The world’s future oil and gas supplies depend on existing reserves and the additions to those reserves that may result, in part, from ongoing exploration and new discoveries. This Circular summarizes available oil and gas exploration data for the world outside the United States and Canada (the study area) through 2015. It updates U.S. Geological Survey Circulars 981, 1096, and 1288 (by D.H. Root, E.D. Attanasi, and R.L. Turner, 1987; E.D. Attanasi and D.H. Root, 1993; and E.D. Attanasi, P.A. Freeman, and J.A. Glovier, 2007). The exploration measures focus on the search for undiscovered conventional oil and gas accumulations.
The goal of this compilation, presentation, and analysis of exploration and discovery data is to identify, at the reconnaissance level, the areas explored for oil and gas and to characterize their degree of exploration maturity. Maps and graphs provide a visual summary of the exploration maturity of an area. The maps include both land and offshore areas. The maps show delineated prospective areas, which are the industry-defined areas of interest in the search for undiscovered conventional oil and gas accumulations. The maps also show explored areas, which are areas where the density of exploration and development drilling rules out new discoveries of large conventional petroleum accumulations.
Whereas the maps show the static state of oil and gas exploration, the dynamic measures of exploration progress are characterized graphically. The graphs show the growth in the delineated prospective and explored areas as a function of wildcat drilling. The relation between the expansion of the delineated prospective area and the rate of wildcat drilling is determined by the siting of the wildcat wells. Additional graphs show the magnitude of discoveries tied to specific delineated prospective areas. These graphs provide a way to evaluate the quality, in terms of discovered oil and gas, of areas identified by the dates when each area became prospective.
From 2006 through 2015, the delineated prospective area within the study area expanded at a rate of about 48,100 square miles per year. This is slightly above the expansion rate of 46,200 square miles per year from 1996 through 2005. From 2006 through 2015, the explored area expanded at a rate of about 12,900 square miles per year, which is somewhat greater than the rate of 11,300 square miles per year for the period from 1996 through 2005. The delineated prospective area established by 1970 accounts for 35 percent of the delineated prospective area established through 2015 but contains 70 percent of the oil and 52 percent of the natural gas discovered through 2015. From 2006 through 2015, offshore discoveries accounted for 71 percent of the oil and 78 percent of the gas discovered in the study area and 40 percent of the offshore wildcat wells were drilled in deep offshore areas (deeper than 200 meters water depth).
The delineated prospective area and explored area calculated with oil and gas wells and fields at depths of at least 10,000 feet are less than half of the respective areas calculated with all oil and gas wells and fields. The discovery histories of most regions indicate that average discovery sizes are generally larger in deeper geologic horizons. To correctly interpret the exploration maturity of a deep horizon, drilling and discovery data must be considered in the context of the geology of the area. Such analyses should be prepared at the level of the petroleum basin or subbasin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1450","collaboration":" ","usgsCitation":"Attanasi, E.D., and Freeman, P.A., 2019, Statistics of petroleum exploration in the world outside the United States and Canada through 2015: U.S. Geological Survey Circular 1450, 237 p., https://doi.org/10.3133/cir1450.","productDescription":"vii, 237 p.","numberOfPages":"237","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-102161","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":364691,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1450/circ1450.pdf","text":"Report","size":"31.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CiRC 1450"},{"id":364690,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1450/coverthb.jpg"}],"contact":"Director, Eastern Energy Resources Science Center
U.S. Geological Survey
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12201 Sunrise Valley Drive
Reston, VA 20192
Deployment of carbon capture and storage (CCS) could be necessary to be able to satisfy baseload electricity demand, maintain diversity in the energy mix, and achieve mitigation of carbon dioxide (CO2) emissions at lowest cost (IPCC, 2015; U.S. DOE, 2016). If basin-, regional- or national-scale deployment of CCS is needed, it may be possible to store only a small fraction of the captured CO2 in oil and natural gas reservoirs. The vast majority would likely have to be stored in saline formations. Pressure buildup as a result of injecting CO2 into such reservoirs is expected to be an important source of risk associated with CO2 storage, and could constrain dynamic storage capacities (maximum injection rates) to be far below estimates based on access to theoretical storage resources. Estimates of CO2 storage costs based on an assumption of practical availability of the theoretical storage resource could lead to underestimation of the costs of CO2 storage. In this study, simulation results suggest that the pressure-limited dynamic CO2 storage capacity of the Mount Simon Sandstone could be less than 4% of the theoretical storage resource in this saline formation, and storage costs could be an order of magnitude higher than recent estimates. However, consideration of the geologic heterogeneity in this deep saline formation allowed definition of a high injectivity zone, and estimated costs of CO2 storage in this “sweet spot” of the reservoir approached recent estimates that did not include costs for pressure management.
","language":"English","publisher":"Elsevier Ltd.","doi":"10.1016/j.ijggc.2019.05.031","usgsCitation":"Anderson, S.T., and Jahediesfanjani, H., 2019, Estimating the pressure-limited dynamic capacity and costs of basin-scale CO2 storage in a Saline Formation: International Journal of Greenhouse Gas Control, v. 88, p. 156-167, https://doi.org/10.1016/j.ijggc.2019.05.031.","productDescription":"12 p.","startPage":"156","endPage":"167","ipdsId":"IP-102164","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":467533,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ijggc.2019.05.031","text":"Publisher Index Page"},{"id":364831,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Kentucky","otherGeospatial":"Mount Simon Sandstone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.52734374999999,\n 41.07935114946899\n ],\n [\n -91.03271484375,\n 39.45316112807394\n ],\n [\n -90.37353515625,\n 38.44498466889473\n ],\n [\n -89.07714843749999,\n 38.151837403006766\n ],\n [\n -86.68212890625,\n 37.35269280367274\n ],\n [\n -84.5068359375,\n 37.82280243352756\n ],\n [\n -85.14404296875,\n 39.35129035526705\n ],\n [\n -86.68212890625,\n 40.763901280945866\n ],\n [\n -88.87939453125,\n 40.6306300839918\n ],\n [\n -90.52734374999999,\n 41.07935114946899\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"88","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Steven T. 0000-0003-3481-3424 sanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-3481-3424","contributorId":2532,"corporation":false,"usgs":true,"family":"Anderson","given":"Steven","email":"sanderson@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":764635,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jahediesfanjani, Hossein 0000-0001-6281-5166 hjahediesfanjani@usgs.gov","orcid":"https://orcid.org/0000-0001-6281-5166","contributorId":193397,"corporation":false,"usgs":false,"family":"Jahediesfanjani","given":"Hossein","email":"hjahediesfanjani@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":764665,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70219072,"text":"70219072 - 2019 - Quantitative evaluation of vitrinite reflectance in shale using Raman spectroscopy and multivariate analysis","interactions":[],"lastModifiedDate":"2021-03-23T15:04:37.764859","indexId":"70219072","displayToPublicDate":"2019-06-13T10:00:58","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1709,"text":"Fuel","active":true,"publicationSubtype":{"id":10}},"title":"Quantitative evaluation of vitrinite reflectance in shale using Raman spectroscopy and multivariate analysis","docAbstract":"The current research builds upon a previously published study that demonstrated the combination of Raman spectroscopy coupled with multivariate analysis (MVA) for the prediction of thermal maturity in coal by evaluating the efficacy of this method for the prediction of thermal maturity in shale. MVA techniques eliminate analyst bias in peak-fitting methods by using the full Raman spectrum, and then extricating the important spectral regions for distinguishing samples and building accurate, robust models. Partial least squares (PLS) regression models were developed using Raman spectra and VRo values (0.58–4.59%) for 53 geographically diverse shale chip samples, and 43 shale powder samples. Separate PLS models were built using Raman spectra from shale chips or powders. The calibration sets were validated using approximately one-third of the samples to rigorously assess the predictive accuracy of the models. The root mean standard error of prediction was 0.24 for the shale chip model, and 0.28 for the shale powder model. The coefficients of determination (R2) for the cross-validated data sets were identical (0.90, chips; 0.90, powders), revealing a strong linearity despite the geographic and age diversity of the samples. This study demonstrates the validity of using PLS models for the prediction of shale VRo from Raman spectra. The MVA method described herein presents a Raman alternative to the VRo industry benchmark for assessing thermal maturity in shale that is not imperiled by the shortcomings and subjectivity of peak-fitting methods.
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Inc.","active":true,"usgs":false}],"preferred":false,"id":812667,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70203911,"text":"70203911 - 2019 - Geographic variation in natal dispersal of Northern Spotted Owls over 28 years","interactions":[],"lastModifiedDate":"2019-06-21T09:24:24","indexId":"70203911","displayToPublicDate":"2019-06-13T09:17:20","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Geographic variation in natal dispersal of Northern Spotted Owls over 28 years","docAbstract":"The most recent comprehensive estimates of Northern Spotted Owl (Strix occidentalis caurina) natal dispersal distances were reported in 2002. Since then, Northern Spotted Owl populations have experienced substantial demographic changes, with potential attendant changes in natal dispersal distances, including temporal or geographic trends. We analyzed the natal dispersal of Northern Spotted Owls during 1985–2012 in Oregon and Washington, USA (n = 1,534 dispersal events), to determine current natal dispersal distances and to evaluate potential trends that may inform management actions. Mean net dispersal distance (natal site to site of first attempted breeding) was 23.8 km +- 19.2 km SD, with females dispersing ~50% farther than males. Net dispersal distance varied by ecoregion (Washington Coast and Cascades, Washington Eastern Cascades, Oregon Coast Range, Oregon and California Cascades, and Oregon and California Klamath) but declined similarly in all ecoregions over time (~1 km yr^-1 ). Dispersal direction also varied by ecoregion, following coarse-scale forest habitat configuration, and was bimodal (north–south) in the Oregon Coast Range, south–southwest in the Oregon and California Cascades, and showed little directionality in the Washington Eastern Cascades, Washington Coast and Cascades, and Oregon and California Klamath. Long-distance dispersal events (.50 km) also varied by ecoregion (mean: 62.3–99.5 km), with most long-distance dispersal (8% of dispersers; distances up to 177 km) originating in southern ecoregions. We found no direct relationship between Barred Owl (Strix varia) detections near natal or settling locations and dispersal distance. These findings, particularly the declining trend of dispersal distances, may inform management actions aimed toward conservation of the Northern Spotted Owl.
","language":"English","publisher":"BioOne","doi":"10.1650/CONDOR-17-164.1","usgsCitation":"Hollenbeck, J., Haig, S.M., Forsman, E.D., and Wiens, D., 2019, Geographic variation in natal dispersal of Northern Spotted Owls over 28 years: The Condor, v. 120, no. 3, p. 530-542, https://doi.org/10.1650/CONDOR-17-164.1.","productDescription":"13 p.","startPage":"530","endPage":"542","ipdsId":"IP-093377","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":364874,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.73876953125,\n 48.4146186174932\n ],\n [\n -124.73876953125,\n 47.96050238891509\n ],\n [\n -124.23339843749999,\n 47.27922900257082\n ],\n [\n -123.96972656249999,\n 45.874712248904764\n ],\n [\n -124.12353515624999,\n 44.62175409623324\n ],\n [\n -124.23339843749999,\n 43.5326204268101\n ],\n [\n -124.5849609375,\n 42.84375132629021\n ],\n [\n -124.3212890625,\n 41.918628865183045\n ],\n [\n -120.36621093749999,\n 42.032974332441405\n ],\n [\n -120.16845703125,\n 45.66012730272194\n ],\n [\n -119.091796875,\n 48.980216985374994\n ],\n [\n -123.24462890625,\n 48.980216985374994\n ],\n [\n -122.98095703125,\n 48.76343113791796\n ],\n [\n -123.1787109375,\n 48.66194284607006\n ],\n [\n -123.11279296875001,\n 48.32703913063476\n ],\n [\n -123.3544921875,\n 48.23930899024907\n ],\n [\n -124.73876953125,\n 48.4146186174932\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"120","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hollenbeck, Jeff 0000-0001-6481-5354","orcid":"https://orcid.org/0000-0001-6481-5354","contributorId":216400,"corporation":false,"usgs":true,"family":"Hollenbeck","given":"Jeff","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":764715,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haig, Susan M. 0000-0002-6616-7589 susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":764716,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Forsman, Eric D.","contributorId":96792,"corporation":false,"usgs":false,"family":"Forsman","given":"Eric","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":764717,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wiens, David 0000-0002-2020-038X jwiens@usgs.gov","orcid":"https://orcid.org/0000-0002-2020-038X","contributorId":167538,"corporation":false,"usgs":true,"family":"Wiens","given":"David","email":"jwiens@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":764718,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70203817,"text":"70203817 - 2019 - Geographic variation in the intensity of warming and phenological mismatch between Arctic shorebirds and invertebrates","interactions":[],"lastModifiedDate":"2019-11-13T13:22:24","indexId":"70203817","displayToPublicDate":"2019-06-08T10:09:42","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1459,"text":"Ecological Monographs","active":true,"publicationSubtype":{"id":10}},"title":"Geographic variation in the intensity of warming and phenological mismatch between Arctic shorebirds and invertebrates","docAbstract":"Responses to climate change can vary across functional groups and trophic levels, leading to a temporal decoupling of trophic interactions or ‘phenological mismatches.’ Despite a growing number of single-species studies that identified phenological mismatches as a nearly universal consequence of climate change, we have a limited understanding of the spatial variation in the intensity of this phenomenon nor what influences this variation. In this study, we tested for geographic patterns in phenological mismatches between six species of shorebirds and their invertebrate prey at ten sites spread across ~13º latitude and ~84º longitude in the Arctic over three years. At each site, we quantified the phenological mismatch between shorebirds and their invertebrate prey at: 1) an individual nest level, as the difference in days between the seasonal peak in food and the peak demand by chicks, and 2) a population level, as the overlapped area under fitted curves for total daily biomass of invertebrates and dates of the peak demand by chicks. We tested whether the intensity of past climatic change observed at each site corresponded with the extent of phenological mismatch and used Structural Equation Modeling to test for causal relationships among: 1) environmental factors, including geographic location and current climatic conditions, 2) the timing of invertebrate emergence and the breeding phenology of shorebirds, and 3) the phenological mismatch between the two trophic levels. The extent of phenological mismatch varied more among different sites than among different species within each site. A greater extent of phenological mismatch at both the individual-nest and population-levels coincided with changes in the timing of snowmelt as well as the potential dissociation of long-term snow phenology from changes in temperature. The timing of snowmelt also affected the shape of the food and demand curves, which determined the extent of phenological mismatch at the population level. Finally, we found larger mismatches at more easterly longitudes, which may be affecting the population dynamics of shorebirds, as two of our study species show regional population declines in only the eastern part of their range. This suggests that phenological mismatches may be resulting in demographic consequences for arctic-nesting birds.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecm.1383","usgsCitation":"Kwon, E., Weiser, E.L., Lanctot, R.B., Brown, S.C., Gates, H.R., Gilchrist, H.G., Kendall, S.J., David B. Lank, Joseph R. Liebezeit, McKinnon, L., Erica Nol, Payer, D.C., Rausch, J., Saalfeld, S.T., Rinella, D.J., Senner, N.R., Smith, P., Ward, D., Wissman, R.C., and Sandercock, B.K., 2019, Geographic variation in the intensity of warming and phenological mismatch between Arctic shorebirds and invertebrates: Ecological Monographs, v. 89, no. 4, e01383, https://doi.org/10.1002/ecm.1383.","productDescription":"e01383","ipdsId":"IP-068533","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":467549,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/11250/2607430","text":"External Repository"},{"id":364696,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska","otherGeospatial":"North American Arctic","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.2734375,\n 58.03137242177637\n ],\n [\n -92.197265625,\n 58.03137242177637\n ],\n [\n -92.197265625,\n 59.17592824927136\n ],\n [\n -95.2734375,\n 59.17592824927136\n ],\n [\n -95.2734375,\n 58.03137242177637\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.671875,\n 63.60721668033077\n ],\n [\n -81.34277343749999,\n 63.60721668033077\n ],\n [\n -81.34277343749999,\n 64.49172504435471\n ],\n [\n -83.671875,\n 64.49172504435471\n ],\n [\n -83.671875,\n 63.60721668033077\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -131.923828125,\n 69.83962194067463\n ],\n [\n -159.08203125,\n 71.63599288330609\n ],\n [\n -159.169921875,\n 71.32895017791999\n ],\n [\n -157.32421875,\n 70.64176873584621\n ],\n [\n -148.271484375,\n 69.7485511291223\n ],\n [\n -139.921875,\n 68.9110048456202\n ],\n [\n -132.1875,\n 68.75231494434473\n ],\n [\n -131.923828125,\n 69.83962194067463\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -167.51953124999997,\n 64.28275952823394\n ],\n [\n -164.8828125,\n 64.28275952823394\n ],\n [\n -164.8828125,\n 65.18303007291382\n ],\n [\n -167.51953124999997,\n 65.18303007291382\n ],\n [\n -167.51953124999997,\n 64.28275952823394\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -162.59765625,\n 66.19600891267761\n ],\n [\n -160.13671875,\n 66.19600891267761\n ],\n [\n -160.13671875,\n 67.33986082559095\n ],\n [\n -162.59765625,\n 67.33986082559095\n ],\n [\n -162.59765625,\n 66.19600891267761\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"89","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Kwon, Enubi","contributorId":216232,"corporation":false,"usgs":false,"family":"Kwon","given":"Enubi","email":"","affiliations":[{"id":33062,"text":"Division of Biology, Kansas State University","active":true,"usgs":false}],"preferred":false,"id":764254,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Weiser, Emily L. 0000-0003-1598-659X","orcid":"https://orcid.org/0000-0003-1598-659X","contributorId":213770,"corporation":false,"usgs":true,"family":"Weiser","given":"Emily","email":"","middleInitial":"L.","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":764294,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lanctot, Richard B.","contributorId":31894,"corporation":false,"usgs":true,"family":"Lanctot","given":"Richard","email":"","middleInitial":"B.","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false},{"id":7029,"text":"Queen's University, Kingston, Ontario, Canada","active":true,"usgs":false},{"id":135,"text":"Biological Resources Division","active":false,"usgs":true},{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":764295,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Stephen C. 0000-0002-0421-1660","orcid":"https://orcid.org/0000-0002-0421-1660","contributorId":208214,"corporation":false,"usgs":false,"family":"Brown","given":"Stephen","email":"","middleInitial":"C.","affiliations":[{"id":37764,"text":"Shorebird Recovery Program","active":true,"usgs":false}],"preferred":false,"id":764296,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gates, H. River","contributorId":138969,"corporation":false,"usgs":false,"family":"Gates","given":"H.","email":"","middleInitial":"River","affiliations":[{"id":12600,"text":"ABR, Inc. – Environmental Research and Services","active":true,"usgs":false}],"preferred":false,"id":764297,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gilchrist, H. Grant","contributorId":177911,"corporation":false,"usgs":false,"family":"Gilchrist","given":"H.","email":"","middleInitial":"Grant","affiliations":[],"preferred":false,"id":764298,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kendall, Steve J. 0000-0002-9290-5629","orcid":"https://orcid.org/0000-0002-9290-5629","contributorId":169663,"corporation":false,"usgs":false,"family":"Kendall","given":"Steve","email":"","middleInitial":"J.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":764299,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"David B. Lank","contributorId":203668,"corporation":false,"usgs":false,"family":"David B. Lank","affiliations":[{"id":36678,"text":"Simon Fraser University","active":true,"usgs":false}],"preferred":false,"id":764300,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Joseph R. Liebezeit","contributorId":203669,"corporation":false,"usgs":false,"family":"Joseph R. Liebezeit","affiliations":[{"id":36680,"text":"Audubon Society of Portland","active":true,"usgs":false}],"preferred":false,"id":764301,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"McKinnon, Laura","contributorId":169353,"corporation":false,"usgs":false,"family":"McKinnon","given":"Laura","email":"","affiliations":[],"preferred":false,"id":764302,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Erica Nol","contributorId":203671,"corporation":false,"usgs":false,"family":"Erica Nol","affiliations":[{"id":36679,"text":"Trent University","active":true,"usgs":false}],"preferred":false,"id":764303,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Payer, David C.","contributorId":7495,"corporation":false,"usgs":false,"family":"Payer","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":764304,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Rausch, Jennie","contributorId":203672,"corporation":false,"usgs":false,"family":"Rausch","given":"Jennie","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":764305,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Saalfeld, Sarah T.","contributorId":208223,"corporation":false,"usgs":false,"family":"Saalfeld","given":"Sarah","email":"","middleInitial":"T.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":764306,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Rinella, Daniel J.","contributorId":69048,"corporation":false,"usgs":true,"family":"Rinella","given":"Daniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":764307,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Senner, Nathan R.","contributorId":140465,"corporation":false,"usgs":false,"family":"Senner","given":"Nathan","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":764308,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Ward, David 0000-0002-3355-0637","orcid":"https://orcid.org/0000-0002-3355-0637","contributorId":216231,"corporation":false,"usgs":true,"family":"Ward","given":"David","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":764253,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Smith, Paul A.","contributorId":73477,"corporation":false,"usgs":true,"family":"Smith","given":"Paul A.","affiliations":[],"preferred":false,"id":764309,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Wissman, Robert C.","contributorId":89119,"corporation":false,"usgs":true,"family":"Wissman","given":"Robert","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":764310,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Sandercock, Brett K.","contributorId":95816,"corporation":false,"usgs":true,"family":"Sandercock","given":"Brett","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":764311,"contributorType":{"id":1,"text":"Authors"},"rank":20}]}} ,{"id":70202636,"text":"sir20195015 - 2019 - Evaluation of land subsidence and ground failures at Bicycle Basin, Fort Irwin National Training Center, California, 1992–2017","interactions":[],"lastModifiedDate":"2019-06-26T13:06:37","indexId":"sir20195015","displayToPublicDate":"2019-06-05T08:40:10","publicationYear":"2019","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":"2019-5015","displayTitle":"Evaluation of Land Subsidence and Ground Failures at Bicycle Basin, Fort Irwin National Training Center, California, 1992–2017","title":"Evaluation of land subsidence and ground failures at Bicycle Basin, Fort Irwin National Training Center, California, 1992–2017","docAbstract":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Dispersal shapes demographic processes and therefore is fundamental to understanding biological, ecological, and evolutionary processes acting within populations. However, assessing population connectivity in scoters (Melanitta sp.) is challenging as these species have large spatial distributions that span remote landscapes, have varying nesting distributions (disjunct vs. continuous), exhibit unknown levels of dispersal, and vary in the timing of the formation of pair bonds (winter vs. fall/spring migration) that may influence the distribution of genetic diversity. Here, we used double‐digest restriction‐associated DNA sequence (ddRAD) and microsatellite genotype data to assess population structure within the three North American species of scoter (black scoter, M. americana; white‐winged scoter, M. deglandi; surf scoter, M. perspicillata), and between their European congeners (common scoter, M. nigra; velvet scoter, M. fusca). We uncovered no or weak genomic structure (ddRAD ΦST < 0.019; microsatellite FST < 0.004) within North America but high levels of structure among European congeners (ddRAD ΦST > 0.155, microsatellite FST > 0.086). The pattern of limited genomic structure within North America is shared with other sea duck species and is often attributed to male‐biased dispersal. Further, migratory tendencies (east vs. west) of female surf and white‐winged scoters in central Canada are known to vary across years, providing additional opportunities for intracontinental dispersal and a mechanism for the maintenance of genomic connectivity across North America. In contrast, the black scoter had relatively elevated levels of divergence between Alaska and Atlantic sites and a second genetic cluster found in Alaska at ddRAD loci was concordant with its disjunct breeding distribution suggestive of a dispersal barrier (behavioral or physical). Although scoter populations appear to be connected through a dispersal network, a small percentage (<4%) of ddRAD loci had elevated divergence which may be useful in linking areas (nesting, molting, staging, and wintering) throughout the annual cycle.
Porosity is one of the most important parameters to assess in-place oil or gas in reservoirs, and to evaluate recovery from enhanced production operations. Since it is relatively well-established to determine porosity using different laboratory and field methods, its value is usually determined at many locations across a reservoir as part of the common practice to capture reservoir heterogeneity and the variability in values. This suite of measurements and the distribution of values are most valuable for probabilistic reservoir assessments, and for spatial modeling if the exact data locations are known.
Despite the importance of individual measurements to set the range of values for probabilistic studies, it is not always possible to access these data due to confidentiality. In most cases, commercial or publicly available databases that assessments may rely on usually report only mean values of porosity, like any other reservoir data, or they may not report a value at all. This makes both quantifying the mean value and the uncertainty around it difficult for probabilistic assessments.
In this study, the TORIS (Tertiary Oil Recovery Information System) dataset of the National Petroleum Council and the U.S. Department of Energy was used to model porosity and the uncertainty around predicted values. TORIS is an integrated dataset of production data, reservoir properties, and project databases of crude oil reservoirs in the United States. The model presented in the paper was based on ANCOVA (Analysis of Co-Variance) of data from 1038 reservoirs from the TORIS dataset for porosity prediction, validation and testing for quantitative and qualitative parameters that may be readily available in most cases, and to estimate uncertainty around the mean values. This model also explored association of porosity values to different parameters, and to different depositional systems and diagenetic overprint conditions. Furthermore, an ANN (Artificial Neural Network) model was created to compare the predicted values of both models. Results showed that the ANN model was able to represent more of the variability, however it lacked the insights that might be gained from the ANCOVA model.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.petrol.2019.05.071","usgsCitation":"Karacan, C.O., 2019, An ANCOVA model for porosity and its uncertainty for oil reservoirs based on TORIS dataset: Journal of Petroleum Science and Engineering, 24 p., https://doi.org/10.1016/j.petrol.2019.05.071.","productDescription":"24 p.","ipdsId":"IP-103341","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":364378,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":364371,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S092041051930525X"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":201991,"corporation":false,"usgs":true,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":763708,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70216096,"text":"70216096 - 2019 - Estimating connectivity of hard clam (Mercenaria mercenaria) and eastern oyster (Crassostrea virginica) larvae in Barnegat Bay","interactions":[],"lastModifiedDate":"2020-11-04T16:44:24.630283","indexId":"70216096","displayToPublicDate":"2019-06-01T10:39:03","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Estimating connectivity of hard clam (Mercenaria mercenaria) and eastern oyster (Crassostrea virginica) larvae in Barnegat Bay","docAbstract":"Concentrations and emissions of greenhouse gases CO2, CH4, and N2O commonly are examined individually in aquatic environments in which each is expected to be relatively important; however, their co-occurrence and dynamic interactions in fluvial settings could provide important information about their controlling biogeochemical processes and potential contributions to global climate change. Spatial and temporal variability of CH4, N2O, and CO2 concentrations were measured from June 1999 to September 2003 in two nitrate-rich (40–1200 μM) streams draining agricultural land in the midwestern USA that differed ~13-fold in flow. Seasonal (biweekly), diel (hourly), and transport-oriented (reach-scale) sampling approaches were compared. Dissolved gas concentrations exceeded atmospheric equilibrium values up to 700- and 16-fold, for CH4 and N2O, respectively. Mean concentrations were higher in the larger stream than in the smaller stream. In both streams, CH4 emissions were generally higher in summer-fall and negatively correlated with flow and NO3− concentration while N2O emissions were generally higher in winter/spring and positively correlated with flow and NO3−. In the small stream, diel variations in the concentrations, emissions, and isotopic compositions of CH4, N2O, and NO2− resulted from diel variations in sources, sinks, and air-water gas exchange velocities. Seasonal mean total (CH4 + N2O) area-normalized emission rates, expressed as CO2 warming potential equivalents, were similar for the two streams, but the total reach-scale emission rate for the larger stream, including CO2, was about 2.9 times that of the smaller stream (131.6 vs 46.0 kg CO2 equivalents km−1 day−1, respectively). The CH4contribution to this flux was 9–28%, despite the relatively high NO3−and O2 concentrations in the streams, indicating contributions from upwelling groundwater or reactions in streambed sediment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.05.374","usgsCitation":"Smith, R.L., and Bohlke, J., 2019, Methane and nitrous oxide temporal and spatial variability in two midwestern USA streams containing high nitrate concentrations: Science of the Total Environment, v. 685, p. 574-588, https://doi.org/10.1016/j.scitotenv.2019.05.374.","productDescription":"15 p.","startPage":"574","endPage":"588","ipdsId":"IP-080054","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":467581,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2019.05.374","text":"Publisher Index Page"},{"id":437440,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TH8KWZ","text":"USGS data release","linkHelpText":"Methane and nitrous oxide temporal and spatial concentrations in the Iroquois River and Sugar Creek in Northwestern Indiana and Northeastern Illinois, 1999-2003."},{"id":364586,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana","otherGeospatial":"Iroquois River, Sugar Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.8961181640625,\n 40.46993497635156\n ],\n [\n -87.11334228515625,\n 40.46993497635156\n ],\n [\n -87.11334228515625,\n 40.89067715064627\n ],\n [\n -87.8961181640625,\n 40.89067715064627\n ],\n [\n -87.8961181640625,\n 40.46993497635156\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"685","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Richard L. 0000-0002-3829-0125 rlsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-3829-0125","contributorId":1592,"corporation":false,"usgs":true,"family":"Smith","given":"Richard","email":"rlsmith@usgs.gov","middleInitial":"L.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":764011,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bohlke, J.K. 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":191103,"corporation":false,"usgs":true,"family":"Bohlke","given":"J.K.","email":"jkbohlke@usgs.gov","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":764012,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70206266,"text":"70206266 - 2019 - Mortality of Tufted puffins (Fratercula cirrhata) and other alcids during an unusual mortality event in the eastern Bering Sea","interactions":[],"lastModifiedDate":"2019-10-29T08:38:17","indexId":"70206266","displayToPublicDate":"2019-05-29T08:36:33","publicationYear":"2019","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":"Mortality of Tufted puffins (Fratercula cirrhata) and other alcids during an unusual mortality event in the eastern Bering Sea","docAbstract":"Mass mortality events are increasing in frequency and magnitude, potentially linked with ongoing climate change. In October 2016 through January 2017, St. Paul Island situated at the shelf-edge of the Bering Sea, Alaska, experienced a mortality event of alcids (family: Alcidae), with over 350 carcasses recovered. Almost three-quarters of the carcasses were unscavenged, a rate much higher than in baseline surveys (17%), suggesting that a sudden, large deposition event overwhelmed local scavenger populations. Based on the observation that carcasses were not observed on the neighboring island of St. George, we bounded the at-sea distribution of moribund birds, and estimated all species mortality at 8,000 to 22,000 birds. The event was particularly anomalous given the late fall/winter timing of the event when low numbers of beached birds are typical; and the predominance of Tufted puffins (Fratercula cirrhata, 79% of carcass finds) and Crested auklets (Aethia cristatella, 11% of carcass finds), species that were nearly absent from long-term baseline surveys. Collected specimens were disease-free and severely emaciated, suggesting starvation as the ultimate cause of mortality. The majority (95%, N = 245) of Tufted puffins were regrowing flight feathers, indicating a potential contribution of molt stress. Immediately prior to this event, shifts in zooplankton community composition and forage fish distribution and energy density were documented in the eastern Bering Sea following a period of elevated sea surface temperatures, evidence cumulatively suggestive of a bottom-up shift in seabird prey availability. We posit that shifts in prey composition and distribution pushing birds outside of their normal fall migration pattern, combined with the onset of molt, resulted in this mortality event","language":"English","publisher":"PLos ONE","doi":"10.1371/journal.pone.0216532","usgsCitation":"Jones, T., Divine, L.M., Renner, H., Knowles, S., Lefebvre, K.A., Burgess, H.K., Wright, C., and Parrish, J.K., 2019, Mortality of Tufted puffins (Fratercula cirrhata) and other alcids during an unusual mortality event in the eastern Bering Sea: PLoS ONE, v. 14, no. 5, e0216532, 23 p., https://doi.org/10.1371/journal.pone.0216532.","productDescription":"e0216532, 23 p.","ipdsId":"IP-101916","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":467586,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0216532","text":"Publisher Index Page"},{"id":368694,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Bering Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -182.98828124999997,\n 49.15296965617042\n ],\n [\n -147.3046875,\n 49.15296965617042\n ],\n [\n -147.3046875,\n 62.59334083012024\n ],\n [\n -182.98828124999997,\n 62.59334083012024\n ],\n [\n -182.98828124999997,\n 49.15296965617042\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"5","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, Timothy","contributorId":220052,"corporation":false,"usgs":false,"family":"Jones","given":"Timothy","email":"","affiliations":[{"id":40123,"text":"School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, United States of America","active":true,"usgs":false}],"preferred":false,"id":773997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Divine, Laura M.","contributorId":220056,"corporation":false,"usgs":false,"family":"Divine","given":"Laura","email":"","middleInitial":"M.","affiliations":[{"id":40124,"text":"Aleut Community of St. Paul Island Ecosystem Conservation Office, St. Paul, Pribilof Islands, Alaska, United States of America","active":true,"usgs":false}],"preferred":false,"id":774001,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Renner, Heather","contributorId":200807,"corporation":false,"usgs":false,"family":"Renner","given":"Heather","affiliations":[],"preferred":false,"id":774002,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knowles, Susan 0000-0002-0254-6491 sknowles@usgs.gov","orcid":"https://orcid.org/0000-0002-0254-6491","contributorId":5254,"corporation":false,"usgs":true,"family":"Knowles","given":"Susan","email":"sknowles@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":773996,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lefebvre, Kathi A.","contributorId":220057,"corporation":false,"usgs":false,"family":"Lefebvre","given":"Kathi","email":"","middleInitial":"A.","affiliations":[{"id":40125,"text":"Environmental and Fisheries Sciences Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, Washington, United States of America","active":true,"usgs":false}],"preferred":false,"id":774003,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Burgess, Hillary K.","contributorId":220053,"corporation":false,"usgs":false,"family":"Burgess","given":"Hillary","email":"","middleInitial":"K.","affiliations":[{"id":40123,"text":"School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, United States of America","active":true,"usgs":false}],"preferred":false,"id":773998,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wright, Charlie","contributorId":220054,"corporation":false,"usgs":false,"family":"Wright","given":"Charlie","email":"","affiliations":[{"id":40123,"text":"School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, United States of America","active":true,"usgs":false}],"preferred":false,"id":773999,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Parrish, Julia K.","contributorId":220055,"corporation":false,"usgs":false,"family":"Parrish","given":"Julia","email":"","middleInitial":"K.","affiliations":[{"id":40123,"text":"School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, United States of America","active":true,"usgs":false}],"preferred":false,"id":774000,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70203733,"text":"70203733 - 2019 - Ross Ice Shelf response to climate driven by the tectonic imprint on seafloor bathymetry","interactions":[],"lastModifiedDate":"2019-06-07T14:40:03","indexId":"70203733","displayToPublicDate":"2019-05-27T14:23:35","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Ross Ice Shelf response to climate driven by the tectonic imprint on seafloor bathymetry","docAbstract":"Ocean melting has thinned Antarctica's ice shelves at an increasing rate over the past two decades, leading to loss of grounded ice. The Ross Ice Shelf is currently close to steady state but geological records indicate that it can disintegrate rapidly, which would accelerate grounded ice loss from catchments equivalent to 11.6 m of global sea level rise. Here, we use data from the ROSETTA-Ice airborne survey and new ocean simulations, to identify the principal threats to Ross Ice Shelf stability. We locate the tectonic boundary between East and West Antarctica from magnetic anomalies and use gravity data to generate a new high-resolution map of sub-ice-shelf bathymetry. The tectonic imprint on bathymetry constrains sub-ice-shelf ocean circulation, protecting the ice shelf grounding line from moderate changes in global ocean heat content. In contrast, local, seasonal production of warm upper-ocean water near the ice front drives rapid ice shelf melting east of Ross Island, where thinning would lead to faster grounded ice loss from both East and West Antarctic ice sheets. We confirm high modelled melt rates in this region using ROSETTA-Ice radar data. Our findings highlight the significance of both the tectonic framework and local ocean-atmosphere exchange processes near the ice front in determining the future of the Antarctic Ice Sheet.","language":"English","publisher":"Springer Nature Publishing AG","doi":"10.1038/s41561-019-0370-2","usgsCitation":"Tinto, K., Padman, L., Siddoway, C.S., Springer, M., Fricker, H., Das, I., Caratori Tontini, F., Porter, D., Frearson, N., Howard, S., Siegfried, M., Mosbeux, C., Becker, M., Bertinato, C., Boghosian, A., Brady, N., Burton, B.L., Chu, W., Cordero, S., Dhakal, T., Dong, L., Gustafson, C., Keeshin, S., Locke, C., Lockett, A., O'Brien, G., Spergel, J., Starke, S., Tankersley, M., Wearing, M., and Bell, R.E., 2019, Ross Ice Shelf response to climate driven by the tectonic imprint on seafloor bathymetry: Nature Geoscience, v. 12, p. 441-449, https://doi.org/10.1038/s41561-019-0370-2.","productDescription":"9 p.","startPage":"441","endPage":"449","ipdsId":"IP-103834","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":364522,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica, Ross Ice Shelf","volume":"12","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Tinto, K J","contributorId":216084,"corporation":false,"usgs":false,"family":"Tinto","given":"K J","affiliations":[{"id":39364,"text":"Columbia University LDEO","active":true,"usgs":false}],"preferred":false,"id":763859,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Padman, L","contributorId":216085,"corporation":false,"usgs":false,"family":"Padman","given":"L","email":"","affiliations":[{"id":39365,"text":"Earth & Space Research","active":true,"usgs":false}],"preferred":false,"id":763860,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Siddoway, C S","contributorId":216086,"corporation":false,"usgs":false,"family":"Siddoway","given":"C","email":"","middleInitial":"S","affiliations":[{"id":37163,"text":"Colorado College","active":true,"usgs":false}],"preferred":false,"id":763861,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Springer, M.R.","contributorId":216087,"corporation":false,"usgs":false,"family":"Springer","given":"M.R.","email":"","affiliations":[{"id":39366,"text":"Earth and Space Research","active":true,"usgs":false}],"preferred":false,"id":763862,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fricker, H.A.","contributorId":216088,"corporation":false,"usgs":false,"family":"Fricker","given":"H.A.","email":"","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":763863,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Das, I.","contributorId":216089,"corporation":false,"usgs":false,"family":"Das","given":"I.","email":"","affiliations":[{"id":39367,"text":"Columbia University, LDEO","active":true,"usgs":false}],"preferred":false,"id":763864,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Caratori Tontini, F.","contributorId":216090,"corporation":false,"usgs":false,"family":"Caratori Tontini","given":"F.","email":"","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":763865,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Porter, D.F.","contributorId":216091,"corporation":false,"usgs":false,"family":"Porter","given":"D.F.","email":"","affiliations":[{"id":39367,"text":"Columbia University, LDEO","active":true,"usgs":false}],"preferred":false,"id":763866,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Frearson, N.P.","contributorId":216092,"corporation":false,"usgs":false,"family":"Frearson","given":"N.P.","email":"","affiliations":[{"id":39367,"text":"Columbia University, LDEO","active":true,"usgs":false}],"preferred":false,"id":763867,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Howard, S. 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A.","contributorId":216105,"corporation":false,"usgs":false,"family":"Lockett","given":"A.","email":"","affiliations":[{"id":37163,"text":"Colorado College","active":true,"usgs":false}],"preferred":false,"id":763882,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"O'Brien, G.","contributorId":216106,"corporation":false,"usgs":false,"family":"O'Brien","given":"G.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":763883,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Spergel, J.J.","contributorId":216107,"corporation":false,"usgs":false,"family":"Spergel","given":"J.J.","email":"","affiliations":[{"id":39367,"text":"Columbia University, LDEO","active":true,"usgs":false}],"preferred":false,"id":763884,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Starke, S.E.","contributorId":216108,"corporation":false,"usgs":false,"family":"Starke","given":"S.E.","email":"","affiliations":[{"id":39367,"text":"Columbia University, LDEO","active":true,"usgs":false}],"preferred":false,"id":763885,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Tankersley, M.","contributorId":216109,"corporation":false,"usgs":false,"family":"Tankersley","given":"M.","email":"","affiliations":[{"id":37163,"text":"Colorado College","active":true,"usgs":false}],"preferred":false,"id":763886,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Wearing, M.","contributorId":216110,"corporation":false,"usgs":false,"family":"Wearing","given":"M.","email":"","affiliations":[{"id":39367,"text":"Columbia University, LDEO","active":true,"usgs":false}],"preferred":false,"id":763887,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Bell, R. E.","contributorId":216111,"corporation":false,"usgs":false,"family":"Bell","given":"R.","email":"","middleInitial":"E.","affiliations":[{"id":39367,"text":"Columbia University, LDEO","active":true,"usgs":false}],"preferred":false,"id":763888,"contributorType":{"id":1,"text":"Authors"},"rank":31}]}} ,{"id":70215293,"text":"70215293 - 2019 - Predicting hydrologic disturbance of streams using species occurrence data","interactions":[],"lastModifiedDate":"2020-10-14T15:39:43.431592","indexId":"70215293","displayToPublicDate":"2019-05-25T10:32:10","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Predicting hydrologic disturbance of streams using species occurrence data","docAbstract":"Aquatic organisms have adapted over evolutionary time-scales to hydrologic variability represented by the natural flow regime of rivers and streams in their unimpaired state. Rapid landscape change coupled with growing human demand for water have altered natural flow regimes of many rivers and streams on a global scale. Climate non-stationarity is expected to further intensify hydrologic variability, placing increased pressure on aquatic communities. Using a machine learning approach and georeferenced species occurrence data, we modeled and mapped spatial patterns of hydrologic disturbance for streams in Arkansas, Missouri, and eastern Oklahoma. Random forest (RF) models trained on fish community data, hydrologic, and landscape metrics for gaged streams in the National Hydrography (NHDPlusV2) database were used to predict a hydrologic disturbance index (HDI) for ungaged streams. The HDI is part of the USGS Geospatial Attributes of Gages for Evaluating Streamflow (GAGESII) database and is a composite index of watershed-scale disturbance from anthropogenic stressors. Fish presence/absence data had similar overall model prediction accuracy (77%; 95% CI: 0.74, 0.80) as flow variables (76%; CI: 0.73, 0.80). Including topographic variables increased the RF prediction accuracy of both the fish (90%; CI: 0.88, 0.92) and flow models (86%; CI: 0.84, 0.89). Spatial patterns of hydrologic disturbance suggest distinct ecohydrological regions exist where conservation actions may be focused. Streams with low HDI were predominately located in the Ozark Highlands, Boston Mountains, and Ouachita Mountains. Correlation analysis of HDI by flow regime showed groundwater stable streams had the lowest disturbance frequency, with over 50% of stream reaches with low HDI located in forested land cover. HDI was highest for big rivers, intermittent runoff streams and streams in areas of agricultural land use. Our results show long-term georeferenced biological data can provide a valuable resource for predictive modeling of hydrologic disturbance for ungaged rivers and streams.
Avian avulavirus (commonly known as avian paramyxovirus-1 or APMV-1) can cause disease of varying severity in both domestic and wild birds. Understanding how viruses move among hosts and geography would be useful for informing prevention and control efforts. A Bayesian statistical framework was employed to estimate the evolutionary history of 1602 complete fusion gene APMV-1 sequences collected from 1970 to 2016 in order to infer viral transmission between avian host orders and diffusion among geographic regions. Ancestral states were estimated with a non-reversible continuous-time Markov chain model, allowing transition rates between discrete states to be calculated. The evolutionary analyses were stratified by APMV-1 classes I (n = 198) and II (n = 1404), and only those sequences collected between 2006 and 2016 were allowed to contribute host and location information to the viral migration networks.
While the current data was unable to assess impact of host domestication status on APMV-1 diffusion, these analyses supported the sharing of APMV-1 among divergent host taxa. The highest supported transition rate for both classes existed from domestic chickens to Anseriformes (class I:6.18 transitions/year, 95% highest posterior density (HPD) 0.31–20.02, Bayes factor (BF) = 367.2; class II:2.88 transitions/year, 95%HPD 1.9–4.06, BF = 34,582.9). Further, among class II viruses, domestic chickens also acted as a source for Columbiformes (BF = 34,582.9), other Galliformes (BF = 34,582.9), and Psittaciformes (BF = 34,582.9). Columbiformes was also a highly supported source to Anseriformes (BF = 322.0) and domestic chickens (BF = 402.6). Additionally, our results provide support for the diffusion of viruses among continents and regions, but no interhemispheric viral exchange between 2006 and 2016. Among class II viruses, the highest transition rates were estimated from South Asia to the Middle East (1.21 transitions/year; 95%HPD 0.36–2.45; BF = 67,107.8), from Europe to East Asia (1.17 transitions/year; 95%HPD 0.12–2.61; BF = 436.2) and from Europe to Africa (1.06 transitions/year, 95%HPD 0.07–2.51; BF = 169.3).
While migration appears to occur infrequently, geographic movement may be important in determining viral diversification and population structure. In contrast, inter-order transmission of APMV-1 may occur readily, but most events are transient with few lineages persisting in novel hosts.
Waterfowl are the natural hosts of avian influenza virus (AIV), and through migration spread the virus worldwide. Most AIVs carried by wild waterfowl are low pathogenic strains; however, Goose/Guangdong/1996 lineage clade 2.3.4.4 H5 highly pathogenic (HP) AIV now appears to be endemic in wild birds in much of the Eastern Hemisphere. Most research efforts studying AIV pathogenicity in waterfowl thus far have been directed toward dabbling ducks. In order to better understand the role of diving ducks in AIV ecology, we previously characterized the pathogenesis of clade 2.3.4.4 H5 HPAIV in lesser scaup (Aythya affinis). In an effort to further elucidate AIV infection in diving ducks, the relative susceptibility and pathogenesis of two North American lineage H7 HPAIV isolates from the most recent outbreaks in the United States was investigated. Lesser scaup were inoculated with either A/turkey/IN/1403-1/2016 H7N8 or A/chicken/TN/17-007147-2/2017 H7N9 HPAIV by the intranasal route. The approximate 50% bird infectious dose (BID50) of the H7N8 isolate was determined to be 103 50% egg infectious doses (EID50), and the BID50 of the H7N9 isolate was determined to be <102 EID50, indicating some variation in adaptation between the two isolates. No mortality or clinical disease was observed in either group except for elevated body temperatures at 2 and 4 days postinoculation (DPI). Virus shedding was detected up to 14 DPI from both groups, and there was a trend for shedding to have a longer duration and at higher titer levels from the cloacal route. These results demonstrate that lesser scaup are susceptible to both H7 lineages of HPAIV, and similar to dabbling duck species, they shed virus for long periods relative to gallinaceous birds and don't present with clinical disease.
Eastern carpenter bees, Xylocopa virginica (L.) (Hymenoptera: Apidae), are among the most abundant native bee visitors to highbush blueberry, Vaccinium corymbosum L., flowers in the northeastern United States, and they sometimes display corolla-slitting behavior to rob nectar. We studied foraging behavior of X. virginica on 14 blueberry cultivars in an experimental planting in Rhode Island, and assessed factors related to slitting frequency, and the effects of slitting on fruit set and blueberry quality. Among 14 cultivars in bloom, an average of 35% (range 16–67%) of flowers were slit in 2017, and 39% (range 20–62%) in 2018. Factors that affected the proportion of corollas slit included cultivar, anther length, flower volume, and number of days in bloom at or above 15°C. Corolla slitting did not affect fruit set. Average weight and percent soluble solids of fruit resulting from slit and non-slit corollas did not differ significantly in two early- (‘Bluehaven’, ‘Earliblue’), two mid- (‘Collins’, ‘Bluecrop’), and two late-season (‘Herbert’, ‘Lateblue’) ripening cultivars in 2017. In 2018, average fruit weight and percent soluble solids resulting from slit and non-slit flowers did not differ significantly in most cultivars, but slit corollas resulted in berries with greater mass in two cultivars, ‘Bluehaven’ and ‘Collins’. ‘Collins’ fruit from non-slit corollas had a significantly higher percentage of soluble solids at maturity than fruit from slit corollas in 2018. Corolla slitting and nectar robbery by X. virginica did not have a significant negative effect on fruit quality under the described growing conditions and pollinator community.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/ee/nvz055","usgsCitation":"Tucker, S.K., Ginsberg, H., and Alm, S.R., 2019, Effect of corolla slitting and nectar robbery by the Eastern Carpenter Bee (Hymenoptera: Apidae) on fruit quality of Vaccinium corymbosum, L.; (Ericales: Ericaceae).: Environmental Entomology, v. 48, no. 3, p. 718-726, https://doi.org/10.1093/ee/nvz055.","productDescription":"9 p.","startPage":"718","endPage":"726","ipdsId":"IP-104445","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":467612,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ee/nvz055","text":"Publisher Index Page"},{"id":364524,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Rhode 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Island\",\"nation\":\"USA \"}}]}","volume":"48","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Tucker, Sara K","contributorId":216119,"corporation":false,"usgs":false,"family":"Tucker","given":"Sara","email":"","middleInitial":"K","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":763919,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ginsberg, Howard S. 0000-0002-4933-2466 hginsberg@usgs.gov","orcid":"https://orcid.org/0000-0002-4933-2466","contributorId":147665,"corporation":false,"usgs":true,"family":"Ginsberg","given":"Howard S.","email":"hginsberg@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":763918,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alm, Steven R.","contributorId":177872,"corporation":false,"usgs":false,"family":"Alm","given":"Steven","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":763920,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70203460,"text":"70203460 - 2019 - Assessment of coal mine methane (CMM) and abandoned mine methane (AMM) resource potential of longwall mine panels: example from Northern Appalachian Basin, USA","interactions":[],"lastModifiedDate":"2019-05-15T15:06:14","indexId":"70203460","displayToPublicDate":"2019-05-15T14:42:39","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of coal mine methane (CMM) and abandoned mine methane (AMM) resource potential of longwall mine panels: example from Northern Appalachian Basin, USA","docAbstract":"\"Coal mine methane (CMM) and abandoned mine methane (AMM), are by-products of underground coal mining. The quantity and the emission rate of CMM and AMM may vary depending on the type of mine, gas content of the mined coal seam, and gas sourced from strata and coal beds in overlying and underlying formations affected by mining. Therefore, if a mine has the potential of accumulating gas after being abandoned and sealed properly, methane may be produced and used as an energy source to serve to local communities around the mine. Producing AMM also prevents methane, which is a potent greenhouse gas, from leaking to the atmosphere through seals, shaft plugs or surface cracks. \nOne of the technical barriers in front of investments to economical utilization of CMM and AMM is the difficulty to predict how much methane may be available in the gas emission zone (GEZ) as a resource during mining, and after the panels are sealed and the mine is abandoned. Another difficulty is to estimate how much of the potential methane resource can be produced, and its production feasibility with boreholes, such as gob gas ventholes (GGV) converted to capture AMM.\nIn this study, a comparative assessment is presented to address the issues stated above. The assessment was conducted on two adjacent panels of a longwall mine that operated until 2016 in the Pennsylvania section of the Northern Appalachian Basin. The study is based on two approaches that might be used depending on the availability of data, extensive or minimal. The first approach uses an extensive geological data set, geostatistics, and measured shaft gas emission and GGV production values that were collected while the panel(s) were active to assess the AMM resource. The second approach uses a minimal amount of geologic data and its uncertainty as probabilistic distributions as well as predicted during-mining emissions using a publicly available software. Results showed that both approaches provide relatively comparable estimates of AMM resources and AMM recovery potential using wellbores. The differences in assessed quantities are mostly due to the characteristics of the two methods. In that regard, this paper can be considered as guidance to choose the assessment approach based on data availability.\n\"","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2019.04.005","collaboration":"none","usgsCitation":"Karacan, C.O., and Warwick, P., 2019, Assessment of coal mine methane (CMM) and abandoned mine methane (AMM) resource potential of longwall mine panels: example from Northern Appalachian Basin, USA: International Journal of Coal Geology, v. 208, p. 37-53, https://doi.org/10.1016/j.coal.2019.04.005.","productDescription":"17 p.","startPage":"37","endPage":"53","ipdsId":"IP-103342","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":363934,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Northern Appalachian Basin","volume":"208","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":201991,"corporation":false,"usgs":true,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":762770,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warwick, Peter D. 0000-0002-3152-7783","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":205928,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":762771,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70202335,"text":"ofr20191016 - 2019 - Analysis for agreement of the Northern Gulf of Mexico topobathymetric digital elevation model with 3-Dimensional Elevation Program 1/3 arc-second digital elevation models","interactions":[],"lastModifiedDate":"2019-05-14T11:43:13","indexId":"ofr20191016","displayToPublicDate":"2019-05-13T11:35:20","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1016","displayTitle":"Analysis for Agreement of the Northern Gulf of Mexico Topobathymetric Digital Elevation Model with 3-Dimensional Elevation Program 1/3 Arc-Second Digital Elevation Models","title":"Analysis for agreement of the Northern Gulf of Mexico topobathymetric digital elevation model with 3-Dimensional Elevation Program 1/3 arc-second digital elevation models","docAbstract":"Topographical differencing and edge-matching analyses were used to evaluate agreement of the Coastal National Elevation Database Applications Project’s Northern Gulf of Mexico topobathymetric digital elevation model (TBDEM) with The National Map 3-Dimensional Elevation Program (3DEP) 1/3 arc-second digital elevation models (DEMs). In addition to topographic map products provided through the National Geospatial Program, the model integrates bathymetric and topobathymetric datasets for three-dimensional (3D) mapping of rivers, lakes, and bays in the upland and intertidal wetlands to offshore environments in coastal zones from the border between Texas and Louisiana to east of Mobile Bay, Alabama.
Contoured elevation differences between the Northern Gulf of Mexico TBDEM and the 3DEP 1/3 arc-second DEMs indicate that 85 percent of elevation data in the Northern Gulf of Mexico TBDEM agree (no difference for contoured elevations) between 95 and 100 percent with 3DEP 1/3 arc-second DEMs. Edge matching differences between adjacent Northern Gulf of Mexico TBDEM source projects or between the TBDEM and 3DEP DEMs indicate most seams between integrated and 3DEP DEMs are smooth. Where seams did not match, most differences were in the range of tenths to hundredths of a meter. Valid differences that are greater than plus or minus 2 meters in areas of bathymetric data are found in the Mississippi River, Atchafalaya River, Lower Atchafalaya River, Wax Lake Pass channel, the Vermilion Bay bathymetric datasets, and where topobathymetric datasets are integrated in the model. Areas with positive or negative outlier difference elevations seem to be a result of site conditions that affect light detection and ranging (lidar) waveform return signals, misclassification of surface features, or possibly because of interpolation required to develop a smooth elevation surface. Results of this analysis provide information to help understand model parameters and agreement of the Northern Gulf of Mexico TBDEM developed using different data types from different sources with The National Map 3DEP DEMs.
Inclusion of bathymetric and topobathymetric data types in the 3DEP aligns with the mission to respond to growing needs for a wide range of three-dimensional representations of the Nation and supports the U.S. Geological Survey strategy for developing a National Terrain Model to provide hydrographic and elevation data that extend the elevation surface below water bodies. The 3D Nation Requirements and Benefits Study sponsored by the U.S. Geological Survey and National Oceanic and Atmospheric Administration to assess local to regional Tribal, State, and Federal technical requirements, needs, and benefits for using topographic and bathymetric 3DEP elevation data will be used to help develop and refine future program alternatives for 3D elevation data that include a category for bathymetry and topobathymetry. At the time of this report (2019), 3DEP acquisition is specific to topographic lidar that meets lidar DEM specifications and which requires surface-water feature areas to be hydroflattened. Cataloging bathymetric and topobathymetric DEMs as part of the 3DEP will require new specifications for acoustic, lidar, merged acoustic and lidar, and possibly other bathymetric and topobathymetric survey data types.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191016","usgsCitation":"Miller-Corbett, C., 2019, Analysis for agreement of the Northern Gulf of Mexico topobathymetric digital elevation model with 3-Dimensional Elevation Program 1/3 arc-second digital elevation models: U.S. Geological Survey Open-File Report 2019–1016, 44 p., https://doi.org/10.3133/ofr20191016.","productDescription":"vi, 43 p.","numberOfPages":"54","onlineOnly":"Y","ipdsId":"IP-081383","costCenters":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"links":[{"id":363655,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1016/ofr20191016.pdf","text":"Report","size":"16.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1016"},{"id":363654,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1016/coverthb.jpg"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.48193359375,\n 28.43971381702788\n ],\n [\n -84.13330078125,\n 28.43971381702788\n ],\n [\n -84.13330078125,\n 31.39115752282472\n ],\n [\n -96.48193359375,\n 31.39115752282472\n ],\n [\n -96.48193359375,\n 28.43971381702788\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, National Geospatial Technical Operations Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401