,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70154775,"text":"70154775 - 2015 - Landscapes for energy and wildlife: conservation prioritization for golden eagles across large spatial scales","interactions":[],"lastModifiedDate":"2019-06-03T13:24:23","indexId":"70154775","displayToPublicDate":"2015-08-13T11:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Landscapes for energy and wildlife: conservation prioritization for golden eagles across large spatial scales","docAbstract":"Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publicly available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development.
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\"}}]}","volume":"10","issue":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-11","publicationStatus":"PW","scienceBaseUri":"55cdb1ace4b08400b1fe13a7","contributors":{"authors":[{"text":"Tack, Jason D. jtack@usgs.gov","contributorId":145460,"corporation":false,"usgs":true,"family":"Tack","given":"Jason D.","email":"jtack@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":564104,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fedy, Bradley C.","contributorId":40536,"corporation":false,"usgs":true,"family":"Fedy","given":"Bradley C.","affiliations":[],"preferred":false,"id":564105,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70148339,"text":"70148339 - 2015 - Terrain parameters of glide snow avalanches and a simple spatial glide snow avalanche model","interactions":[],"lastModifiedDate":"2020-10-29T20:20:51.220065","indexId":"70148339","displayToPublicDate":"2015-08-13T10:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1264,"text":"Cold Regions Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Terrain parameters of glide snow avalanches and a simple spatial glide snow avalanche model","docAbstract":"Glide snow avalanches are dangerous and difficult to predict. Despite substantial recent research there is still inadequate understanding regarding the controls of glide snow avalanche release. Glide snow avalanches often occur in similar terrain or the same locations annually, and repeat observations and prior work suggest that specific topography may be critical. Thus, to gain a better understanding of the terrain component of these types of avalanches we examined terrain parameters associated with the specific area of glide snow avalanche release in comparison to avalanche starting zones where no glide snow avalanches were observed (i.e. non-glide snow avalanche terrain).
Glide snow avalanche occurrences visible from the Going-to-the-Sun Road corridor in Glacier National Park, Montana from 2003 to 2013 are investigated using a database of all avalanche occurrences derived of daily observations each year from 1 April to 1 June. This yielded 192 glide snow avalanches in 53 distinct avalanche paths. Each avalanche was digitized in a GIS using satellite, oblique, and aerial imagery as reference. A set of 117 non-glide snow avalanche starting zones were also selected in this manner. These were start zones with avalanche activity potential, but without glide avalanches observed. Topographical parameters such as area, slope, aspect, curvature, potential incoming solar radiation, distance from ridge, and elevation were then derived for the entire dataset utilizing tools with a GIS and a 10 m DEM. Ground class and a glide factor were calculated using a four level classification index with in-situ observations and a land surface type layer in a GIS.
A total of 21 terrain variables were examined using a univariate analysis between areas where glide snow avalanches occurred and areas where glide snow avalanches were never observed, despite crack formation. Only two variables were not significantly different. The significantly different variables were then used to train a classification tree to distinguish between glide and non-glide snow avalanche terrain. A 10-fold cross validated tree resulted in four decision nodes to classify the data. The nodes split on glide factor, maximum slope angle, seasonal sum of incoming solar radiation, and maximum curvature to distinguish between glide snow avalanche and non-glide snow avalanche terrain with an unweighted average accuracy (RPC) of 0.95 and probability of detection of events (POD) of 0.99.
Finally, the results of the cross-validated tree were used in a GIS to examine other areas, not used in the training dataset of the classification tree, of potential glide snow avalanche release within Glacier National Park. Using this understanding of the role of topographic parameters on glide snow avalanche activity, a spatial terrain based model was developed to identify other areas with high glide snow avalanche potential outside of the immediate observation area. This simple spatial model correctly classified 78 percent of actual glide snow avalanche terrain (pixel count) of a small test area of four independent observed glide snow avalanches.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coldregions.2015.08.002","usgsCitation":"Peitzsch, E.H., Hendrikx, J., and Fagre, D.B., 2015, Terrain parameters of glide snow avalanches and a simple spatial glide snow avalanche model: Cold Regions Science and Technology, v. 120, p. 237-250, https://doi.org/10.1016/j.coldregions.2015.08.002.","productDescription":"14 p.","startPage":"237","endPage":"250","numberOfPages":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061113","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":301089,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.345703125,\n 48.23016176791893\n ],\n [\n -113.15093994140625,\n 48.23016176791893\n ],\n [\n -113.15093994140625,\n 48.980216985374994\n ],\n [\n -114.345703125,\n 48.980216985374994\n ],\n [\n -114.345703125,\n 48.23016176791893\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"120","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55780e29e4b032353cbeb6f1","contributors":{"authors":[{"text":"Peitzsch, Erich H. 0000-0001-7624-0455 epeitzsch@usgs.gov","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":3786,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","email":"epeitzsch@usgs.gov","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":547717,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hendrikx, Jordy 0000-0001-6194-3596","orcid":"https://orcid.org/0000-0001-6194-3596","contributorId":140954,"corporation":false,"usgs":false,"family":"Hendrikx","given":"Jordy","email":"","affiliations":[{"id":13628,"text":"Department of Earth Sciences, P.O. Box 173480, Montana State University, Bozeman, MT, USA. 59717.","active":true,"usgs":false}],"preferred":false,"id":547718,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fagre, Daniel B. 0000-0001-8552-9461 dan_fagre@usgs.gov","orcid":"https://orcid.org/0000-0001-8552-9461","contributorId":2036,"corporation":false,"usgs":true,"family":"Fagre","given":"Daniel","email":"dan_fagre@usgs.gov","middleInitial":"B.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":547719,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155830,"text":"sir20145167 - 2015 - Numerical simulation of groundwater flow, resource optimization, and potential effects of prolonged drought for the Citizen Potawatomi Nation Tribal Jurisdictional Area, central Oklahoma","interactions":[],"lastModifiedDate":"2018-02-05T15:03:57","indexId":"sir20145167","displayToPublicDate":"2015-08-13T10:30:00","publicationYear":"2015","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":"2014-5167","title":"Numerical simulation of groundwater flow, resource optimization, and potential effects of prolonged drought for the Citizen Potawatomi Nation Tribal Jurisdictional Area, central Oklahoma","docAbstract":"A hydrogeological study including two numerical groundwater-flow models was completed for the Citizen Potawatomi Nation Tribal Jurisdictional Area of central Oklahoma. One numerical groundwater-flow model, the Citizen Potawatomi Nation model, encompassed the jurisdictional area and was based on the results of a regional-scale hydrogeological study and numerical groundwater flow model of the Central Oklahoma aquifer, which had a geographic extent that included the Citizen Potawatomi Nation Tribal Jurisdictional Area. The Citizen Potawatomi Nation numerical groundwater-flow model included alluvial aquifers not in the original model and improved calibration using automated parameter-estimation techniques. The Citizen Potawatomi Nation numerical groundwater-flow model was used to analyze the groundwater-flow system and the effects of drought on the volume of groundwater in storage and streamflow in the North Canadian River. A more detailed, local-scale inset model was constructed from the Citizen Potawatomi Nation model to estimate available groundwater resources for two Citizen Potawatomi Nation economic development zones near the North Canadian River, the geothermal supply area and the Iron Horse Industrial Park.
\nGroundwater pumping rates at potential well locations were optimized using the most recent version of the U.S. Geological Survey Groundwater-Management Process for MODFLOW. The objectives of optimization were to determine if a total pumping rate of 500 gallons per minute could be pumped from 5 wells at the geothermal supply area and to maximize discharge from 16 wells at the Iron Horse Industrial Park without exceeding specified head drawdown constraints at the pumping wells and thus prevent groundwater depletion.
\nThe inset model was used to estimate North Canadian River streamflow depletion caused by optimized pumping at the Iron Horse Industrial Park because water quality was a concern, and the river may have degraded water quality compared to water in other parts of the alluvial aquifer. The fate of streamflow that infiltrates into groundwater because of pumping was not directly determined, but it was assumed that this water could end up in the well discharge, and was considered to be a maximum proportion of well discharge derived from the North Canadian River.
\nThe total optimized continuous pumping rate from five managed wells at the geothermal supply area was 638 gallons per minute, which exceeded the target pumping rate of 500 gallons per minute. The total continuous pumping rate from 16 wells at the Iron Horse Industrial Park was 1,472 gallons per minute, which induced stream infiltration of approximately 4.1 gallons per minute (approximately 0.3 percent of the total well discharge) from the North Canadian River.
\nTo estimate the effects of drought on water resources in the Citizen Potawatomi Nation Tribal Jurisdictional Area, a hypothetical 10-year drought during which precipitation would decrease by 50 percent was simulated by decreasing model groundwater recharge by the same proportion for the period 1990–2000 of the transient model. The effects of the drought were estimated by calculating the change in the volume of groundwater storage and groundwater flow to streams at the end of the drought period, and the change in simulated streamflow in the North Canadian River at the streamflow-gaging station at Shawnee, Okla., during and after the drought.
\nThe hypothetical decrease in recharge during the simulated drought caused groundwater in storage over the entire model in the study area to decrease by 361,500 acre-feet (14,100 acre-feet in the North Canadian River alluvial aquifer and 347,400 acre-feet in the Central Oklahoma aquifer), or approximately 0.2 percent of the total groundwater in storage over the drought period. This small percentage of groundwater loss showed that the Central Oklahoma aquifer as a bedrock aquifer has relatively low rates of recharge from the surface relative to the approximate storage. The budget for base flow to the North Canadian River indicated that the change in groundwater flow to the North Canadian River decreased during the 10-year drought by 386,500 acre-feet, or 37 percent. In all other parts of the Citizen Potawatomi Nation Tribal Jurisdictional Area, base flow decreased by 292,000 acre-feet, or 28 percent. Streamflow in the North Canadian River at the streamflow-gaging station at Shawnee, Okla., decreased during the hypothetical drought by as much as 28 percent, and the mean change in streamflow decreased as much as 16 percent. Streamflow at the Shawnee streamflow-gaging station did not recover to nondrought conditions until about 3 years after the simulated drought ended, during the relatively wet year of 2007.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145167","collaboration":"Prepared in cooperation with the Citizen Potawatomi Nation","usgsCitation":"Ryter, D.R., Kunkel, C.D., Peterson, S.M., and Traylor, J.P., 2018, Numerical simulation of groundwater flow, resource optimization, and potential effects of prolonged drought for the Citizen Potawatomi Nation Tribal Jurisdictional Area, central Oklahoma (ver. 1.2, February 2018), U.S. Geological Survey Scientific Investigations Report 2014–5167, 27 p., https://doi.org/10.3133/sir20145167.","productDescription":"viii, 27 p.","numberOfPages":"39","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058249","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":306619,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2014/5167/coverthb4.jpg"},{"id":350836,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5167/sir20145167.pdf","text":"Report","size":"1.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2014–5167"},{"id":350837,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2014/5167/versionHist_v1_2.txt","text":"Version History","size":"1.21 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2014–5167 Version History"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Citizen Potawatomi National Tribal Jurisdictional Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.30453491210938,\n 35.5802672918083\n ],\n [\n -97.28805541992188,\n 34.96137177524017\n ],\n [\n -97.25784301757812,\n 34.951241964789645\n ],\n [\n -97.17544555664062,\n 34.928726792983845\n ],\n [\n -97.08892822265625,\n 34.929852698375804\n ],\n [\n -97.00790405273438,\n 34.90620544067929\n ],\n [\n -96.93511962890624,\n 34.96812428662085\n ],\n [\n -96.86920166015625,\n 34.92760087214065\n ],\n [\n -96.82525634765625,\n 34.94448806230625\n ],\n [\n -96.79641723632812,\n 34.939985151560435\n ],\n [\n -96.79229736328125,\n 34.8971951696173\n ],\n [\n -96.75796508789062,\n 34.90057413710918\n ],\n [\n -96.734619140625,\n 34.86565143737161\n ],\n [\n -96.71539306640625,\n 34.858890491257824\n ],\n [\n -96.734619140625,\n 35.41479572901859\n ],\n [\n -96.7510986328125,\n 35.40360292969232\n ],\n [\n -96.78543090820312,\n 35.40696093270201\n ],\n [\n -96.8280029296875,\n 35.42374884923695\n ],\n [\n -96.866455078125,\n 35.431582013221295\n ],\n [\n -96.87332153320311,\n 35.39912537474419\n ],\n [\n -96.84997558593749,\n 35.37337460834958\n ],\n [\n -96.82662963867186,\n 35.36105611877928\n ],\n [\n -96.82937622070312,\n 35.345375322554474\n ],\n [\n -96.86508178710936,\n 35.35321610123821\n ],\n [\n -96.87744140625,\n 35.33641350071231\n ],\n [\n -96.8994140625,\n 35.31736632923788\n ],\n [\n -96.9598388671875,\n 35.33641350071231\n ],\n [\n -97.00790405273438,\n 35.36105611877928\n ],\n [\n -97.04086303710938,\n 35.394647571120544\n ],\n [\n -97.04635620117188,\n 35.428225036228106\n ],\n [\n -97.10678100585936,\n 35.460669951495305\n ],\n [\n -97.15072631835938,\n 35.47856499535729\n ],\n [\n -97.15896606445312,\n 35.49757411565533\n ],\n [\n -97.16720581054688,\n 35.4925427272974\n ],\n [\n -97.18231201171875,\n 35.50372316239306\n ],\n [\n -97.20634460449219,\n 35.50316417759601\n ],\n [\n -97.23381042480469,\n 35.51434313431818\n ],\n [\n -97.2454833984375,\n 35.53054985697859\n ],\n [\n -97.24891662597656,\n 35.55178129792757\n ],\n [\n -97.28118896484375,\n 35.58808520476325\n ],\n [\n -97.30316162109375,\n 35.595902354416566\n ],\n [\n -97.30453491210938,\n 35.5802672918083\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted August 13, 2015; Version 1.1: February 24, 2016; Version 1.2: February 5, 2018","contact":"Director, Oklahoma Water Science Center
U.S. Geological Survey
202 NW 66th, Bldg 7
Oklahoma City, OK 73116
http://ok.water.usgs.gov/
We developed models and provide computer code to make carcass recovery data more useful to wildlife managers. With these tools, wildlife managers can understand the spatial, temporal (e.g., across time periods, seasons), and demographic patterns in mortality causes from carcass recovery datasets. From datasets of radio-collared and non-collared carcasses, managers can calculate the detection bias by mortality cause in a non-collared carcass dataset compared to a collared carcass dataset. As a first step, we provide a standard procedure to assign mortality causes to carcasses. We provide an example of these methods for radio-collared wolves (n = 208) and non-collared wolves (n = 668) found dead in Wisconsin (1979–2012). We analyzed differences in mortality cause relative to season, age and sex classes, wolf harvest zones, and recovery phase (1979–1995: initial recovery, 1996–2002: early growth, 2003–2012: late growth). Seasonally, illegal kills and natural deaths were proportionally higher in winter (Oct–Mar) than summer (Apr–Sep) for collared wolves, whereas vehicle strikes and legal kills were higher in summer than winter. Spatially, more illegally killed collared wolves occurred in eastern wolf harvest zones where wolves reestablished more slowly and in the central forest region where optimal habitat is isolated by agriculture. Natural mortalities of collared wolves (e.g., disease, intraspecific strife, or starvation) were highest in western wolf harvest zones where wolves established earlier and existed at higher densities. Calculating detection bias in the non-collared dataset revealed that more than half of the non-collared carcasses on the landscape are not found. The lowest detection probabilities for non-collared carcasses (0.113–0.176) occurred in winter for natural, illegal, and unknown mortality causes.
","language":"English","publisher":"Wildlife Society","doi":"10.1002/jwmg.922","usgsCitation":"Stenglein, J.L., Van Deelen, T.R., Wydeven, A.P., Mladenoff, D.J., Wiedenhoft, J.E., Businga, N.K., Langenberg, J.A., Thomas, N.J., and Heisey, D.M., 2015, Mortality patterns and detection bias from carcass data: An example from wolf recovery in Wisconsin: Journal of Wildlife Management, v. 79, no. 7, p. 1173-1184, https://doi.org/10.1002/jwmg.922.","productDescription":"12 p.","startPage":"1173","endPage":"1184","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056314","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":306626,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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In this region, grassland dynamics are tightly linked to available moisture, yet it has proven difficult to resolve what aspects of climate drive vegetation change. In part, this is because it is unclear how heterogeneity in soils affects plant responses to climate. Here, we combine climate and soil properties with a mechanistic soil water model to explain temporal fluctuations in perennial grass cover, quantify where and the degree to which incorporating soil water dynamics enhances our ability to understand temporal patterns, and explore the potential consequences of climate change by assessing future trajectories of important climate and soil water variables. Our analyses focused on long-term (20–56 years) perennial grass dynamics across the Colorado Plateau, Sonoran, and Chihuahuan Desert regions. Our results suggest that climate variability has negative effects on grass cover, and that precipitation subsidies that extend growing seasons are beneficial. Soil water metrics, including the number of dry days and availability of water from deeper (>30 cm) soil layers, explained additional grass cover variability. While individual climate variables were ranked as more important in explaining grass cover, collectively soil water accounted for 40–60% of the total explained variance. Soil water conditions were more useful for understanding the responses of C3 than C4 grass species. Projections of water balance variables under climate change indicate that conditions that currently support perennial grasses will be less common in the future, and these altered conditions will be more pronounced in the Chihuahuan Desert and Colorado Plateau. We conclude that incorporating multiple aspects of climate and accounting for soil variability can improve our ability to understand patterns, identify areas of vulnerability, and predict the future of desert grasslands.
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lower Snake River reservoir","docAbstract":"Subyearling fall Chinook salmon (Oncorhynchus tshawytscha) in the Columbia River basin exhibit a transient rearing strategy and depend on connected shoreline habitats during freshwater rearing. Impoundment has greatly reduced the amount of shallow-water rearing habitat that is exacerbated by the steep topography of reservoirs. Periodic dredging creates opportunities to strategically place spoils to increase the amount of shallow-water habitat for subyearlings while at the same time reducing the amount of unsuitable area that is often preferred by predators. We assessed the amount and spatial arrangement of subyearling rearing habitat in Lower Granite Reservoir on the Snake River to guide future habitat improvement efforts. A spatially explicit habitat assessment was conducted using physical habitat data, two-dimensional hydrodynamic modelling and a statistical habitat model in a geographic information system framework. We used field collections of subyearlings and a common predator [smallmouth bass (Micropterus dolomieu)] to draw inferences about predation risk within specific habitat types. Most of the high-probability rearing habitat was located in the upper half of the reservoir where gently sloping landforms created low lateral bed slopes and shallow-water habitats. Only 29% of shorelines were predicted to be suitable (probability >0.5) for subyearlings, and the occurrence of these shorelines decreased in a downstream direction. The remaining, less suitable areas were composed of low-probability habitats in unmodified (25%) and riprapped shorelines (46%). As expected, most subyearlings were found in high-probability habitat, while most smallmouth bass were found in low-probability locations. However, some subyearlings were found in low-probability habitats, such as riprap, where predation risk could be high. Given their transient rearing strategy and dependence on shoreline habitats, subyearlings could benefit from habitat creation efforts in the lower reservoir where high-probability habitat is generally lacking. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.2934","usgsCitation":"Tiffan, K.F., Hatten, J.R., and Trachtenbarg, D.A., 2015, Assessing juvenile salmon rearing habitat and associated predation risk in a lower Snake River reservoir: River Research and Applications, v. 32, no. 5, p. 1030-1038, https://doi.org/10.1002/rra.2934.","productDescription":"9 p.","startPage":"1030","endPage":"1038","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064916","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":306672,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lower Snake river reservior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.69653320312499,\n 46.67582559793001\n ],\n [\n -117.53173828125,\n 46.66074749832068\n ],\n [\n -117.40264892578124,\n 46.6268063953552\n ],\n [\n -117.29553222656249,\n 46.53052428878426\n ],\n [\n -117.21313476562499,\n 46.411351502899215\n ],\n [\n -117.07305908203124,\n 46.39998810407942\n ],\n [\n -117.07855224609376,\n 46.34313560260196\n ],\n [\n -117.0098876953125,\n 46.28622391806706\n ],\n [\n -116.98242187499999,\n 46.214050815339526\n ],\n [\n -116.97418212890625,\n 46.11513371326539\n ],\n [\n -116.91375732421875,\n 46.128459837044915\n ],\n [\n -116.93847656250001,\n 46.26534147068603\n ],\n [\n -116.99615478515624,\n 46.36588370484979\n ],\n [\n -116.98242187499999,\n 46.40188216826328\n ],\n [\n -116.82861328125001,\n 46.430285240839964\n ],\n [\n -116.84234619140624,\n 46.464349400461124\n ],\n [\n -117.103271484375,\n 46.45110475854117\n ],\n [\n -117.26257324218749,\n 46.56452573114373\n ],\n [\n -117.3065185546875,\n 46.62492015414768\n ],\n [\n -117.3944091796875,\n 46.685247274319565\n ],\n [\n -117.46307373046874,\n 46.717268685073954\n ],\n [\n -117.69653320312499,\n 46.72291755083757\n ],\n [\n -117.69653320312499,\n 46.67582559793001\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-03","publicationStatus":"PW","scienceBaseUri":"55cdb1a7e4b08400b1fe139d","contributors":{"authors":[{"text":"Tiffan, Kenneth F. 0000-0002-5831-2846 ktiffan@usgs.gov","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":3200,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","email":"ktiffan@usgs.gov","middleInitial":"F.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":567529,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatten, James R. 0000-0003-4676-8093 jhatten@usgs.gov","orcid":"https://orcid.org/0000-0003-4676-8093","contributorId":3431,"corporation":false,"usgs":true,"family":"Hatten","given":"James","email":"jhatten@usgs.gov","middleInitial":"R.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":567530,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Trachtenbarg, David A","contributorId":146351,"corporation":false,"usgs":false,"family":"Trachtenbarg","given":"David","email":"","middleInitial":"A","affiliations":[{"id":16680,"text":"U.S. Army Corps of Engineers, Walla Walla District, Walla Walla, WA 99362","active":true,"usgs":false}],"preferred":false,"id":567531,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155509,"text":"sir20155106 - 2015 - Hydrologic budget and conditions of Permian, Pennsylvanian, and Mississippian aquifers in the Appalachian Plateaus physiographic province","interactions":[],"lastModifiedDate":"2015-10-26T14:28:11","indexId":"sir20155106","displayToPublicDate":"2015-08-12T15:45:00","publicationYear":"2015","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":"2015-5106","title":"Hydrologic budget and conditions of Permian, Pennsylvanian, and Mississippian aquifers in the Appalachian Plateaus physiographic province","docAbstract":"In response to challenges to groundwater availability posed by historic land-use practices, expanding development of hydrocarbon resources, and drought, the U.S. Geological Survey Groundwater Resources Program began a regional assessment of the Appalachian Plateaus aquifers in 2013 that incorporated a hydrologic landscape approach to estimate all components of the hydrologic system: surface runoff, base flow from groundwater, and interaction with atmospheric water (precipitation and evapotranspiration). This assessment was intended to complement other Federal and State investigations and provide foundational groundwater-related datasets in the Appalachian Plateaus.
\nA regional Soil-Water-Balance model was constructed for a 160,000-square-mile study area that extended to the topographic divide of all streams originating outside but flowing into areas underlain by Appalachian Plateaus aquifers. The model incorporated soil, landscape, and climate variables to estimate an annual water budget for the 32-year period from 1980 to 2011 and was calibrated using base-flow data estimated by hydrograph separation techniques from 20 streamflow gaging stations across the study area. Over this period, an average of 47 inches per year (in/yr) of precipitation fell on Appalachian Plateaus aquifers. Simulations from the regional Soil-Water-Balance model indicate that only 19 percent of the precipitation or an average 9 in/yr recharged aquifers, and 19 percent resulted in surface runoff to streams. The remaining 62 percent, an average of 27 in/yr of water, was returned to the atmosphere via evapotranspiration. Because withdrawals from aquifers due to pumping equated to less than 1 percent of the water budget, differences in predevelopment and postdevelopment regional water budgets of the Appalachian Plateaus were minimal. Storage changes caused by filling of abandoned coal-mine aquifers and long-term differences in aquifer storage resulting from climate fluctuations constitute a small portion of the overall water budget.
\nThe percentage of precipitation that results in recharge, runoff, or evapotranspiration from the landscape varies annually by up to a factor of two depending on temporal changes in prevailing climate conditions and spatial changes in basin characteristics, precipitation patterns, and sources of atmospheric moisture over a large study area. A comparison of water-budget estimates from the regional Soil-Water-Balance model for a dry year (1988) and wet year (2004) showed that evapotranspiration accounts for most of the annual differences in precipitation. As a portion of annual precipitation, evapotranspiration ranged from 69 percent (dry year) to 52 percent (wet year), a range four times greater than the 15 percent (dry year) to 18 percent (wet year) range estimated for recharge. Evapotranspiration as a percentage of precipitation peaks during dry periods, whereas base flow and runoff tend to reach minimum values. During wet periods, this relationship is reversed and base flow and runoff as a percentage of precipitation generally peak while evapotranspiration percentages reach minimum values. Annual recharge in the Appalachian Plateaus reaches a maximum at near 20 percent of annual precipitation, regardless of the severity of wet conditions.
\nHydrograph separation data from 849 streamflow gaging stations in the study area were used to assess trends in streamflow, base flow, surface runoff, and base-flow index, or ratio of base flow to streamflow, in the Appalachian Plateaus for the period from 1930 to 2011. Annual data anomalies for each of the four variables were individually defined as the annual standard deviation from the mean at all 849 streamflow gaging stations. Annual data anomalies confirm the close relation of annual precipitation to both base flow and runoff components of streamflow, and both components increased during the period of analysis. Around 1970, conditions shifted streamflow from values generally below to above long-term means. At a regional scale, increases in base flow account for most of these observed increases in mean annual streamflow. The independence of the base-flow index to annual climate trends indicate that changes in the components of streamflow of the Appalachian Plateaus are probably in response to shifts in seasonal precipitation or widespread land-use practices.
\nA subset of 77 index streamgages, defined as having 60 or more years of complete record between the years 1930 and 2011 with no more than 20 percent missing data, was selected to show spatial patterns of change in the water budget. Data from the index streamgages showed that the overall trends in base flow are dependent upon the period of evaluation. Long-term (1930–2011) increases in base flow were observed throughout the study area. For two shorter periods (1930–1969 and 1970–2011) trends in base flow were largely negative. In general, spatial patterns of change in streamflow, base flow, and runoff were mixed but generally consistent with prevailing climate patterns and land-use changes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155106","collaboration":"Groundwater Resources Program","usgsCitation":"McCoy, K.J., Yager, R.M., Nelms, D.L., Ladd, D.E., Monti, Jack, Jr., and Kozar, M.D., 2015, Hydrologic budget and conditions of Permian, Pennsylvanian, and Mississippian aquifers in the Appalachian Plateaus Physiographic Province (ver. 1.1, October 2015): U.S. Geological Survey Scientific Investigations Report 2015–5106, 77 p., https://dx.doi.org/10.3133/sir20155106.","productDescription":"vii, 77 p.","numberOfPages":"90","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060623","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":306582,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5106/sir20155106.pdf","text":"Report","size":"36.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5106"},{"id":306581,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5106/images/coverthb.jpg"},{"id":309929,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2015/5106/versionHist.txt","text":"October 26, 2015","size":"1.06 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2015-5106"}],"country":"United States","state":"Alabama, Kentucky, Maryland, Ohio, Pennslyvania, Virginia, Tennessee, West Virginia","otherGeospatial":"Mississippian aquifer, Pennsylvanian aquifer, Permian aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.31103515625,\n 41.705728515237524\n ],\n [\n -81.2109375,\n 41.83682786072714\n ],\n [\n -82.79296874999999,\n 41.36031866306708\n ],\n [\n -83.8037109375,\n 38.66835610151509\n ],\n [\n -86.98974609375,\n 34.97600151317591\n ],\n [\n -88.22021484375,\n 34.79576153473033\n ],\n [\n -88.39599609375,\n 32.62087018318113\n ],\n [\n -85.4736328125,\n 34.95799531086792\n ],\n [\n -83.3203125,\n 36.5978891330702\n ],\n [\n -80.22216796875,\n 37.474858084971046\n ],\n [\n -78.5302734375,\n 39.707186656826565\n ],\n [\n -76.31103515625,\n 41.705728515237524\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted August 13, 2015; Version 1.1: October 26, 2015","contact":"Director, Virginia Water Science Center
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This report addresses the standard operating procedures used by the U.S. Geological Survey’s Noble Gas Laboratory in Denver, Colorado, U.S.A., for the measurement of dissolved gases (methane, nitrogen, oxygen, and carbon dioxide) and noble gas isotopes (helium-3, helium-4, neon-20, neon-21, neon-22, argon-36, argon-38, argon-40, kryton-84, krypton-86, xenon-103, and xenon-132) dissolved in water. A synopsis of the instrumentation used, procedures followed, calibration practices, standards used, and a quality assurance and quality control program is presented. The report outlines the day-to-day operation of the Residual Gas Analyzer Model 200, Mass Analyzer Products Model 215–50, and ultralow vacuum extraction line along with the sample handling procedures, noble gas extraction and purification, instrument measurement procedures, instrumental data acquisition, and calculations for the conversion of raw data from the mass spectrometer into noble gas concentrations per unit mass of water analyzed. Techniques for the preparation of artificial dissolved gas standards are detailed and coupled to a quality assurance and quality control program to present the accuracy of the procedures used in the laboratory.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Water analysis in Book 5 Laboratory Analysis","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm5A11","usgsCitation":"Hunt, A.G., 2015, Noble Gas Laboratory’s standard operating procedures for the measurement of dissolved gas in water samples: U.S. Geological Survey Techniques and Methods, book 5, chap. A11, 22 p., https://dx.doi.org/10.3133/tm5A11.","productDescription":"vi, 21 p.","numberOfPages":"31","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-065997","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":306599,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/05/a11/coverthb.jpg"},{"id":306600,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/05/a11/tm5a11.pdf","text":"Report","size":"2.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 5-A11"}],"publicComments":"This report is Chapter 11 of Section A: Water analysis in Book 5 Laboratory Analysis.","contact":"Director, Crustal Geophysics and Geochemistry Science Center
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The North Platte Natural Resources District (NPNRD) has been actively collecting data and studying groundwater resources because of concerns about the future availability of the highly inter-connected surface-water and groundwater resources. This report, prepared by the U.S. Geological Survey in cooperation with the North Platte Natural Resources District, describes a groundwater-flow model of the North Platte River valley from Bridgeport, Nebraska, extending west to 6 miles into Wyoming. The model was built to improve the understanding of the interaction of surface-water and groundwater resources, and as an optimization tool, the model is able to analyze the effects of water-management options on the simulated stream base flow of the North Platte River. The groundwater system and related sources and sinks of water were simulated using a newton formulation of the U.S. Geological Survey modular three-dimensional groundwater model, referred to as MODFLOW–NWT, which provided an improved ability to solve nonlinear unconfined aquifer simulations with wetting and drying of cells. Using previously published aquifer-base-altitude contours in conjunction with newer test-hole and geophysical data, a new base-of-aquifer altitude map was generated because of the strong effect of the aquifer-base topography on groundwater-flow direction and magnitude. The largest inflow to groundwater is recharge originating from water leaking from canals, which is much larger than recharge originating from infiltration of precipitation. The largest component of groundwater discharge from the study area is to the North Platte River and its tributaries, with smaller amounts of discharge to evapotranspiration and groundwater withdrawals for irrigation. Recharge from infiltration of precipitation was estimated with a daily soil-water-balance model. Annual recharge from canal seepage was estimated using available records from the Bureau of Reclamation and then modified with canal-seepage potentials estimated using geophysical data. Groundwater withdrawals were estimated using land-cover data, precipitation data, and published crop water-use data. For fields irrigated with surface water and groundwater, surface-water deliveries were subtracted from the estimated net irrigation requirement, and groundwater withdrawal was assumed to be equal to any demand unmet by surface water.
\nThe groundwater-flow model was calibrated to measured groundwater levels and stream base flows estimated using the base-flow index method. The model was calibrated through automated adjustments using statistical techniques through parameter estimation using the parameter estimation suite of software (PEST). PEST was used to adjust 273 parameters, grouped as hydraulic conductivity of the aquifer, spatial multipliers to recharge, temporal multipliers to recharge, and two specific recharge parameters. Base flow of the North Platte River at Bridgeport, Nebraska, streamgage near the eastern, downstream end of the model was one of the primary calibration targets. Simulated base flow reasonably matched estimated base flow for this streamgage during 1950–2008, with an average difference of 15 percent. Overall, 1950–2008 simulated base flow followed the trend of the estimated base flow reasonably well, in cases with generally increasing or decreasing base flow from the start of the simulation to the end. Simulated base flow also matched estimated base flow reasonably well for most of the North Platte River tributaries with estimated base flow. Average simulated groundwater budgets during 1989–2008 were nearly three times larger for irrigation seasons than for non-irrigation seasons.
\nThe calibrated groundwater-flow model was used with the Groundwater-Management Process for the 2005 version of the U.S. Geological Survey modular three-dimensional groundwater model, MODFLOW–2005, to provide a tool for the NPNRD to better understand how water-management decisions could affect stream base flows of the North Platte River at Bridgeport, Nebr., streamgage in a future period from 2008 to 2019 under varying climatic conditions. The simulation-optimization model was constructed to analyze the maximum increase in simulated stream base flow that could be obtained with the minimum amount of reductions in groundwater withdrawals for irrigation. A second analysis extended the first to analyze the simulated base-flow benefit of groundwater withdrawals along with application of intentional recharge, that is, water from canals being released into rangeland areas with sandy soils. With optimized groundwater withdrawals and intentional recharge, the maximum simulated stream base flow was 15–23 cubic feet per second (ft3/s) greater than with no management at all, or 10–15 ft3/s larger than with managed groundwater withdrawals only. These results indicate not only the amount that simulated stream base flow can be increased by these management options, but also the locations where the management options provide the most or least benefit to the simulated stream base flow. For the analyses in this report, simulated base flow was best optimized by reductions in groundwater withdrawals north of the North Platte River and in the western half of the area. Intentional recharge sites selected by the optimization had a complex distribution but were more likely to be closer to the North Platte River or its tributaries. Future users of the simulation-optimization model will be able to modify the input files as to type, location, and timing of constraints, decision variables of groundwater withdrawals by zone, and other variables to explore other feasible management scenarios that may yield different increases in simulated future base flow of the North Platte River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155093","collaboration":"Prepared in cooperation with the North Platte Natural Resources District","usgsCitation":"Peterson, S.M, Flynn, A.T., Vrabel, Joseph, and Ryter, D.W., 2015, Simulation of groundwater flow and analysis of the effects of water-management options in the North Platte Natural Resources District, Nebraska: U.S. Geological Survey Scientific Investigations Report 2015–5093, 67 p., https://dx.doi.org/10.3133/sir20155093.","productDescription":"ix, 67 p.","numberOfPages":"82","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-055438","costCenters":[{"id":464,"text":"Nebraska Water Science 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U.S. Geological Survey
5231 South 19th Street
Lincoln, Nebraska 68512
http://ne.water.usgs.gov/
A forested wetland (swamp) is a forest where soils are saturated or flooded for at least a portion of the growing season, and vegetation, dominated by trees, is adapted to tolerate flooded conditions. A tidal freshwater forested wetland is a forested wetland that experiences frequent but short-term surface flooding via tidal action, with average salinity of soil porewater less than 0.5 g/l. It is known locally as tidal várzea in the Amazon delta, Brazil. A tidal saltwater forested wetland (mangrove forest) is a forested wetland that experiences frequent but short-term surface flooding via tidal action, with average salinity often exceeding 3 g/l and reaching levels that can exceed seawater. Mangrove ecosystems are composed of facultative halophytes that generally experience better growth at moderate salinity concentrations.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Encyclopedia of Estuaries","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-94-017-8801-4_387","usgsCitation":"Duberstein, J., and Krauss, K.W., 2015, Forested wetland habitat, chap. of Encyclopedia of Estuaries, p. 334-338, https://doi.org/10.1007/978-94-017-8801-4_387.","productDescription":"5 p.","startPage":"334","endPage":"338","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-046271","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":322394,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-12","publicationStatus":"PW","scienceBaseUri":"57569eb1e4b023b96ec28458","contributors":{"editors":[{"text":"Kennish, Michael J.","contributorId":111903,"corporation":false,"usgs":true,"family":"Kennish","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":633494,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Duberstein, Jamie A.","contributorId":91007,"corporation":false,"usgs":false,"family":"Duberstein","given":"Jamie A.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":577755,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krauss, Ken W. 0000-0003-2195-0729 kraussk@usgs.gov","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":2017,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","email":"kraussk@usgs.gov","middleInitial":"W.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":577754,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70148090,"text":"sir20155075 - 2015 - Median nitrate concentrations in groundwater in the New Jersey Highlands Region estimated using regression models and land-surface characteristics","interactions":[],"lastModifiedDate":"2015-09-03T13:16:16","indexId":"sir20155075","displayToPublicDate":"2015-08-12T00:00:00","publicationYear":"2015","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":"2015-5075","title":"Median nitrate concentrations in groundwater in the New Jersey Highlands Region estimated using regression models and land-surface characteristics","docAbstract":"Nitrate-concentration data are used in conjunction with land-use and land-cover data to estimate median nitrate concentrations in groundwater underlying the New Jersey (NJ) Highlands Region. Sources of data on nitrate in 19,670 groundwater samples are from the U.S. Geological Survey (USGS) National Water Information System (NWIS) and the NJ Private Well Testing Act (PWTA).
\nIn a study conducted by the USGS, in cooperation with the New Jersey Department of Environmental Protection, logistic regression was used to relate measured nitrate concentrations to five explanatory variables (percent urban and agricultural land use, septic-system density, total length of streams, and number of known contaminated sites) quantified in 610-meter-square grid cells. A method for calculating the median concentrations of nitrate from a series of logistic regression models was developed. Two calibration and two validation procedures showed that the logistic-regression-based method can estimate groundwater-nitrate concentrations in the Highlands Region accurately to within 0.1 milligram per liter as nitrogen (mg/L as N). Limitations of the logistic-regression-based method include the inability to select a logistic model with exactly 0.5 probability of exceeding the threshold value and lack of an algorithm to directly calculate the median value. Quantile regression was evaluated as a suitable alternative and was slightly less accurate than the logistic-regression method in estimating median groundwater nitrate concentrations in the Highlands Region.
\nMultiple-linear regression with log-transformed nitrate-concentration data and the same five explanatory values was less accurate than either logistic or quantile regression in estimating median nitrate concentrations. On the basis of 4,516 2000 x 2000 foot grid cells that contain wells with data stored in NWIS and the PWTA database, the estimated median nitrate concentration for the entire Highlands Region is about 1.25 mg/L as N, and estimated median concentrations range from about 1.05 to 1.78 mg/L as N among 11 smaller administratively defined areas within the Highlands Region that vary in percentages of urban land use, agricultural land use, and septic-system density.
\nThe Kaplan-Meier method of estimating summary statistics from left-censored data was applied in order to include nondetects (left-censored data) in median nitrate-concentration calculations. Median concentrations also were determined using three alternative methods of handling nondetects. Treatment of the 23 percent of samples that were nondetects had little effect on estimated median nitrate concentrations because method detection limits were mostly less than median values.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155075","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Baker, R.J., Chepiga, M., and Cauller, S.J., 2015, Median nitrate concentrations in groundwater in the New Jersey Highlands Region estimated using regression models and land-surface characteristics: U.S. Geological Survey Scientific Investigations Report 2015-5075, Report: vii, 26 p.; 2 Appendices, https://doi.org/10.3133/sir20155075.","productDescription":"Report: vii, 26 p.; 2 Appendices","numberOfPages":"39","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-060925","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":305639,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5075/support/sir20155075_appendix01.xlsx","text":"Appendix 1","size":"17.2 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015-5075 Appendix 1","linkHelpText":"Example spreadsheet for calculating median nitrate concentrations with logistic-regression models"},{"id":305637,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2015/5075/"},{"id":305638,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5075/support/sir20155075.pdf","text":"Report","size":"16.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5075 Report"},{"id":305640,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5075/support/sir20155075_appendix02.pdf","text":"Appendix 2","size":"180 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5075 Appendix 2","linkHelpText":"Geographic and environmental characteristics evaluated as possible explanatory variables in models of median nitrate concentrations in groundwater in the NJ Highlands Region"},{"id":305641,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20155075.jpg"}],"projection":"Universal Transverse Mercator projection","country":"United States","state":"New Jersey","otherGeospatial":"New Jersey Highlands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.0531005859375,\n 40.86783384138491\n ],\n [\n -74.9212646484375,\n 41.03793062246529\n ],\n [\n -74.805908203125,\n 41.03793062246529\n ],\n [\n -74.5697021484375,\n 41.290189955885644\n ],\n [\n -74.2236328125,\n 41.13315883477399\n ],\n [\n -74.3115234375,\n 40.94671366508002\n ],\n [\n -74.4049072265625,\n 40.77638178482896\n ],\n [\n -74.5806884765625,\n 40.643135583312805\n ],\n [\n -74.72351074218749,\n 40.51379915504413\n ],\n [\n -74.9212646484375,\n 40.47202439692057\n ],\n [\n -75.08056640625,\n 40.44694705960048\n ],\n [\n -75.069580078125,\n 40.53050177574321\n ],\n [\n -75.146484375,\n 40.56389453066509\n ],\n [\n -75.1904296875,\n 40.538851525354666\n ],\n [\n -75.2288818359375,\n 40.62646106367355\n ],\n [\n -75.234375,\n 40.693134153308094\n ],\n [\n -75.223388671875,\n 40.751418432997454\n ],\n [\n -75.16845703124999,\n 40.78885994449482\n ],\n [\n -75.135498046875,\n 40.772221877329024\n ],\n [\n -75.091552734375,\n 40.82212357516945\n ],\n [\n -75.1190185546875,\n 40.863679665481676\n ],\n [\n -75.0531005859375,\n 40.86783384138491\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"560bb6cbe4b058f706e53d66","contributors":{"authors":[{"text":"Baker, Ronald J. rbaker@usgs.gov","contributorId":1436,"corporation":false,"usgs":true,"family":"Baker","given":"Ronald","email":"rbaker@usgs.gov","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":547539,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chepiga, Mary M. mchepiga@usgs.gov","contributorId":888,"corporation":false,"usgs":true,"family":"Chepiga","given":"Mary M.","email":"mchepiga@usgs.gov","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":false,"id":547540,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cauller, Stephen J. 0000-0002-1823-8813 sjcaulle@usgs.gov","orcid":"https://orcid.org/0000-0002-1823-8813","contributorId":3641,"corporation":false,"usgs":true,"family":"Cauller","given":"Stephen","email":"sjcaulle@usgs.gov","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":false,"id":564557,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155838,"text":"70155838 - 2015 - Transdisciplinary science: A path to understanding the interactions among ocean acidification, ecosystems, and society","interactions":[],"lastModifiedDate":"2026-01-26T14:24:49.438181","indexId":"70155838","displayToPublicDate":"2015-08-11T16:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2929,"text":"Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Transdisciplinary science: A path to understanding the interactions among ocean acidification, ecosystems, and society","docAbstract":"The global nature of ocean acidification (OA) transcends habitats, ecosystems, regions, and science disciplines. The scientific community recognizes that the biggest challenge in improving understanding of how changing OA conditions affect ecosystems, and associated consequences for human society, requires integration of experimental, observational, and modeling approaches from many disciplines over a wide range of temporal and spatial scales. Such transdisciplinary science is the next step in providing relevant, meaningful results and optimal guidance to policymakers and coastal managers. We discuss the challenges associated with integrating ocean acidification science across funding agencies, institutions, disciplines, topical areas, and regions, and the value of unifying science objectives and activities to deliver insights into local, regional, and global scale impacts. We identify guiding principles and strategies for developing transdisciplinary research in the ocean acidification science community.
","language":"English","publisher":"Oceanography Society","publisherLocation":"Rockville, MD","doi":"10.5670/oceanog.2015.43","usgsCitation":"Yates, K.K., Turley, C., Hopkinson, B.M., Todgham, A.E., Cross, J.N., Greening, H., Williamson, P., Van Hooidonk, R., Deheyn, D.D., and Johnson, Z., 2015, Transdisciplinary science: A path to understanding the interactions among ocean acidification, ecosystems, and society: Oceanography, v. 28, no. 2, p. 212-225, https://doi.org/10.5670/oceanog.2015.43.","productDescription":"i, 14 p.","startPage":"212","endPage":"225","numberOfPages":"15","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060179","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":471885,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5670/oceanog.2015.43","text":"Publisher Index Page"},{"id":306587,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"2","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55cb0ea4e4b08400b1fdd45d","contributors":{"authors":[{"text":"Yates, Kimberly K. 0000-0001-8764-0358 kyates@usgs.gov","orcid":"https://orcid.org/0000-0001-8764-0358","contributorId":420,"corporation":false,"usgs":true,"family":"Yates","given":"Kimberly","email":"kyates@usgs.gov","middleInitial":"K.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":566557,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Turley, Carol","contributorId":146189,"corporation":false,"usgs":false,"family":"Turley","given":"Carol","email":"","affiliations":[{"id":16618,"text":"Plymouth Marine Laboratory - UK","active":true,"usgs":false}],"preferred":false,"id":566558,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hopkinson, Brian M.","contributorId":146190,"corporation":false,"usgs":false,"family":"Hopkinson","given":"Brian","email":"","middleInitial":"M.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":566559,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Todgham, Anne E.","contributorId":146191,"corporation":false,"usgs":false,"family":"Todgham","given":"Anne","email":"","middleInitial":"E.","affiliations":[{"id":6690,"text":"San Francisco State University","active":true,"usgs":false}],"preferred":false,"id":566560,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cross, Jessica N.","contributorId":146192,"corporation":false,"usgs":false,"family":"Cross","given":"Jessica","email":"","middleInitial":"N.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":566562,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Greening, Holly","contributorId":64299,"corporation":false,"usgs":true,"family":"Greening","given":"Holly","email":"","affiliations":[],"preferred":false,"id":566561,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Williamson, Phillip","contributorId":146188,"corporation":false,"usgs":false,"family":"Williamson","given":"Phillip","email":"","affiliations":[{"id":16617,"text":"University of East Anglia","active":true,"usgs":false}],"preferred":false,"id":566564,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Van Hooidonk, Ruben","contributorId":146193,"corporation":false,"usgs":false,"family":"Van Hooidonk","given":"Ruben","email":"","affiliations":[{"id":12641,"text":"NOAA NMFS","active":true,"usgs":false}],"preferred":false,"id":566563,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Deheyn, Dimitri D.","contributorId":146194,"corporation":false,"usgs":false,"family":"Deheyn","given":"Dimitri","email":"","middleInitial":"D.","affiliations":[{"id":16619,"text":"UCSD","active":true,"usgs":false}],"preferred":false,"id":566565,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Johnson, Zackary","contributorId":229396,"corporation":false,"usgs":false,"family":"Johnson","given":"Zackary","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":954394,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70155836,"text":"70155836 - 2015 - How well can wave runup be predicted? comment on Laudier et al. (2011) and Stockdon et al. (2006)","interactions":[],"lastModifiedDate":"2015-08-11T15:55:32","indexId":"70155836","displayToPublicDate":"2015-08-11T16:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"How well can wave runup be predicted? comment on Laudier et al. (2011) and Stockdon et al. (2006)","docAbstract":"Laudier et al. (2011) suggested that there may be a systematic bias error in runup predictions using a model developed by Stockdon et al. (2006). Laudier et al. tested cases that sampled beach and wave conditions that differed from those used to develop the Stockdon et al. model. Based on our re-analysis, we found that in two of the three Laudier et al. cases observed overtopping was actually consistent with the Stockdon et al. predictions. In these cases, the revised predictions indicated substantial overtopping with, in one case, a freeboard deficit of 1 m. In the third case, the revised prediction had a low likelihood of overtopping, which reflected a large uncertainty due to wave conditions that included a broad and bi-modal frequency distribution. The discrepancy between Laudier et al. results and our re-analysis appear to be due, in part, to simplifications made by Laudier et al. when they implemented a reduced version of the Stockdon et al. model.
","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.coastaleng.2015.05.001","usgsCitation":"Plant, N.G., and Stockdon, H.F., 2015, How well can wave runup be predicted? comment on Laudier et al. (2011) and Stockdon et al. (2006): Coastal Engineering, v. 102, no. 2015, p. 44-48, https://doi.org/10.1016/j.coastaleng.2015.05.001.","productDescription":"5 p.","startPage":"44","endPage":"48","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065141","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":471886,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coastaleng.2015.05.001","text":"Publisher Index Page"},{"id":306590,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"102","issue":"2015","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55cb0ea3e4b08400b1fdd455","chorus":{"doi":"10.1016/j.coastaleng.2015.05.001","url":"http://dx.doi.org/10.1016/j.coastaleng.2015.05.001","publisher":"Elsevier BV","authors":"Plant Nathaniel G., Stockdon Hilary F.","journalName":"Coastal Engineering","publicationDate":"8/2015","auditedOn":"7/24/2015"},"contributors":{"authors":[{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":566551,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stockdon, Hilary F. 0000-0003-0791-4676 hstockdon@usgs.gov","orcid":"https://orcid.org/0000-0003-0791-4676","contributorId":2153,"corporation":false,"usgs":true,"family":"Stockdon","given":"Hilary","email":"hstockdon@usgs.gov","middleInitial":"F.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":566552,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70155520,"text":"70155520 - 2015 - Earthquake shaking hazard estimates and exposure changes in the conterminous United States","interactions":[],"lastModifiedDate":"2016-06-29T13:20:28","indexId":"70155520","displayToPublicDate":"2015-08-11T13:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Earthquake shaking hazard estimates and exposure changes in the conterminous United States","docAbstract":"A large portion of the population of the United States lives in areas vulnerable to earthquake hazards. This investigation aims to quantify population and infrastructure exposure within the conterminous U.S. that are subjected to varying levels of earthquake ground motions by systematically analyzing the last four cycles of the U.S. Geological Survey's (USGS) National Seismic Hazard Models (published in 1996, 2002, 2008 and 2014). Using the 2013 LandScan data, we estimate the numbers of people who are exposed to potentially damaging ground motions (peak ground accelerations at or above 0.1g). At least 28 million (~9% of the total population) may experience 0.1g level of shaking at relatively frequent intervals (annual rate of 1 in 72 years or 50% probability of exceedance (PE) in 50 years), 57 million (~18% of the total population) may experience this level of shaking at moderately frequent intervals (annual rate of 1 in 475 years or 10% PE in 50 years), and 143 million (~46% of the total population) may experience such shaking at relatively infrequent intervals (annual rate of 1 in 2,475 years or 2% PE in 50 years). We also show that there is a significant number of critical infrastructure facilities located in high earthquake-hazard areas (Modified Mercalli Intensity ≥ VII with moderately frequent recurrence interval).
","language":"English","publisher":"The Earthquake Engineering Research Institute","publisherLocation":"Berkeley, CA","doi":"10.1193/111814EQS195M","usgsCitation":"Jaiswal, K., Petersen, M.D., Rukstales, K.S., and Leith, W.S., 2015, Earthquake shaking hazard estimates and exposure changes in the conterminous United States: Earthquake Spectra, v. 31, no. 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rukstales@usgs.gov","orcid":"https://orcid.org/0000-0003-2818-078X","contributorId":775,"corporation":false,"usgs":true,"family":"Rukstales","given":"Kenneth","email":"rukstales@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":565672,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leith, William S. 0000-0002-3463-3119 wleith@usgs.gov","orcid":"https://orcid.org/0000-0002-3463-3119","contributorId":2248,"corporation":false,"usgs":true,"family":"Leith","given":"William","email":"wleith@usgs.gov","middleInitial":"S.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":565673,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70160796,"text":"70160796 - 2015 - Behavioral evidence for a role of chemoreception during reproduction in lake trout","interactions":[],"lastModifiedDate":"2015-12-30T16:04:50","indexId":"70160796","displayToPublicDate":"2015-08-10T17:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Behavioral evidence for a role of chemoreception during reproduction in lake trout","docAbstract":"Chemoreception is hypothesized to influence spawning site selection, mate search, and synchronization of gamete release in chars (Salvelinus spp.), but behavioral evidence is generally lacking. Here, we provide a survey of the behavioral responses of reproductive male and female lake trout (Salvelinus namaycush) to natural conspecific chemosensory stimuli. A flow-through laboratory assay with side-by-side artificial spawning reefs was used to evaluate behavioral preferences of spawning-phase males and females for chemosensory stimuli from juveniles and from spawning-phase males and females. Males and females preferred male and juvenile stimuli over no stimuli, but only had weak preferences for female stimuli. Only females had a preference for male over juvenile stimuli when given a direct choice between the two. The unexpected observation of male attraction to male stimuli, even when offered female stimuli, indicates a fundamental difference from the existing models of chemical communication in fishes. We discuss our results from the perspectives of prespawning aggregation, mate evaluation, and spawning synchronization. Identification of specific components of the stimuli will allow confirmation of the function and may have management implications for native and invasive populations of lake trout that are ecologically and economically important.
","language":"English","publisher":"National Research Council Canada","publisherLocation":"Ottawa","doi":"10.1139/cjfas-2015-0351","collaboration":"Tyler J. Buchinger; Weiming Li","usgsCitation":"Buchinger, T.J., Li, W., and Johnson, N., 2015, Behavioral evidence for a role of chemoreception during reproduction in lake trout: Canadian Journal of Fisheries and Aquatic Sciences, v. 72, no. 12, p. 1847-1852, https://doi.org/10.1139/cjfas-2015-0351.","productDescription":"6 p.","startPage":"1847","endPage":"1852","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067693","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":313092,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"72","issue":"12","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56850e57e4b0a04ef49337f5","contributors":{"authors":[{"text":"Buchinger, Tyler J.","contributorId":40508,"corporation":false,"usgs":true,"family":"Buchinger","given":"Tyler","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":583937,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Li, Weiming","contributorId":126748,"corporation":false,"usgs":false,"family":"Li","given":"Weiming","email":"","affiliations":[{"id":6590,"text":"Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":583938,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":150983,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas S.","email":"njohnson@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":583936,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70148270,"text":"ds942 - 2015 - Geochemical, modal, and geochronologic data for 1.4 Ga A-type granitoid intrusions of the conterminous United States","interactions":[],"lastModifiedDate":"2016-06-29T13:23:03","indexId":"ds942","displayToPublicDate":"2015-08-10T11:45:00","publicationYear":"2015","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":"942","title":"Geochemical, modal, and geochronologic data for 1.4 Ga A-type granitoid intrusions of the conterminous United States","docAbstract":"The purpose of this report is to present available geochemical, modal, and geochronologic data for approximately 1.4 billion year (Ga) A-type granitoid intrusions of the United States and to make those data available to ongoing petrogenetic investigations of these rocks. A-type granites, as originally defined by Loiselle and Wones (1979), are iron-enriched granitoids (synonymous with the ferroan granitoids of Frost and Frost, 2011) that occur in an anorogenic, within-continent setting. Relative to other granitic rocks, A-type granites have high FeO*/(FeO*+MgO), high K2O and K2O/Na2O, are metaluminous to weakly peraluminous, and are enriched in incompatible trace elements. Loiselle and Wones (1979) further suggested that A-type granites are relatively anhydrous. Anderson (1983) provides an early compilation of data for the products of 1.4 Ga magmatism in North America and notes the spatial and temporal association of a trio of rock types, which includes gabbro to anorthosite, intermediate composition mangerite, and granitic rapakivi rocks. In North America, the majority of known A-type intrusions were emplaced between 1.5 and 1.3 Ga and are predominantly of the granitic variety (Anderson, 1983).
\nThis report addresses the broadly Mesoproterozoic-age granitic rocks of the conterminous United States. Constituents of this group of intrusive rocks were defined using a variety of spatial, compositional, and geochronologic metrics. Thomas and others (2012) provided an updated synthesis, largely based on new isotopic and geochronologic data (for example, Fisher and others, 2010), for the large-scale geologic and tectonic evolution of the eastern United States. Their findings suggest that the basement rocks of the central and southern Appalachian region are allochthonous relative to the remainder of Laurentia and were accreted along the Grenville front between 1.25 and 1.0 Ga. Accordingly, Mesoproterozoic rocks east of the Grenville front and south of the approximate latitude of New York City do not represent North American magmatism. Consequently, geochemical, modal, and geochronologic data for these rocks are not included in the compilation described herein. Further, the structural styles and compositions of granitoid rocks east of the Grenville front, mostly highly deformed gneissic rocks, are dissimilar to those characteristic of the A-type granitoid rocks described herein.
\nA variety of compositional and age information further characterizes the 1.4 Ga A-type granitoid rocks in the conterminous United States. Most samples included in this compilation have felsic compositions, although some extend to intermediate compositions. SiO2 contents range from 56 to almost 78 weight percent, and median and mean SiO2 contents are 72.0 and 71.1 weight percent, respectively. The majority of these rocks for which modal data are available are composed of monzogranite (Streckeisen, 1976), although the dataset also contains many samples composed of granodiorite and syenogranite. A smaller group of the granitoid rocks in this dataset are composed of quartz monzodiorite and quartz monzonite, and a very small subset of samples is composed of alkali-feldspar granite, tonalite, alkali-feldspar quartz syenite, and quartz syenite (fig. 1). Many of the 1.4 Ga granitoid rocks are further characterized by medium- to coarse-grain size and are also conspicuously porphyritic; alkali feldspar phenocrysts or megacrysts (2–10 cm), often with rapakivi overgrowths, are a common feature of many of these rocks (Anderson, 1983; Anderson and Bender, 1989; Anderson and Cullers, 1978; Condie and Budding, 1979). The age of A-type magmatism in North America ranges from about 1.8 to 1.0 Ga, although Anderson (1983) suggests that more than 70 percent (by volume) of A-type magmatism in this region occurred between 1.49 and 1.41 Ga. In the conterminous United States, ages of A-type granitoid rocks are restricted to the period between about 1.49 and 1.33 Ga (Anderson, 1983; Bauer and Pollock, 1993; Bickford and Mose, 1975; Bickford, Harrower, and others, 1981; Bickford and others, 1989; Dewane and Van Schmus, 2007; Hoppe and others, 1983; Peterman and Hedge, 1968; Van Schmus and Bickford, 1981; Van Schmus and others, 1975). Using these recognition criteria, we identified A-type granitoid intrusions of the conterminous United States; for those intrusions, we compiled available geochemical, modal, isotopic (Sr and Nd) and geochronologic data for inclusion in the databases described herein.
\nThe significance of 1.4 Ga granitoid rocks relative to the geologic evolution of the conterminous United States remains unclear, despite Anderson’s (1983) compilation and synthesis of compositional data pertinent to these rocks. The large-volume magmatic events indicated by these rocks, as well as their broad geographic distribution, tectonic significance, and association with mineral deposits, underscore their importance. The broad distribution of these rocks, from the northern mid-continent to the southwestern United States (in New Mexico, Arizona, California, and southernmost Nevada), throughout the Rocky Mountains in New Mexico and Colorado (and sporadically in southern Wyoming and central Idaho), and beneath much of the Plains region (as indicated by drilling), has led to the large-scale tectonic and magmatic processes responsible for genesis of the associated magmas being actively studied.
\nIn addition, Kisvarsanyi (1972) suggests that iron-copper deposits in the St. Francois Mountains of southeastern Missouri are petrogenetically associated with 1.4 Ga A-type granitoids that occur in that region. Similarly, Dall’Agnol and others (2012) summarize important global associations between A-type granitoid rocks and a variety of important ore deposit types, particularly tin, high-field-strength elements (Zr, Hf, Nb, Ta), rare-earth elements, and iron oxide-copper-gold deposits. Consequently, the need to better understand relations between A-type granitoid rocks, tectonic setting, and magma petrogenesis, as well as their genetic associations with important types of ore deposits, suggests that developing a definitive geochemical, modal, and geochronologic database for these rocks in the conterminous United States is of considerable value.
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U.S. Geological Survey
Box 25046, Mail Stop 973
Denver, CO 80225
http://minerals.cr.usgs.gov/
This report, prepared for the National Earthquake Prediction Evaluation Council (NEPEC), is intended as a step toward improving communications about earthquake hazards between information providers and users who coordinate emergency-response activities in the Cascadia region of the Pacific Northwest. NEPEC charged a subcommittee of scientists with writing this report about forewarnings of increased probabilities of a damaging earthquake. We begin by clarifying some terminology; a “prediction” refers to a deterministic statement that a particular future earthquake will or will not occur. In contrast to the 0- or 100-percent likelihood of a deterministic prediction, a “forecast” describes the probability of an earthquake occurring, which may range from >0 to <100 percent. When the time window is short (days to months) and the forecast is formulated for operational utility, this term may be “operational earthquake forecasting.” The subcommittee considered short-term forecasts only, herein referred to as “forewarnings,” but not their formulation into messages or their applications, which will be addressed by NEPEC in subsequent activities.
The subcommittee considered “direct” and “indirect” forewarnings. Direct forewarnings originate with observed changes in geologic processes or conditions, which may include
Indirect forewarnings are based largely on model predictions of increased earthquake-occurrence probabilities. In this context, “models” refers to simulations of the processes believed to affect earthquake occurrence, as implemented in computer software, laboratory experiments, or some analog natural system. These indirect forewarnings likely will be more uncertain and difficult to interpret than direct forewarnings. This report also highlights the challenges of assessing the significance of forewarnings, which mostly will be extraordinary events with little or no historical precedent in the Cascadia region.
\n","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151151","usgsCitation":"Gomberg, J., Atwater, B., Beeler, N., Bodin, P., Davis, E., Frankel, A., Hayes, G., McConnell, V., Melbourne, T., Oppenheimer, D., Parrish, J., Roeloffs, E., Rogers, G., Sherrod, B., Vidale, J., Walsh, T., Weaver, C., and Whitmore, P., 2015, Earthquake forewarning in the Cascadia region: U.S. Geological Survey Open-File Report 2015–1151, 8 p., https://dx.doi.org/10.3133/ofr20151151.","productDescription":"iii, 8 p.","numberOfPages":"11","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-064402","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":306462,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1151/coverthb.gif"},{"id":306463,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1151/ofr20151151.pdf","text":"Report","size":"230 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1151"}],"country":"Canada, United States","otherGeospatial":"Cascadia region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.0576171875,\n 40.58058466412764\n ],\n [\n -122.4755859375,\n 41.1455697310095\n ],\n [\n -121.37695312499999,\n 42.00032514831621\n ],\n [\n -120.14648437499999,\n 42.553080288955826\n ],\n [\n -117.72949218749999,\n 42.16340342422401\n ],\n [\n -116.630859375,\n 41.83682786072714\n ],\n [\n -114.78515624999999,\n 41.672911819602085\n ],\n [\n -111.181640625,\n 42.00032514831621\n ],\n [\n -109.6875,\n 42.52069952914966\n ],\n [\n -109.9072265625,\n 43.51668853502909\n ],\n [\n -111.0498046875,\n 44.59046718130883\n ],\n [\n -113.37890625,\n 45.02695045318546\n ],\n [\n -112.8076171875,\n 45.9511496866914\n ],\n [\n -113.4228515625,\n 46.800059446787316\n ],\n [\n -116.45507812500001,\n 49.724479188713005\n ],\n [\n -118.21289062499999,\n 52.64306343665892\n ],\n [\n -121.025390625,\n 54.41892996865827\n ],\n [\n -129.7265625,\n 58.63121664342478\n ],\n [\n -134.912109375,\n 59.977005492196\n ],\n [\n -137.373046875,\n 60.15244221438077\n ],\n [\n -139.21874999999997,\n 60.37042901631508\n ],\n [\n -141.064453125,\n 60.413852350464914\n ],\n [\n -141.591796875,\n 59.88893689676585\n ],\n [\n -140.09765625,\n 59.66774058164963\n ],\n [\n -138.25195312499997,\n 58.99531118795094\n ],\n [\n -135.17578125,\n 56.36525013685606\n ],\n [\n -132.451171875,\n 52.32191088594773\n ],\n [\n -129.0234375,\n 50.62507306341435\n ],\n [\n -125.94726562499999,\n 49.095452162534826\n ],\n [\n -124.365234375,\n 48.04870994288686\n ],\n [\n -124.27734374999999,\n 44.84029065139799\n ],\n [\n -124.71679687499999,\n 42.81152174509788\n ],\n [\n -124.0576171875,\n 40.58058466412764\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"
Earthquake Science Center—Seattle, Washington Field Office
\nU.S. Geological Survey
\nDept. Earth & Space Sciences
\nUniversity of Washington, Box 351310
\nSeattle, WA 98195-1310
\n","tableOfContents":"Isolated patches of native vegetation in human-modified landscapes are important reservoirs of biological diversity because they may be the only places in which rare or native species can persist. Manassas National Battlefield Park, Virginia, is an island embedded in a matrix of intensively modified lands; it is becoming increasingly isolated due to growth of the greater Washington, D.C. area. A series of cliffs along Bull Run support an eastern white pine community disjunct from its more typical range in the Appalachian Mountains. Cliffs frequently support vegetation communities that differ from surrounding habitat. In this ecological context, the cliffs along Bull Run are islands of specialized habitat within an island of natural and semi-natural communities (the park), surrounded by a human-dominated landscape. A floral survey of these cliffs was a top priority identified by the National Park Service National Capital Region via the National Resource Preservation Program; in 2014, we completed a floral survey of 11 cliffs in the park. We recorded 282 species in 194 genera and 83 families, including 23 newly documented species for the park.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds940","usgsCitation":"Stroh, E.D., Struckhoff, M.A., and Grabner, K.W., 2015, A floral survey of cliff habitats along Bull Run at Manassas National Battlefield Park, Virginia, 2014: U.S. Geological Survey Data Series 940, 20 p., https://dx.doi.org/10.3133/ds940.","productDescription":"Report: iv, 20 p.; Database; ReadMe","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2014-01-01","ipdsId":"IP-063929","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":306469,"rank":4,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/0940/ReadMe.txt","text":"ReadMe File","size":"5.47 kB","linkFileType":{"id":2,"text":"txt"}},{"id":306409,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0940/coverthb.jpg"},{"id":306410,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0940/ds940.pdf","text":"Report","size":"1.53 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 940"},{"id":306411,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://dx.doi.org/10.5066/F7NS0RXN","text":"Manassas Bluff Flora Database"}],"country":"United States","state":"Virginia","otherGeospatial":"Bull Run, Manassas National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.62424468994139,\n 38.79369731838258\n ],\n [\n -77.61566162109375,\n 38.80654039080489\n ],\n [\n -77.60948181152344,\n 38.819916129801314\n ],\n [\n -77.60261535644531,\n 38.830614912420565\n ],\n [\n -77.59471893310547,\n 38.849066533608884\n ],\n [\n -77.58956909179688,\n 38.85414657465764\n ],\n [\n -77.58922576904297,\n 38.86083028642411\n ],\n [\n -77.57377624511719,\n 38.85708748522446\n ],\n [\n -77.57068634033203,\n 38.85468129471517\n ],\n [\n -77.552490234375,\n 38.850938169890064\n ],\n [\n -77.54528045654297,\n 38.84505571861154\n ],\n [\n -77.53429412841797,\n 38.841579496043266\n ],\n [\n -77.52399444580078,\n 38.83837052447013\n ],\n [\n -77.50991821289062,\n 38.84345132929943\n ],\n [\n -77.50373840332031,\n 38.83837052447013\n ],\n [\n -77.50099182128906,\n 38.833556795784745\n ],\n [\n -77.50614166259766,\n 38.83034746244922\n ],\n [\n -77.50030517578125,\n 38.82473078093276\n ],\n [\n -77.50133514404297,\n 38.81777618035695\n ],\n [\n -77.4972152709961,\n 38.81536866036827\n ],\n [\n -77.48451232910156,\n 38.81028585091164\n ],\n [\n -77.486572265625,\n 38.80225962384825\n ],\n [\n -77.48485565185547,\n 38.79931644737651\n ],\n [\n -77.47318267822266,\n 38.80225962384825\n ],\n [\n -77.48846054077148,\n 38.79543661993626\n ],\n [\n -77.51644134521484,\n 38.79008478723166\n ],\n [\n -77.5224494934082,\n 38.79704209139086\n ],\n [\n -77.53961563110352,\n 38.793831112316646\n ],\n [\n -77.54631042480467,\n 38.791556581282244\n ],\n [\n -77.54287719726562,\n 38.783795870358176\n ],\n [\n -77.57240295410156,\n 38.77710492428489\n ],\n [\n -77.62046813964844,\n 38.77041335043523\n ],\n [\n -77.62424468994139,\n 38.79369731838258\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Columbia Environmental Research Center
U.S. Geological Survey
4200 New Haven Road
Columbia, MO 65201
http://www.cerc.usgs.gov/
The U.S. Geological Survey (USGS) and partners in other Federal and State agencies are working collaboratively toward Statewide coverage of interferometric synthetic aperture radar (ifsar) elevation data in Alaska. These data will provide many benefits to a wide range of stakeholders and users. Some applications include development of more accurate and highly detailed topographic maps; improvement of surface water information included in the National Hydrography (NHD) and Watershed Boundary Datasets (WBDs); and use in scientific modeling applications such as calculating glacier surface elevation differences over time and estimating tsunami inundation areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153051","usgsCitation":"Craun, K.J., 2015, Mapping benefits from updated ifsar data in Alaska—Improved source data enables better maps: U.S. Geological Survey Fact Sheet 2015–3051, 2 p., https://dx.doi.org/10.3133/fs20153051.","productDescription":"2 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067048","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":306439,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3051/fs20153051.pdf","text":"Report","size":"4.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3051"},{"id":306438,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2015/3051/coverthb.jpg"}],"country":"United 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\":{\"name\":\"Alaska\",\"nation\":\"USA \"}}]}","contact":"Director, National Geospatial Technical Operations Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401-2602
http://ngtoc.usgs.gov/
Large research studies are often challenged to effectively expose and document the types of information being collected and the reasons for data collection across what are often a diverse cadre of investigators of differing disciplines. We applied concepts from the field of information or data modeling to the U.S. Geological Survey (USGS) Changing Arctic Ecosystems (CAE) initiative to prototype an application of information modeling. The USGS CAE initiative is collecting information from marine and terrestrial environments in Alaska to identify and understand the links between rapid physical changes in the Arctic and response of wildlife populations to these ecosystem changes. An associated need is to understand how data collection strategies are informing the overall science initiative and facilitating communication of those strategies to a wide audience. We explored the use of conceptual data modeling to provide a method by which to document, describe, and visually communicate both enterprise and study level data; provide a simple means to analyze commonalities and differences in data acquisition strategies between studies; and provide a tool for discussing those strategies among researchers and managers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151148","collaboration":"Science to Support the USGS Changing Arctic Ecosystems Initiative","usgsCitation":"Walworth, Dennis, and Pearce, J.M., 2015, Conceptual data modeling of wildlife response indicators to\necosystem change in the Arctic: U.S. Geological Survey Open-File Report 2015-1148, 28 p.,\nhttps://dx.doi.org/10.3133/ofr20151148.","productDescription":"iv, 28 p.","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-053898","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":306449,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1148/coverthb.jpg"},{"id":306450,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1148/ofr20151148.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1148 Report"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -168.48632812499997,\n 62.3903694381427\n ],\n [\n -168.48632812499997,\n 71.46912418989677\n ],\n [\n -144.84375,\n 71.46912418989677\n ],\n [\n -144.84375,\n 62.3903694381427\n ],\n [\n -168.48632812499997,\n 62.3903694381427\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Dr
Anchorage, Alaska 99508-4560
http://alaska.usgs.gov