Rock falls from cliffs and other steep slopes present numerous challenges for detailed geological characterization. In steep terrain, rock-fall source areas are both dangerous and difficult to access, severely limiting the ability to make detailed structural and volumetric measurements necessary for hazard assessment. Airborne and terrestrial lidar survey methods can provide high-resolution data needed for volumetric, structural, and deformation analyses of rock falls, potentially making these analyses straightforward and routine. However, specific methods to collect, process, and analyze lidar data of steep cliffs are needed to maximize analytical accuracy and efficiency. This paper presents observations showing how lidar data sets should be collected, filtered, registered, and georeferenced to tailor their use in rock fall characterization. Additional observations concerning surface model construction, volumetric calculations, and deformation analysis are also provided.
","conferenceTitle":"GeoCongress 2012","conferenceDate":"March 25-29, 2012","conferenceLocation":"Oakland, CA ","language":"English","doi":"10.1061/9780784412121.309","usgsCitation":"Collins, B.D., and M.Stock, G., 2017, Lidar-Based Rock-Fall Hazard Characterization of Cliffs, GeoCongress 2012, Oakland, CA , March 25-29, 2012, p. 3021-3030, https://doi.org/10.1061/9780784412121.309.","productDescription":"10 p. ","startPage":"3021","endPage":"3030","ipdsId":"IP-033959","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":342986,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":342985,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.nps.gov/yose/learn/nature/upload/Collins-Stock-2012-ASCE.pdf"}],"country":"United States","state":"California","otherGeospatial":"Yosemite National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.234619140625,\n 38.048091067457236\n ],\n [\n -119.59716796875,\n 38.21660403859855\n ],\n [\n -119.6685791015625,\n 38.22091976683121\n ],\n [\n -120.003662109375,\n 38.08268954483802\n ],\n [\n -120.201416015625,\n 37.965854128749434\n ],\n [\n -119.8553466796875,\n 37.53586597792038\n ],\n [\n -119.72900390625001,\n 37.38761749978395\n ],\n [\n -119.39941406249999,\n 37.42688834526727\n ],\n [\n -119.25659179687499,\n 37.48793540168987\n ],\n [\n -119.1741943359375,\n 37.63163475580643\n ],\n [\n -119.091796875,\n 37.67512527892127\n ],\n [\n -119.05334472656249,\n 37.71859032558816\n ],\n [\n -119.05334472656249,\n 37.783740105227224\n ],\n [\n -119.1302490234375,\n 37.87485339352928\n ],\n [\n -119.15222167968751,\n 37.95719224376526\n ],\n [\n -119.234619140625,\n 38.048091067457236\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2012-06-20","publicationStatus":"PW","scienceBaseUri":"59536ea7e4b062508e3c7a71","contributors":{"authors":[{"text":"Collins, Brian D. 0000-0003-4881-5359 bcollins@usgs.gov","orcid":"https://orcid.org/0000-0003-4881-5359","contributorId":149278,"corporation":false,"usgs":true,"family":"Collins","given":"Brian","email":"bcollins@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":700725,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"M.Stock, Greg","contributorId":193525,"corporation":false,"usgs":false,"family":"M.Stock","given":"Greg","email":"","affiliations":[],"preferred":false,"id":700726,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70189027,"text":"70189027 - 2017 - Identification of the Polaris Fault using lidar and shallow geophysical methods","interactions":[],"lastModifiedDate":"2017-06-29T16:00:56","indexId":"70189027","displayToPublicDate":"2010-12-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Identification of the Polaris Fault using lidar and shallow geophysical methods","docAbstract":"As part of the U.S. Army Corps of Engineers' (USACE) Dam Safety Assurance Program, Martis Creek Dam near Truckee, CA, is under evaluation for earthquake and seepage hazards. The investigations to date have included LiDAR (Light Detection and Ranging) and a wide range of geophysical surveys. The LiDAR data led to the discovery of an important and previously unknown fault tracing very near and possibly under Martis Creek Dam. The geophysical surveys of the dam foundation area confirm evidence of the fault in the area.
In January 2001, the U.S. Geological Survey—in cooperation with the Edwards Aquifer Authority—began a study to refine and, if possible, extend previously derived (1995–96) relations between the stage in Medina Lake and recharge to the Edwards aquifer to include the effects of reservoir stages below 1,018 feet and greater than 1,046 feet above National Geodetic Vertical Datum of 1929. The principal objective of this present (2001–02) study was to estimate ground-water outflow (seepage) from Medina Lake, Diversion Lake, and from the Medina/Diversion Lake system through the calculation of water budgets representing steady-state conditions over as wide a range as possible in the stages of Medina and Diversion Lakes. The water budgets were compiled for selected periods during which time the water-budget components were inferred to be relatively stable and the influence of precipitation, stormwater runoff, and changes in storage were presumably minimal.
Water budgets for the Medina/Diversion Lake system were compiled for 127 water-budget periods ranging from 8 to 78 days from daily hydrologic data collected during March 1955–September 1964, October 1995–September 1996, and February 2001–June 2002. Budgets for Medina and Diversion Lakes were compiled for 14 periods ranging from 8 to 23 days from daily hydrologic data collected only during October 1995–September 1996 and April 2001–June 2002.
Linear equations were developed to relate the stage in Medina Lake to ground-water outflow from Medina Lake, Diversion Lake, and the Medina/Diversion Lake system. The computed mean rates of outflow from Medina Lake ranged from about 18 to 182 acre-feet per day between stages of 1,019 and 1,064 feet above National Geodetic Vertical Datum of 1929. The computed rates of outflow from Diversion Lake ranged from about -85 to 52 acre-feet per day. The rates of outflow from the entire lake system ranged from about 5 to 178 acre-feet per day between Medina Lake stages of 963 to 1,064 feet. It is assumed that all outflow from the lake system enters the ground-water system as recharge to the Edwards aquifer.
During the time that the stage in Medina Lake was greater than about 1,040 feet, Diversion Lake gained more water than it lost to the ground-water system and the rate of ground-water outflow from Medina Lake increased sharply while its stage was between about 1,043 and 1,045 feet. The observed outflow from Diversion Lake during this time decreased sharply to the extent that a net gain resulted—indicating that a substantial amount of the additional outflow from Medina Lake returned to Diversion Lake. When the stage in Medina Lake is at the spillway elevation of 1,064 feet, Diversion Lake appears to gain as much as 40 percent of the concurrent ground-water outflow from Medina Lake.
An indication of water moving from the lake system into the ground-water system and back to the surface-water system was observed in the most downstream reach of the Medina River, between Diversion Lake and the Medina River near Riomedina. During conditions of no flow over Diversion Dam, this reach of the Medina River gained from about 32 to 94 acre-feet per day, with the gain increasing with increasing stage in Diversion Lake.
The average of the monthly recharge to the Edwards aquifer from the Medina/Diversion Lake system—as estimated by the present study for the October 1995–September 2002 period—is 3,083 acre-feet, or about 56 percent of recharge computed for this period with a previously used (Lowry) method. The present study’s estimates of recharge for months with rising-lake stage conditions are about 44 percent of those computed with the previously used method, compared to about 60 percent for months with steady or falling-stage conditions. For stages greater than 1,045 feet, the present study estimated recharge to be about 52 percent of that computed with the previously used method, compared to about 64 percent at stages below 1,045 feet.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20045209","collaboration":"In cooperation with the Edwards Aquifer Authority","usgsCitation":"Slattery, R.N., and Miller, L.D., 2017, A water-budget analysis of Medina and Diversion Lakes and the Medina/Diversion Lake system, with estimated recharge to Edwards aquifer, San Antonio area, Texas (ver. 1.1, February 2017): U.S. Geological Survey Scientific Investigations Report 2004–5209, 41 p., https://doi.org/10.3133/sir20045209. ","productDescription":"Report: iv, 41 p.; Appendix; Data Release; Version History","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":181763,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":335301,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2004/5209/versionHist.txt","text":"Version History","size":"1.45 KB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"},{"id":335300,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ZS2TNF","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Reanalysis of the Medina/Diversion Lake System Water-Budget, with Estimated Recharge to Edwards Aquifer, San Antonio Area, Texas"},{"id":335297,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2004/5209/coverthb.jpg"},{"id":335298,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2004/5209/sir20045209.pdf","text":"Report","size":"4.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2004–5209"},{"id":335299,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/fs20173008","text":"Fact Sheet 2017–3008","size":"332 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017–3008"},{"id":335302,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2004/5209/sir20045209_appendix1.pdf","text":"Appendix 1","size":"363 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2004–5209 Appendix 1"}],"country":"United States","state":"Texas","otherGeospatial":"Upper Medina Basin","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-98.7869,29.7168],[-98.8056,29.6968],[-98.8042,29.2513],[-98.8039,29.0884],[-99.4107,29.087],[-99.4132,29.6253],[-99.6033,29.6257],[-99.6031,29.9068],[-99.6908,29.9079],[-99.6915,29.9575],[-99.6923,30.0775],[-99.7577,30.0772],[-99.7576,30.2882],[-99.3032,30.289],[-99.3034,30.1398],[-98.9217,30.139],[-98.5896,30.1375],[-98.4138,29.9442],[-98.6478,29.7477],[-98.6493,29.7495],[-98.6508,29.7509],[-98.6514,29.7523],[-98.6529,29.7532],[-98.6534,29.7532],[-98.6555,29.7528],[-98.6561,29.7514],[-98.6561,29.7491],[-98.6567,29.7478],[-98.6583,29.7478],[-98.6593,29.7492],[-98.6609,29.7492],[-98.6624,29.7492],[-98.663,29.7483],[-98.6646,29.7465],[-98.6646,29.7447],[-98.6646,29.7433],[-98.6657,29.7415],[-98.6683,29.7415],[-98.6725,29.7429],[-98.6741,29.742],[-98.6762,29.7407],[-98.681,29.7389],[-98.6926,29.7381],[-98.6984,29.7364],[-98.7016,29.7341],[-98.7042,29.7332],[-98.7084,29.7337],[-98.711,29.7342],[-98.7132,29.7315],[-98.7153,29.7283],[-98.719,29.7274],[-98.7222,29.728],[-98.7279,29.7294],[-98.7316,29.7294],[-98.7342,29.7285],[-98.7343,29.7267],[-98.7338,29.7235],[-98.7333,29.7208],[-98.7407,29.7185],[-98.747,29.7186],[-98.7527,29.721],[-98.7595,29.7224],[-98.768,29.7216],[-98.7801,29.7204],[-98.7843,29.7195],[-98.7869,29.7168]]]},\"properties\":{\"name\":\"Bandera\",\"state\":\"TX\"}}]}","edition":"Originally posted December 22, 2004; Version 1.1: February 15, 2017","contact":"Director, Texas Water Science Center
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This report presents results of the evaluation and interpretation of hydrologic and water-quality data collected as part of a cooperative program between the U.S. Geological Survey and the U.S. Environmental Protection Agency. Streamflow, phosphorus, and solids dissolved and suspended in stream water were the focus of monitoring by the U.S. Geological Survey at 10 sites on 9 selected tributaries to Lake Ontario during the period from October 2011 through September 2014. Streamflow yields (flow per unit area) were the highest from the Salmon River Basin due to sustained yields from the Tug Hill aquifer. The Eighteenmile Creek streamflow yields also were high as a result of sustained base flow contributions from a dam just upstream of the U.S. Geological Survey monitoring station at Burt. The lowest streamflow yields were measured in the Honeoye Creek Basin, which reflects a decrease in flow because of withdrawals from Canadice and Hemlock Lakes for the water supply of the City of Rochester. The Eighteenmile Creek and Oak Orchard Creek Basins had relatively high yields due in part to groundwater contributions from the Niagara Escarpment and seasonal releases from the New York State Barge Canal.
Annual constituent yields (load per unit area) of suspended solids, phosphorus, orthophosphate, and dissolved solids were computed to assess the relative contributions and allow direct comparison of loads among the monitored basins. High yields of total suspended solids were attributed to agricultural land use in highly erodible soils at all sites. The Genesee River, Irondequoit Creek, and Honeoye Creek had the highest concentrations and largest mean yields of total suspended solids (165 short tons per square mile [t/mi2], 184 t/mi2, and 89.7 t/mi2, respectively) of the study sites.
Samples from Eighteenmile Creek, Oak Orchard Creek at Kenyonville, and Irondequoit Creek had the highest concentrations and largest mean yields of phosphorus (0.27 t/mi2, 0.26 t/mi2, and 0.20 t/mi2, respectively) and orthophosphate (0.17 t/mi2, 0.13 t/mi2, and 0.04 t/mi2, respectively) of the study sites. These results were attributed to a combination of sources, including discharges from wastewater treatment plants, diversions from the New York State Barge Canal, and manure and fertilizers applied to agricultural land. Yields of phosphorus also were high in the Genesee River Basin (0.17 t/mi2) and were presumably associated with nutrient and sediment transport from agricultural land and from streambank erosion. The Salmon and Black Rivers, which drain a substantial amount of forested land and are influenced by large groundwater discharges, had the lowest concentrations and yields of phosphorus and orthophosphate of the study sites.
Mean annual yields of dissolved solids were the highest in Irondequoit Creek due to a high percentage of urbanized area in the basin and in Oak Orchard Creek at Kenyonville and in Eighteenmile Creek due to groundwater contributions from the Niagara Escarpment. High yields of dissolved solids of 840 t/mi2, 829 t/mi2, and 715 t/mi2, respectively, from these basins can be attributed to seasonal chloride yields associated with use of road deicing salts. The Niagara Escarpment can produce large amounts of dissolved solids from the dissolution of minerals (a continual process reflected in base flow samples). Groundwater inflows in the Salmon River have very low concentrations of dissolved solids due to minimal bedrock interaction along the Tug Hill Plateau and discharge from the Tug Hill sand and gravel aquifer, which has minimal mineralization.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165084","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency as part of the Great Lakes Restoration Initiative","usgsCitation":"Hayhurst, B.A., Fisher, B.N., and Reddy, J.E., 2016, Streamflow and estimated loads of phosphorus and dissolved and suspended solids from selected tributaries to Lake Ontario, New York, water years 2012–14: U.S. Geological Survey Scientific Investigations Report 2016–5084, 34 p., https://dx.doi.org/10.3133/sir20165084.","productDescription":"Report: viii, 46 p. Appendixes: 1-2","startPage":"1","endPage":"33","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-065269","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":325295,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5084/attachments/sir20165084_appendix2.xlsx","text":"Appendix 2 - Water-quality data - MS Excel","size":"80.7 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5084"},{"id":325296,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5084/attachments/sir20165084_appendix2.csv","text":"Appendix 2 - Water-quality data- CSV","size":"45.4 KB cvs","description":"SIR 2016-5084"},{"id":325384,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5084/attachments/sir20165084_appendix1.csv","text":"Appendix 1 -Streamflow and streamflow yields - CSV","size":"127 KB cvs","description":"SIR 2016-5084"},{"id":325291,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5084/coverthb.jpg"},{"id":325292,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5084/sir20165084.pdf","text":"Report","size":"6.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5084"},{"id":325293,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5084/attachments/sir20165084_appendix1.xlsx","text":"Appendix 1 - Streamflow and streamflow yields - MS Excel","size":"328 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5084"}],"country":"United States","state":"New York","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.88732910156249,\n 42.92827401776912\n ],\n [\n -78.9971923828125,\n 42.976520698105546\n ],\n [\n -78.9862060546875,\n 43.02071359427862\n ],\n [\n -78.9532470703125,\n 43.072900581493215\n ],\n [\n -79.03564453124999,\n 43.092960677116295\n ],\n [\n -79.024658203125,\n 43.16111586765961\n ],\n [\n -79.03564453124999,\n 43.28520334369384\n ],\n [\n -79.2059326171875,\n 43.432977075795606\n ],\n [\n -78.6785888671875,\n 43.61619382369188\n ],\n [\n -76.783447265625,\n 43.620170616189924\n ],\n [\n -76.4208984375,\n 44.10336537791152\n ],\n [\n -76.058349609375,\n 44.280604121518145\n ],\n [\n -75.9979248046875,\n 44.29240108529005\n ],\n [\n -76.0089111328125,\n 43.846412964702395\n ],\n [\n -76.1846923828125,\n 43.1090040242731\n ],\n [\n -76.2066650390625,\n 42.577354839557856\n ],\n [\n -77.1185302734375,\n 42.25291778330197\n ],\n [\n -78.88732910156249,\n 42.92827401776912\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
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30 Brown Road
Ithaca, NY 14850
Or visit our Web site at: http://ny.water.usgs.gov
","tableOfContents":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road, Suite 129
Columbia, SC 29210
Digital geospatial datasets of the extents and top elevations of the regional hydrogeologic units of the Northern Atlantic Coastal Plain aquifer system from Long Island, New York, to northeastern North Carolina were developed to provide an updated hydrogeologic framework to support analysis of groundwater resources. The 19 regional hydrogeologic units were delineated by elevation grids and extent polygons for 20 layers: the land and bathymetric surface at the top of the unconfined surficial aquifer, the upper surfaces of 9 confined aquifers and 9 confining units, and the bedrock surface that defines the base of all Northern Atlantic Coastal Plain sediments. The delineation of the regional hydrogeologic units relied on the interpretive work from source reports for New York, New Jersey, Delaware and Maryland, Virginia, and North Carolina rather than from re-analysis of fundamental hydrogeologic data. This model of regional hydrogeologic unit geometries represents interpolation, extrapolation, and generalization of the earlier interpretive work. Regional units were constructed from available digital data layers from the source studies in order to extend units consistently across political boundaries and approximate units in offshore areas.
Though many of the Northern Atlantic Coastal Plain hydrogeologic units may extend eastward as far as the edge of the Atlantic Continental Shelf, the modeled boundaries of all regional hydrogeologic units in this study were clipped to an area approximately defined by the furthest offshore extent of fresh to brackish water in any part of the aquifer system, as indicated by chloride concentrations of 10,000 milligrams per liter. Elevations and extents of units that do not exist onshore in Long Island, New York, were not included north of New Jersey. Hydrogeologic units in North Carolina were included primarily to provide continuity across the Virginia-North Carolina State boundary, which was important for defining the southern edge of the Northern Atlantic Coastal Plain study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds996","usgsCitation":"Pope, J.P., Andreasen, D.C., McFarland, E.R., and Watt, M.K., 2016, Digital elevations and extents of regional hydrogeologic units in the Northern Atlantic Coastal Plain aquifer system from Long Island, New York, to North Carolina (ver. 1.1, December 2020): U.S. Geological Survey Data Series 996, 28 p., https://doi.org/10.3133/ds996.","productDescription":"Report: vi, 28 p.; Data Releases","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-069216","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":326342,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20165076","text":"Scientific Investigations Report 2016–5076","linkHelpText":"- Documentation of a Groundwater Flow Model Developed To Assess Groundwater Availability in the Northern Atlantic Coastal Plain Aquifer System From Long Island, New York, to North Carolina"},{"id":326339,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/fs20163046","text":"Fact Sheet 2016–3046","linkHelpText":"- Sustainability of Groundwater Supplies in the Northern Atlantic Coastal Plain Aquifer System"},{"id":326341,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20165034","text":"Scientific Investigations Report 2016–5034","linkHelpText":"- Regional Chloride Distribution in the Northern Atlantic Coastal Plain Aquifer System From Long Island, New York, to North Carolina"},{"id":326340,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/pp1829","text":"Professional Paper 1829","linkHelpText":"- Assessment of Groundwater Availability in the Northern Atlantic Coastal Plain Aquifer System From Long Island, New York, to North Carolina"},{"id":381387,"rank":10,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/ds/0996/versionHist.txt","size":"810 B","linkFileType":{"id":2,"text":"txt"}},{"id":327887,"rank":9,"type":{"id":18,"text":"Project Site"},"url":"https://water.usgs.gov/wausp/","text":"USGS Water Availability and Use Science Program"},{"id":326873,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F70V89WN","text":"USGS data release","linkHelpText":"Digital elevations and extents of hydrogeologic units"},{"id":326872,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7MG7MKR","text":"USGS data release","linkHelpText":"MODFLOW-NWT model"},{"id":326337,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0996/coverthb2.jpg"},{"id":326338,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0996/ds996.pdf","text":"Report","size":"11.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 996"}],"country":"United States","state":"Delaware, Maryland, New Jersey, New York, North Carolina, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.71875,\n 41.244772343082104\n ],\n [\n -72.861328125,\n 41.22824901518532\n ],\n [\n -73.93798828125,\n 40.830436877649255\n ],\n [\n -75.78369140625,\n 39.707186656826565\n ],\n [\n -77.080078125,\n 38.94232097947902\n ],\n [\n -77.62939453125,\n 38.39333888832238\n ],\n [\n -77.62939453125,\n 37.56199695314352\n ],\n [\n -77.5634765625,\n 36.82687474287728\n ],\n [\n -78.02490234375,\n 35.88905007936091\n ],\n [\n -75.6298828125,\n 34.63320791137959\n ],\n [\n -74.4873046875,\n 36.06686213257888\n ],\n [\n -71.103515625,\n 40.64730356252251\n ],\n [\n -71.71875,\n 41.244772343082104\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: August 31, 2016; Version 1.1: December 17, 2020","contact":"Water Availability and Use Science Program
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https://www.usgs.gov/water-resources/water
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The North Carolina Turnpike Authority, a division of the North Carolina Department of Transportation, is planning to make transportation improvements in the Currituck Sound area by constructing a two-lane bridge from U.S. Highway 158 just south of Coinjock, North Carolina, to State Highway 12 on the Outer Banks just south of Corolla, North Carolina. The results of the Final Environmental Impact Study associated with the bridge and existing roadway improvements indicated potential water-quality and habitat impacts to Currituck Sound related to stormwater runoff, altered light levels, introduction of piles as hard substrate, and localized turbidity and siltation during construction.
\nThe primary objective of this initial study phase was to establish baseline water-quality conditions and bed-sediment chemistry of Currituck Sound in the vicinity of the planned alignment of the Mid-Currituck Bridge. These data will be used to evaluate the impacts associated with the bridge construction and bridge deck stormwater runoff. Between 2011 and 2015, discrete water-quality samples were collected monthly and after selected storm events from five locations in Currituck Sound. The sampling locations were distributed along the proposed alignment of the Mid-Currituck Bridge. Water samples were analyzed for physical parameters and water-quality constituents associated with bridge deck stormwater runoff and important in identifying impaired waters designated as “SC” (saltwater-aquatic life propagation/ protection and secondary recreation) under North Carolina’s water-quality classifications. Bed-sediment chemistry was also measured three times during the study at the five sampling locations. Continuous water-level and wind speed and direction data in Currituck Sound were also collected by the U.S. Geological Survey during the study period.
\nFor the water samples, measured concentrations were greater than water-quality thresholds on 52 occasions. In addition, there were 190 occurrences of censored results having a reporting level higher than specific thresholds. All 52 occurrences of concentrations greater than water-quality thresholds were confined to seven different physical properties or constituents, namely pH (25), turbidity (8), total recoverable chromium (6), total recoverable copper (6), dissolved copper (3), total recoverable mercury (2), and total recoverable nickel (2). Concentrations of 17 other constituents were never measured to be greater than their established water-quality thresholds during the study.
\nThe focus of the water-quality characterization was on concentrations of constituents identified as parameters of concern in a 2011 collaborative U.S. Geological Survey/North Carolina Department of Transportation study that characterized bridge deck stormwater runoff across North Carolina. The occurrence and distribution of parameters of concern identified in the 2011 study, including pH, nutrients, total recoverable and dissolved metals, and polycyclic aromatic hydrocarbons, and some additional pertinent physical properties (dissolved oxygen, specific conductance, and turbidity), were analyzed in water and bed-sediment samples. Statistical differences were identified between monthly and storm samples for the following physical properties and constituents: pH, dissolved oxygen, specific conductance, turbidity, Escherichia coli bacteria, total recoverable aluminum, and total recoverable iron. Seasonality was observed in pH, specific conductance, dissolved oxygen, turbidity, total phosphorus, and total nitrogen, and total recoverable aluminum, arsenic, iron, lead, and manganese during the study period. The volume and residence time of the water in Currituck Sound are such that the water chemistry is relatively uniform spatially, but variable temporally.
\nOne of the most variable constituents in bed sediments was the fraction of fines, those sediments less than 63 micrometers in diameter. Because most, if not all, of the measured constituents were presumably associated with this fraction, bulk-sediment concentrations are determined largely by the amount of fines present. Only four constituents were greater than bed-sediment thresholds: tin (5 samples), barium (4 samples), aluminum (2 samples), and diethyl phthalate (1 sample). The occurrences of concentrations being greater than referenced thresholds could be underestimated for diethyl phthalate, because the reporting level exceeded the threshold in nine samples. Thirty-five constituents had sampled concentrations that were never greater than quality thresholds, although 21 of these constituents (154 instances) had at least one sample with a reporting level that was greater than the corresponding threshold. Finally, 33 constituents had no quality thresholds.
\nThis study and previous studies of bed-sediment quality in Currituck Sound, although few, indicate that sedimentation in Currituck Sound near the proposed alignment of the MidCurrituck Bridge is characterized overall by low and transient input, frequent wind-driven resuspension, and little long-term deposition of fines. To the extent that it might alter water depths along the alignment, bridge construction might also alter the deposition and resuspension rates of fine sediments in Currituck Sound in the vicinity of the bridge.
\nThe characterization of water-quality and bed-sediment chemistry in Currituck Sound along the proposed alignment of the Mid-Currituck Bridge summarized herein provides a baseline for determining the effect of bridge construction and bridge deck runoff on environmental conditions in Currituck Sound.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151208","collaboration":"Prepared in cooperation with the North Carolina Turnpike Authority","usgsCitation":"Wagner, Chad, Fitzgerald, Sharon, and Antolino, Dominick, 2016, Characterization of water-quality and bed-sediment conditions in Currituck Sound, North Carolina, prior to the Mid-Currituck Bridge construction, 2011–15 (ver. 1.1, July 2016): U.S. Geological Survey Open-File Report 2015–1208, 84 p., https://dx.doi.org/10.3133/ofr20151208.","productDescription":"Report: viii, 84 p.; 2 Appendixes","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-066575","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":324820,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2015/1208//versionHist.txt","size":"1.11 KB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2015-1208"},{"id":312524,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1208/ofr20151208_appendix2.xlsx","text":"Appendix 2","size":"38.3 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2015-1208"},{"id":312523,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1208/ofr20151208_appendix1.xlsx","text":"Appendix 1","size":"285 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2015-1208"},{"id":312522,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1208/ofr20151208.pdf","text":"Report","size":"2.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1208"},{"id":312521,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1208/coverthb1.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Currituck Sound, Mid-Currituck Bridge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.4,\n 35.7\n ],\n [\n -76.4,\n 36.75\n ],\n [\n -75.4,\n 36.75\n ],\n [\n -75.4,\n 35.7\n ],\n [\n -76.4,\n 35.7\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1:Originally posted December 24, 2015; Version 1.1: July 8, 2016;","publicComments":"Open-File Report 2015-1208 is superseded by Open-File Report 2020-1031","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
http://www.usgs.gov/water/southatlantic/
Trends in long-term water-quality and streamflow data from 14 water-quality monitoring sites in Connecticut were evaluated for water years 1974–2013 and 2001–13, coinciding with implementation of the Clean Water Act of 1972 and the Connecticut Nitrogen Credit Exchange program, as part of an assessment of nutrient and chloride concentrations and loads discharged to Long Island Sound. In this study, conducted by the U.S. Geological Survey in cooperation with the Connecticut Department of Energy and Environmental Protection, data were evaluated using a recently developed methodology of weighted regressions with time, streamflow, and season. Trends in streamflow were evaluated using a locally weighted scatterplot smoothing method. Annual mean streamflow increased at 12 of the 14 sites averaging 8 percent during the entire study period, primarily in the summer months, and increased by an average of 9 percent in water years 2001–13, primarily during summer and fall months. Downward trends in flow-normalized nutrient concentrations and loads were observed during both periods for most sites for total nitrogen, total Kjeldahl nitrogen, nitrite plus nitrate nitrogen, total phosphorus, and total organic carbon. Average flow-normalized loads of total nitrogen decreased by 23.9 percent for the entire period and 10.9 percent for the period of water years 2001‒13. Major factors contributing to decreases in flow-normalized loads and concentrations of these nutrients include improvements in wastewater treatment practices, declining atmospheric wet deposition of nitrogen, and changes in land management and land use.
\nLoads of dissolved silica (DSi; flow-normalized and non-flow-normalized) increased slightly at most stations during the study period and were positively correlated to urbanized land in the basin and negatively correlated to area of open water. Concentrations and loads of chloride increased at 12 of the 14 sites during both periods. Increases likely are the result of an increase in the use of salt for deicing, as well as other factors related to urbanization and population growth, such as increases in wastewater discharge and discharge from septic systems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155189","collaboration":"Prepared in cooperation with the Connecticut Department of Energy and Environmental Protection","usgsCitation":"Mullaney, J.R., 2016, Nutrient, organic carbon, and chloride concentrations and loads in selected Long Island Sound tributaries—Four decades of change following the passage of the Federal Clean Water Act: U.S. Geological Survey Scientific Investigations Report 2015–5189, 47 p., https://dx.doi.org/10.3133/sir20155189.","productDescription":"Report: viii, 47 p.; Appendixes: 2.1-2.7","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-068448","costCenters":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"links":[{"id":318574,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5189/downloads/sir20155189_appendix2-7.xlsx","text":"Appendix 2.7","size":"43 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix 2.7 SIR 2015-5189","linkHelpText":"- Total chloride results"},{"id":318573,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5189/downloads/sir20155189_appendix2-6.xlsx","text":"Appendix 2.6","size":"43 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix 2.6 SIR 2015-5189","linkHelpText":"- Total dissolved silica results"},{"id":318566,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5189/coverthb2.jpg"},{"id":318568,"rank":2,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5189/downloads/sir20155189_appendix2-1.xlsx","text":"Appendix 2.1","size":"46 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix 2.1 SIR 2015-5189","linkHelpText":"- Total nitrogen results"},{"id":371144,"rank":9,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5189/sir20155189.pdf","text":"Report","size":"2.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5189"},{"id":318569,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5189/downloads/sir20155189_appendix2-2.xlsx","text":"Appendix 2.2","size":"44 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix 2.2 SIR 2015-5189","linkHelpText":"- Total Kjeldahl nitrogen results"},{"id":318570,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5189/downloads/sir20155189_appendix2-3.xlsx","text":"Appendix 2.3","size":"46 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix 2.3 SIR 2015-5189","linkHelpText":"- Nitrite plus nitrate nitrogen results"},{"id":318571,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5189/downloads/sir20155189_appendix2-4.xlsx","text":"Appendix 2.4","size":"45 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix 2.4 SIR 2015-5189","linkHelpText":"- Total phosphorus results"},{"id":318572,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5189/downloads/sir20155189_appendix2-5.xlsx","text":"Appendix 2.5","size":"43 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix 2.5 SIR 2015-5189","linkHelpText":"- Total organic carbon results"}],"country":"United States","state":"Connecticut","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.47381591796875,\n 41.0772807426254\n ],\n [\n -73.48480224609375,\n 41.31288691435732\n ],\n [\n -73.2183837890625,\n 41.7672146942102\n ],\n [\n -72.94647216796874,\n 41.97378534488489\n ],\n [\n -72.7459716796875,\n 42.002366213375524\n ],\n [\n -72.5372314453125,\n 42.01665183556825\n ],\n [\n -71.9549560546875,\n 42.02889410108475\n ],\n [\n -71.90277099609375,\n 41.781552998900345\n ],\n [\n -71.96319580078125,\n 41.32732632036622\n ],\n [\n -72.22137451171874,\n 41.292253642159466\n ],\n [\n -72.476806640625,\n 41.263356094059326\n ],\n [\n -72.63885498046875,\n 41.263356094059326\n ],\n [\n -72.75970458984375,\n 41.25716209782705\n ],\n [\n -72.88330078125,\n 41.24890252240322\n ],\n [\n -72.9766845703125,\n 41.24270715552139\n ],\n [\n -73.26507568359375,\n 41.11246878918086\n ],\n [\n -73.465576171875,\n 41.05035951931887\n ],\n [\n -73.47381591796875,\n 41.0772807426254\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
101 Pitkin Street
East Hartford, CT 06108
Or visit our Web site at
http://newengland.water.usgs.gov/
Geospatial data, often embedded with geographic references, are important to many application and science domains, and represent a major type of big data. The increased volume and diversity of geospatial data have caused serious usability issues for researchers in various scientific domains, which call for innovative cyberGIS solutions. To address these issues, this paper describes a cyberGIS community data service framework to facilitate geospatial big data access, processing, and sharing based on a hybrid supercomputer architecture. Through the collaboration between the CyberGIS Center at the University of Illinois at Urbana-Champaign (UIUC) and the U.S. Geological Survey (USGS), a community data service for accessing, customizing, and sharing digital elevation model (DEM) and its derived datasets from the 10-meter national elevation dataset, namely TopoLens, is created to demonstrate the workflow integration of geospatial big data sources, computation, analysis needed for customizing the original dataset for end user needs, and a friendly online user environment. TopoLens provides online access to precomputed and on-demand computed high-resolution elevation data by exploiting the ROGER supercomputer. The usability of this prototype service has been acknowledged in community evaluation.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"ACM","doi":"10.1145/2949550.2949652","usgsCitation":"Hu, H., Hong, X., Terstriep, J., Liu, Y., Finn, M.P., Rush, J., Wendel, J., and Wang, S., 2016, TopoLens: Building a cyberGIS community data service for enhancing the usability of high-resolution National Topographic datasets, in Proceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale, 8 p., https://doi.org/10.1145/2949550.2949652.","productDescription":"8 p.","ipdsId":"IP-075407","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":470253,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1145/2949550.2949652","text":"Publisher Index Page"},{"id":352022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-17","publicationStatus":"PW","scienceBaseUri":"5afee916e4b0da30c1bfc516","contributors":{"authors":[{"text":"Hu, Hao","contributorId":198962,"corporation":false,"usgs":false,"family":"Hu","given":"Hao","email":"","affiliations":[],"preferred":false,"id":717701,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hong, Xingchen","contributorId":198963,"corporation":false,"usgs":false,"family":"Hong","given":"Xingchen","email":"","affiliations":[],"preferred":false,"id":717702,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Terstriep, Jeff","contributorId":198964,"corporation":false,"usgs":false,"family":"Terstriep","given":"Jeff","email":"","affiliations":[],"preferred":false,"id":717703,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Liu, Yan 0000-0003-2298-4728","orcid":"https://orcid.org/0000-0003-2298-4728","contributorId":196790,"corporation":false,"usgs":false,"family":"Liu","given":"Yan","email":"","affiliations":[],"preferred":false,"id":717704,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Finn, Michael P. 0000-0003-0415-2194 mfinn@usgs.gov","orcid":"https://orcid.org/0000-0003-0415-2194","contributorId":2657,"corporation":false,"usgs":true,"family":"Finn","given":"Michael","email":"mfinn@usgs.gov","middleInitial":"P.","affiliations":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":717700,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rush, Johnathan","contributorId":198965,"corporation":false,"usgs":false,"family":"Rush","given":"Johnathan","email":"","affiliations":[],"preferred":false,"id":717705,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wendel, Jeffrey 0000-0003-0294-0250 jwendel@usgs.gov","orcid":"https://orcid.org/0000-0003-0294-0250","contributorId":196792,"corporation":false,"usgs":true,"family":"Wendel","given":"Jeffrey","email":"jwendel@usgs.gov","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":717706,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wang, Shaowen","contributorId":198966,"corporation":false,"usgs":false,"family":"Wang","given":"Shaowen","email":"","affiliations":[],"preferred":false,"id":717707,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70178400,"text":"ofr20161181 - 2016 - Data from exploratory sampling of groundwater in selected oil and gas areas of coastal Los Angeles County and Kern and Kings Counties in southern San Joaquin Valley, 2014–15: California oil, gas, and groundwater project","interactions":[],"lastModifiedDate":"2017-11-27T10:38:25","indexId":"ofr20161181","displayToPublicDate":"2017-11-21T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1181","title":"Data from exploratory sampling of groundwater in selected oil and gas areas of coastal Los Angeles County and Kern and Kings Counties in southern San Joaquin Valley, 2014–15: California oil, gas, and groundwater project","docAbstract":"Exploratory sampling of groundwater in coastal Los Angeles County and Kern and Kings Counties of the southern San Joaquin Valley was done by the U.S. Geological Survey from September 2014 through January 2015 as part of the California State Water Resources Control Board’s Water Quality in Areas of Oil and Gas Production Regional Groundwater Monitoring Program. The Regional Groundwater Monitoring Program was established in response to the California Senate Bill 4 of 2013 mandating that the California State Water Resources Control Board design and implement a groundwater-monitoring program to assess potential effects of well-stimulation treatments on groundwater resources in California. The U.S. Geological Survey is in cooperation with the California State Water Resources Control Board to collaboratively implement the Regional Groundwater Monitoring Program through the California Oil, Gas, and Groundwater Project. Many researchers have documented the utility of different suites of chemical tracers for evaluating the effects of oil and gas development on groundwater quality. The purpose of this exploratory sampling effort was to determine whether tracers reported in the literature could be used effectively in California. This reconnaissance effort was not designed to assess the effects of oil and gas on groundwater quality in the sampled areas. A suite of water-quality indicators and geochemical tracers were sampled at groundwater sites in selected areas that have extensive oil and gas development. Groundwater samples were collected from a total of 51 wells, including 37 monitoring wells at 17 multiple-well monitoring sites in coastal Los Angeles County and 5 monitoring wells and 9 water-production wells in southern San Joaquin Valley, primarily in Kern and Kings Counties. Groundwater samples were analyzed for field waterquality indicators; organic constituents, including volatile and semi-volatile organic compounds and dissolved organic carbon indicators; naturally present inorganic constituents, including trace elements, nutrients, major and minor ions, and iron species; naturally present stable and radioactive isotopes; dissolved noble gases; dissolved standard and hydrocarbon gases, δ13C of methane, ethane, and δ2 H of methane. In total, 249 constituents and water-quality indicators were measured. Four types of quality-control samples (blanks, replicates, matrix spikes, and surrogates spiked in environmental and blank samples) were collected at approximately 10 percent of the wells. The quality-control data were used to determine whether the groundwater-sample data were of sufficient quality for the measured analytes to be used as potential indicators of oil and gas effects. The data from the 51 groundwater samples and from the quality-control samples are presented in this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161181","collaboration":"A product of the California Oil and Gas Regional Groundwater Monitoring ProgramDirector, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Variations in seabed gradient are widely acknowledged to influence deep-water deposition, but are often difficult to measure in sufficient detail from both modern and ancient examples. On the continental slope offshore Los Angeles, California, autonomous underwater vehicle, remotely operated vehicle, and shipboard methods were used to collect a dense grid of high-resolution multibeam bathymetry, chirp sub-bottom profiles, and targeted sediment core samples that demonstrate the influence of seafloor gradient on sediment accumulation, depositional environment, grain size of deposits, and seafloor morphology. In this setting, restraining and releasing bends along the active right-lateral Palos Verdes Fault create and maintain variations in seafloor gradient. Holocene down-slope flows appear to have been generated by slope failure, primarily on the uppermost slope (~ 100–200 m water depth). Turbidity currents created a low relief (< 10 m) channel, up-slope migrating sediment waves (λ = ~ 100 m, h ≤ 2 m), and a series of depocenters that have accumulated up to 4 m of Holocene sediment. Sediment waves increase in wavelength and decrease in wave height with decreasing gradient. Integrated analysis of high-resolution datasets provides quantification of morphodynamic sensitivity to seafloor gradients acting throughout deep-water depositional systems. These results help to bridge gaps in scale between existing deep-sea and experimental datasets and may provide constraints for future numerical modeling studies.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.margeo.2016.10.001","usgsCitation":"Maier, K., Brothers, D.S., Paull, C.K., McGann, M., Caress, D.W., and Conrad, J.E., 2016, Records of continental slope sediment flow morphodynamic responses to gradient and active faulting from integrated AUV and ROV data, offshore Palos Verdes, southern California Borderland: Marine Geology, v. 393, p. 47-66, https://doi.org/10.1016/j.margeo.2016.10.001.","productDescription":"20 p.","startPage":"47","endPage":"66","ipdsId":"IP-074023","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470255,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.margeo.2016.10.001","text":"Publisher Index Page"},{"id":344624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"393","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59897c15e4b09fa1cb0c2c0c","contributors":{"authors":[{"text":"Maier, Katherine L.","contributorId":91411,"corporation":false,"usgs":true,"family":"Maier","given":"Katherine L.","affiliations":[],"preferred":false,"id":707301,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brothers, Daniel S. 0000-0001-7702-157X dbrothers@usgs.gov","orcid":"https://orcid.org/0000-0001-7702-157X","contributorId":167089,"corporation":false,"usgs":true,"family":"Brothers","given":"Daniel","email":"dbrothers@usgs.gov","middleInitial":"S.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":707302,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paull, Charles K. 0000-0001-5940-3443","orcid":"https://orcid.org/0000-0001-5940-3443","contributorId":55825,"corporation":false,"usgs":false,"family":"Paull","given":"Charles","email":"","middleInitial":"K.","affiliations":[{"id":7043,"text":"University of North Carolina","active":true,"usgs":false}],"preferred":true,"id":707303,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McGann, Mary 0000-0002-3057-2945 mmcgann@usgs.gov","orcid":"https://orcid.org/0000-0002-3057-2945","contributorId":169540,"corporation":false,"usgs":true,"family":"McGann","given":"Mary","email":"mmcgann@usgs.gov","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":707304,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Caress, David W.","contributorId":147392,"corporation":false,"usgs":false,"family":"Caress","given":"David","email":"","middleInitial":"W.","affiliations":[{"id":16837,"text":"MBARI","active":true,"usgs":false}],"preferred":false,"id":707305,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Conrad, James E. 0000-0001-6655-694X jconrad@usgs.gov","orcid":"https://orcid.org/0000-0001-6655-694X","contributorId":2316,"corporation":false,"usgs":true,"family":"Conrad","given":"James","email":"jconrad@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":707306,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70189626,"text":"70189626 - 2016 - An investigation of soil-structure interaction effects observed at the MIT Green Building","interactions":[],"lastModifiedDate":"2017-07-19T10:40:23","indexId":"70189626","displayToPublicDate":"2017-07-19T00:00:00","publicationYear":"2016","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":"An investigation of soil-structure interaction effects observed at the MIT Green Building","docAbstract":"The soil-foundation impedance function of the MIT Green Building is identified from its response signals recorded during an earthquake. Estimation of foundation impedance functions from seismic response signals is a challenging task, because: (1) the foundation input motions (FIMs) are not directly measurable, (2) the as-built properties of the super-structure are only approximately known, and (3) the soil-foundation impedance functions are inherently frequency-dependent. In the present study, aforementioned difficulties are circumvented by using, in succession, a blind modal identification (BMID) method, a simplified Timoshenko beam model (TBM), and a parametric updating of transfer functions (TFs). First, the flexible-base modal properties of the building are identified from response signals using the BMID method. Then, a flexible-base TBM is updated using the identified modal data. Finally, the frequency-dependent soil-foundation impedance function is estimated by minimizing the discrepancy between TFs (of pairs instrumented floors) that are (1) obtained experimentally from earthquake data and (2) analytically from the updated TBM. Using the fully identified flexible-base TBM, the FIMs as well as building responses at locations without instruments can be predicted, as demonstrated in the present study.
","language":"English","publisher":"Earthquake Engineering Research Institute","doi":"10.1193/072215EQS118M","usgsCitation":"Taciroglu, E., Çelebi, M., Ghahari, S.F., and Abazarsa, F., 2016, An investigation of soil-structure interaction effects observed at the MIT Green Building: Earthquake Spectra, v. 32, no. 4, p. 2425-2448, https://doi.org/10.1193/072215EQS118M.","productDescription":"24 p.","startPage":"2425","endPage":"2448","ipdsId":"IP-067185","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":344009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachussets","city":"Cambridge","otherGeospatial":"MIT Green Building","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.11175537109375,\n 42.34547721740614\n ],\n [\n -71.06609344482422,\n 42.34547721740614\n ],\n [\n -71.06609344482422,\n 42.36704215735293\n ],\n [\n -71.11175537109375,\n 42.36704215735293\n ],\n [\n -71.11175537109375,\n 42.34547721740614\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-01","publicationStatus":"PW","scienceBaseUri":"59706fb6e4b0d1f9f065a88d","contributors":{"authors":[{"text":"Taciroglu, Ertugrul","contributorId":176616,"corporation":false,"usgs":false,"family":"Taciroglu","given":"Ertugrul","email":"","affiliations":[],"preferred":false,"id":705484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Çelebi, Mehmet 0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":3205,"corporation":false,"usgs":true,"family":"Çelebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":705483,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ghahari, S. Farid","contributorId":168417,"corporation":false,"usgs":false,"family":"Ghahari","given":"S.","email":"","middleInitial":"Farid","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":705485,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Abazarsa, Fariba","contributorId":176615,"corporation":false,"usgs":false,"family":"Abazarsa","given":"Fariba","email":"","affiliations":[],"preferred":false,"id":705486,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188794,"text":"70188794 - 2016 - PhasePApy: A robust pure Python package for automatic identification of seismic phases","interactions":[],"lastModifiedDate":"2017-06-27T13:11:00","indexId":"70188794","displayToPublicDate":"2017-06-23T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"PhasePApy: A robust pure Python package for automatic identification of seismic phases","docAbstract":"We developed a Python phase identification package: the PhasePApy for earthquake data processing and near‐real‐time monitoring. The package takes advantage of the growing number of Python libraries including Obspy. All the data formats supported by Obspy can be supported within the PhasePApy. The PhasePApy has two subpackages: the PhasePicker and the Associator, aiming to identify phase arrival onsets and associate them to phase types, respectively. The PhasePicker and the Associator can work jointly or separately. Three autopickers are implemented in the PhasePicker subpackage: the frequency‐band picker, the Akaike information criteria function derivative picker, and the kurtosis picker. All three autopickers identify picks with the same processing methods but different characteristic functions. The PhasePicker triggers the pick with a dynamic threshold and can declare a pick with false‐pick filtering. Also, the PhasePicker identifies a pick polarity and uncertainty for further seismological analysis, such as focal mechanism determination. Two associators are included in the Associator subpackage: the 1D Associator and 3D Associator, which assign phase types to picks that can best fit potential earthquakes by minimizing root mean square (rms) residuals of the misfits in distance and time, respectively. The Associator processes multiple picks from all channels at a seismic station and aggregates them to increase computational efficiencies. Both associators use travel‐time look up tables to determine the best estimation of the earthquake location and evaluate the phase type for picks. The PhasePApy package has been used extensively for local and regional earthquakes and can work for active source experiments as well.
","language":"English","publisher":" Seismological Society of America","doi":"10.1785/0220160019","usgsCitation":"Chen, C., and Holland, A., 2016, PhasePApy: A robust pure Python package for automatic identification of seismic phases: Seismological Research Letters, v. 87, no. 6, p. 1384-1396, https://doi.org/10.1785/0220160019.","productDescription":"13 p.","startPage":"1384","endPage":"1396","ipdsId":"IP-076924","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":342828,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"87","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-31","publicationStatus":"PW","scienceBaseUri":"594e28b0e4b062508e3abe0f","contributors":{"authors":[{"text":"Chen, Chen","contributorId":193408,"corporation":false,"usgs":false,"family":"Chen","given":"Chen","email":"","affiliations":[],"preferred":false,"id":700385,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holland, Austin 0000-0002-7843-1981 aaholland@usgs.gov","orcid":"https://orcid.org/0000-0002-7843-1981","contributorId":173969,"corporation":false,"usgs":true,"family":"Holland","given":"Austin","email":"aaholland@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":700386,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70161898,"text":"70161898 - 2016 - Assessing the seismic risk potential of South America","interactions":[],"lastModifiedDate":"2017-04-25T10:36:02","indexId":"70161898","displayToPublicDate":"2017-04-25T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"title":"Assessing the seismic risk potential of South America","docAbstract":"We present here a simplified approach to quantifying regional seismic risk. The seismic risk for a given region can be inferred in terms of average annual loss (AAL) that represents long-term value of earthquake losses in any one year caused from a long-term seismic hazard. The AAL are commonly measured in the form of earthquake shaking-induced deaths, direct economic impacts or indirect losses caused due to loss of functionality. In the context of South American subcontinent, the analysis makes use of readily available public data on seismicity, population exposure, and the hazard and vulnerability models for the region. The seismic hazard model was derived using available seismic catalogs, fault databases, and the hazard methodologies that are analogous to the U.S. Geological Survey’s national seismic hazard mapping process. The Prompt Assessment of Global Earthquakes for Response (PAGER) system’s direct empirical vulnerability functions in terms of fatality and economic impact were used for performing exposure and risk analyses. The broad findings presented and the risk maps produced herein are preliminary, yet they do offer important insights into the underlying zones of high and low seismic risks in the South American subcontinent. A more detailed analysis of risk may be warranted by engaging local experts, especially in some of the high risk zones identified through the present investigation.
","language":"English","publisher":"European Association for Earthquake Engineering","usgsCitation":"Jaiswal, K.S., Petersen, M.D., Harmsen, S., and Smoczyk, G.M., 2016, Assessing the seismic risk potential of South America, 12 p.","productDescription":"12 p.","ipdsId":"IP-056093","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":340238,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":340237,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.eaee.org/proceedings-of-2ecces-eaee-sessions"}],"otherGeospatial":"South America","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59006063e4b0e85db3a5ddd5","contributors":{"authors":[{"text":"Jaiswal, Kishor S. 0000-0002-5803-8007 kjaiswal@usgs.gov","orcid":"https://orcid.org/0000-0002-5803-8007","contributorId":149796,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":588068,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Petersen, Mark D. 0000-0001-8542-3990 mpetersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8542-3990","contributorId":1163,"corporation":false,"usgs":true,"family":"Petersen","given":"Mark","email":"mpetersen@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":588069,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harmsen, Stephen harmsen@usgs.gov","contributorId":152128,"corporation":false,"usgs":true,"family":"Harmsen","given":"Stephen","email":"harmsen@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":588070,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smoczyk, Gregory M. 0000-0002-6591-4060 gsmoczyk@usgs.gov","orcid":"https://orcid.org/0000-0002-6591-4060","contributorId":5239,"corporation":false,"usgs":true,"family":"Smoczyk","given":"Gregory","email":"gsmoczyk@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":588071,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70174150,"text":"70174150 - 2016 - The Impacts of flow alterations to crayfishes in Southeastern Oklahoma, with an emphasis on the mena crayfish (orconectes menae)","interactions":[],"lastModifiedDate":"2017-04-19T14:18:14","indexId":"70174150","displayToPublicDate":"2017-04-19T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesNumber":"105-2014","title":"The Impacts of flow alterations to crayfishes in Southeastern Oklahoma, with an emphasis on the mena crayfish (orconectes menae)","docAbstract":"Human activities can alter the environment to the point that it is unsuitable to the native species resulting in a loss of biodiversity. Ecologists understand the importance of biodiversity and the conservation of vulnerable species. Species that are narrowly endemic are considered to be particularly vulnerable because they often use specific habitats that are highly susceptible to human disturbance. The basic components of species conservation are 1) delineation of the spatial distribution of the species, 2) understanding how the species interacts with its environment, and 3) employing management strategies based on the ecology of the species. In this study, we investigated several crayfish species endemic to the Ouachita Mountains in Oklahoma and Arkansas. We established the spatial distributions (i.e., range) of the crayfish using Maximum Entropy species distribution modeling. We then investigated crayfish habitat use with quantitative sampling and a paired movement study. Finally, we evaluated the ability of crayfish to burrow under different environmental conditions in a controlled laboratory setting. Crayfish distribution at the landscape scale was largely driven by climate, geology and elevation. In general, the endemic crayfish in this study occurred above 300-m elevation where the geology was dominated by sandstone and shale, and rainfall totals were the highest compared to the rest of the study region. Our quantitative data indicated crayfish did not select for specific habitat types at the reach scale; however, crayfish appeared to continue to use shallow and dry habitat even as the streams dried. Movement by passive integrated transponder (PIT) tagged crayfish was highly variable but crayfish tended to burrow in response to drought rather than migrate to wet habitat. Controlled laboratory experiments revealed smaller substrate size (pebble) restricted crayfish burrowing more than larger substrates (cobble). We also found excess fine sediment restricted crayfish burrowing regardless of dominant substrate size. Our results suggest climate change and sedimentation resulting from land-use practices, combined with increased water withdrawals have the potential to alter crayfish distributions and affect persistence of some crayfish populations.
","language":"English","publisher":"U.S. Fish and Wildlife Service","publisherLocation":"OK","usgsCitation":"Brewer, S.K., and Dyer, J.J., 2016, The Impacts of flow alterations to crayfishes in Southeastern Oklahoma, with an emphasis on the mena crayfish (orconectes menae), ii, 103 p.","productDescription":"ii, 103 p.","ipdsId":"IP-054991","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":339982,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":339981,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://digitalmedia.fws.gov/cdm/ref/collection/document/id/2056"}],"country":"United States","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58f877b2e4b0b7ea54521c0b","contributors":{"authors":[{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":640997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dyer, Joseph J.","contributorId":140681,"corporation":false,"usgs":false,"family":"Dyer","given":"Joseph","email":"","middleInitial":"J.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":692197,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70177839,"text":"70177839 - 2016 - A comparison of NLCD 2011 and LANDFIRE EVT 2010: Regional and national summaries.","interactions":[],"lastModifiedDate":"2018-12-20T11:47:06","indexId":"70177839","displayToPublicDate":"2017-04-18T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"A comparison of NLCD 2011 and LANDFIRE EVT 2010: Regional and national summaries.","docAbstract":"In order to provide the land cover user community a summary of the similarity and differences between the 2011 National Land Cover Dataset (NLCD) and the Landscape Fire and Resource Management Planning Tools Program Existing Vegetation 2010 Data (LANDFIRE EVT), the two datasets were compared at a national (conterminous U.S.) and regional (Eastern, Midwestern, and Western) extents (Figure 1). The comparisons were done by generalizing the LANDFIRE data to be consistent with mapped land cover classes in the NLCD (i.e., crosswalked). Summaries of the comparisons were based on areal extent including 1) the total extent of each land cover class, and 2) land cover classes in corresponding 900-m2 areas. The results from the comparisons provide the user community information regarding the utility of both datasets relative to their intended uses.","language":"English","publisher":"LANDFIRE","usgsCitation":"McKerrow, A., Dewitz, J., Long, D.G., Nelson, K., Connot, J.A., and Smith, J., 2016, A comparison of NLCD 2011 and LANDFIRE EVT 2010: Regional and national summaries., 29 p.","productDescription":"29 p.","ipdsId":"IP-073998","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true},{"id":38315,"text":"GAP Analysis Project","active":true,"usgs":true}],"links":[{"id":339858,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":330338,"type":{"id":15,"text":"Index Page"},"url":"https://landfiredev.cr.usgs.gov/lfpartner_collaborations.php"}],"country":"United States","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58f725e6e4b0b7ea5451eec8","contributors":{"authors":[{"text":"McKerrow, Alexa 0000-0002-8312-2905 amckerrow@usgs.gov","orcid":"https://orcid.org/0000-0002-8312-2905","contributorId":127753,"corporation":false,"usgs":true,"family":"McKerrow","given":"Alexa","email":"amckerrow@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":651906,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dewitz, Jon 0000-0002-0458-212X dewitz@usgs.gov","orcid":"https://orcid.org/0000-0002-0458-212X","contributorId":2401,"corporation":false,"usgs":true,"family":"Dewitz","given":"Jon","email":"dewitz@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":651907,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Long, Donald G.","contributorId":167066,"corporation":false,"usgs":false,"family":"Long","given":"Donald","email":"","middleInitial":"G.","affiliations":[{"id":6679,"text":"US Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":651908,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nelson, Kurtis 0000-0003-4911-4511 knelson@usgs.gov","orcid":"https://orcid.org/0000-0003-4911-4511","contributorId":3602,"corporation":false,"usgs":true,"family":"Nelson","given":"Kurtis","email":"knelson@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":691664,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Connot, Joel A. 0000-0002-2556-3374 jconnot@usgs.gov","orcid":"https://orcid.org/0000-0002-2556-3374","contributorId":4436,"corporation":false,"usgs":true,"family":"Connot","given":"Joel","email":"jconnot@usgs.gov","middleInitial":"A.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":691665,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, Jim","contributorId":191054,"corporation":false,"usgs":false,"family":"Smith","given":"Jim","email":"","affiliations":[],"preferred":false,"id":691666,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70178429,"text":"70178429 - 2016 - Regional geophysics of western Utah and eastern Nevada, with emphasis on the Confusion Range","interactions":[],"lastModifiedDate":"2017-04-18T10:44:21","indexId":"70178429","displayToPublicDate":"2017-04-18T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":4,"text":"Book"},"title":"Regional geophysics of western Utah and eastern Nevada, with emphasis on the Confusion Range","docAbstract":"As part of a long term geologic and hydrologic study of several regional\ngroundwater flow systems in western Utah and eastern Nevada, the U.S. \nGeological Survey was contracted by the Southern Nevada Water Authority \nto provide geophysical data. The primary object of these data was to enable \nconstruction of the geological framework of the flow systems. The main \nnew geophysical data gathered during the study were gravity observations, \nand existing aeromagnetic data were also compiled. These data resulted in \nregional maps of the isostatic gravity and aeromagnetic fields of the area.\nThe isostatic gravity map shows a north-south grain to most of the area, \nwhich was imparted by post-20 Ma basin-range tectonism; whereas the \naeromagnetic map shows an east-west grain to the area, imparted by \nEocene to lower Miocene calc-alkaline calderas and source intrusions. \nTo de-emphasize surface and near-surface features and to gain greater \ninsight into contributions from deeper sources, the isostatic gravity \nanomalies were upward continued by 3 km and the aeromagnetic data \nwere transformed to their magnetic potential (\"pseudogravity\"). \nIdentification of maxima of the horizontal gradients in the gravity and \nmagnetic-potential data helped define deep-seated crustal blocks that are \ncharacterized by major changes in density and magnetization. Maps \nshowing these maxima were useful in defining large faults, especially \nrange-bounding faults, and margins of igneous bodies and calderas. A \ngravity inversion method was used to separate the isostatic residual anomaly \ninto pre-Cenozoic basement and young basin fill. Inasmuch as the primary \naquifer in the area is sedimentary basin fill, this method is especially valuable\nfor hydrogeologic analyses because it estimates the thickness of the fill.\nAs befits its name, the geology of the Confusion Range of Utah has been a \npoint of contention for many years, so we looked at it in greater detail in the \ncourse of our regional study. The northern part of the range is underlain by a \nlarge gravity high, which continues south through the Conger Range, Burbank \nHills, and northern Mountain Home Range. This is the \"structural trough\" \nreported in the literature that was mapped as the axial part of a Sevier \nsynclinorium and contains the maximum thickness (7 km) of high-density \ncarbonates in the area, thus the largest high gravity anomaly.","language":"English","publisher":"Utah Geological Association","usgsCitation":"Mankinen, E.A., Rowley, P.D., Dixon, G.L., and McKee, E.H., 2016, Regional geophysics of western Utah and eastern Nevada, with emphasis on the Confusion Range, v. 45, 13 p.","productDescription":"13 p.","startPage":"147","endPage":"166","ipdsId":"IP-073281","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":339850,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":339848,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.mapstore.utah.gov/uga45.html"}],"country":"United States","state":"Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.0380859375,\n 42.00032514831621\n ],\n [\n -114.06005859375,\n 36.98500309285596\n ],\n [\n -109.05029296875,\n 36.98500309285596\n ],\n [\n -109.039306640625,\n 41.00477542222947\n ],\n [\n -111.03881835937499,\n 40.9964840143779\n ],\n [\n -111.0498046875,\n 42.00032514831621\n ],\n [\n -114.0380859375,\n 42.00032514831621\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58f725e5e4b0b7ea5451eec4","contributors":{"authors":[{"text":"Mankinen, Edward A. 0000-0001-7496-2681 emank@usgs.gov","orcid":"https://orcid.org/0000-0001-7496-2681","contributorId":1054,"corporation":false,"usgs":true,"family":"Mankinen","given":"Edward","email":"emank@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":691624,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rowley, Peter D.","contributorId":27435,"corporation":false,"usgs":true,"family":"Rowley","given":"Peter","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":691625,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dixon, Gary L.","contributorId":23571,"corporation":false,"usgs":true,"family":"Dixon","given":"Gary","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":691626,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKee, Edwin H. mckee@usgs.gov","contributorId":3728,"corporation":false,"usgs":true,"family":"McKee","given":"Edwin","email":"mckee@usgs.gov","middleInitial":"H.","affiliations":[],"preferred":true,"id":691627,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193676,"text":"70193676 - 2016 - Prediction of lake depth across a 17-state region in the United States","interactions":[],"lastModifiedDate":"2018-01-24T16:07:57","indexId":"70193676","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1999,"text":"Inland Waters","active":true,"publicationSubtype":{"id":10}},"title":"Prediction of lake depth across a 17-state region in the United States","docAbstract":"Lake depth is an important characteristic for understanding many lake processes, yet it is unknown for the vast majority of lakes globally. Our objective was to develop a model that predicts lake depth using map-derived metrics of lake and terrestrial geomorphic features. Building on previous models that use local topography to predict lake depth, we hypothesized that regional differences in topography, lake shape, or sedimentation processes could lead to region-specific relationships between lake depth and the mapped features. We therefore used a mixed modeling approach that included region-specific model parameters. We built models using lake and map data from LAGOS, which includes 8164 lakes with maximum depth (Zmax) observations. The model was used to predict depth for all lakes ≥4 ha (n = 42 443) in the study extent. Lake surface area and maximum slope in a 100 m buffer were the best predictors of Zmax. Interactions between surface area and topography occurred at both the local and regional scale; surface area had a larger effect in steep terrain, so large lakes embedded in steep terrain were much deeper than those in flat terrain. Despite a large sample size and inclusion of regional variability, model performance (R2 = 0.29, RMSE = 7.1 m) was similar to other published models. The relative error varied by region, however, highlighting the importance of taking a regional approach to lake depth modeling. Additionally, we provide the largest known collection of observed and predicted lake depth values in the United States.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/IW-6.3.957","usgsCitation":"Oliver, S., Soranno, P.A., Fergus, C.E., Wagner, T., Winslow, L., Scott, C.E., Webster, K.E., Downing, J., and Stanley, E.H., 2016, Prediction of lake depth across a 17-state region in the United States: Inland Waters, v. 6, no. 3, p. 314-324, https://doi.org/10.1080/IW-6.3.957.","productDescription":"11 p.","startPage":"314","endPage":"324","ipdsId":"IP-071256","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":348693,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"6","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-02","publicationStatus":"PW","scienceBaseUri":"5a60fc5ae4b06e28e9c23da8","contributors":{"authors":[{"text":"Oliver, Samantha K.","contributorId":169273,"corporation":false,"usgs":false,"family":"Oliver","given":"Samantha K.","affiliations":[],"preferred":false,"id":721804,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soranno, Patricia A.","contributorId":172104,"corporation":false,"usgs":false,"family":"Soranno","given":"Patricia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":721805,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fergus, C. Emi","contributorId":150608,"corporation":false,"usgs":false,"family":"Fergus","given":"C.","email":"","middleInitial":"Emi","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":721806,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719862,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Winslow, Luke A. lwinslow@usgs.gov","contributorId":139775,"corporation":false,"usgs":true,"family":"Winslow","given":"Luke A.","email":"lwinslow@usgs.gov","affiliations":[],"preferred":false,"id":721807,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scott, Caren E.","contributorId":172184,"corporation":false,"usgs":false,"family":"Scott","given":"Caren","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":721808,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Webster, Katherine E.","contributorId":147903,"corporation":false,"usgs":false,"family":"Webster","given":"Katherine","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":721809,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Downing, John A.","contributorId":70348,"corporation":false,"usgs":true,"family":"Downing","given":"John A.","affiliations":[],"preferred":false,"id":721810,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stanley, Emily H.","contributorId":55725,"corporation":false,"usgs":false,"family":"Stanley","given":"Emily","email":"","middleInitial":"H.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":721811,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70173974,"text":"ofr20161105 - 2016 - Regional water table (2014) in the Mojave River and Morongo Groundwater Basins, southwestern Mojave Desert, California","interactions":[],"lastModifiedDate":"2020-07-28T14:39:44.825439","indexId":"ofr20161105","displayToPublicDate":"2017-03-30T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1105","displayTitle":"Regional Water Table (2014) in the Mojave River and Morongo Groundwater Basins, Southwestern Mojave Desert, California","title":"Regional water table (2014) in the Mojave River and Morongo Groundwater Basins, southwestern Mojave Desert, California","docAbstract":"Data for static water-levels measured in about 610 wells during March-April 2014 by the U.S. Geological Survey (USGS), the Mojave Water Agency (MWA), and other local water districts were compiled to construct a regional water-table map. This map shows the elevation of the water table and general direction of groundwater movement in and around the Mojave River and Morongo groundwater basins. Water-level measurements recorded by the USGS and MWA staff were measured and compiled according to the procedures described in the Groundwater Technical Procedures of the U.S. Geological Survey (Cunningham and Schalk, 2011). Water-level data submitted by cooperating local water districts were collected by using procedures established by the corresponding agency, and compiled according to the procedures described in the Groundwater Technical Procedures of the U.S. Geological Survey (Cunningham and Schalk, 2011). All data were compared to historical data for quality-assurance purposes. Water-level contours from the 2012 water-level map (Teague and others, 2014) were used as a guide to interpret and shape the 2014 water-level contours in areas where 2014 water-level data were not available; these contours are shown as dashed (approximate) on the water-table map. In addition to being available on the interactive map, 2014 water-level data and contours are shown for the entire area of the Mojave River and Morongo groundwater basins on Plate 1. Water-level data for 2014 are accessible through the website by clicking the 2014 Sites button on the Data Downloads page.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161105","collaboration":"Prepared in cooperation with the Mojave Water Agency","usgsCitation":"Teague, N.F., Dick, M.C., House, S.F., and Clark, D.A., 2016, Regional water table (2014) in the Mojave River and Morongo groundwater basins, southwestern Mojave Desert, California, 2016: U.S. Geological Survey Open-File Report 2016–1105 (ver. 3, July 2020), 1 sheet, scale 1:170,000, https://doi.org/10.3133/ofr20161105.","productDescription":"1 Sheet: 42.00 x 37.00 inches; Project Site; Version History","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-074861","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":438469,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P848ZZ","text":"USGS data release","linkHelpText":"Regional Water Table (2014) in the Mojave River and Morongo Groundwater Basins, Southwestern Mojave Desert, California (ver. 1.2, September 2020)"},{"id":337647,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1105/cover/coverthb.jpg"},{"id":340194,"rank":2,"type":{"id":18,"text":"Project Site"},"url":"https://ca.water.usgs.gov/mojave/mojave-2014-water-levels.html","text":"Project Site"},{"id":376391,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2016/1105/versionHist.txt","size":"2 KB","linkFileType":{"id":2,"text":"txt"}},{"id":376390,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1105/ofr20161105.pdf","text":"Sheet","size":"17 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","otherGeospatial":"Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.5,\n 34.05\n ],\n [\n -117.5,\n 35.25\n ],\n [\n -116.0,\n 35.25\n ],\n [\n -116.0,\n 34.05\n ],\n [\n -117.5,\n 34.05\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted June 28, 2016; Version 2.0: March 30, 2017; Version 3.0: July 15, 2020","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Multiple age tracers were measured to estimate groundwater residence times in the regional aquifer system underlying southwestern Oman. This area, known as the Najd, is one of the most arid areas in the world and is planned to be the main agricultural center of the Sultanate of Oman in the near future. The three isotopic age tracers 4He, 14C and 36Cl were measured in waters collected from wells along a line that extended roughly from the Dhofar Mountains near the Arabian Sea northward 400 km into the Empty Quarter of the Arabian Peninsula. The wells sampled were mostly open to the Umm Er Radhuma confined aquifer, although, some were completed in the mostly unconfined Rus aquifer. The combined results from the three tracers indicate the age of the confined groundwater is < 40 ka in the recharge area in the Dhofar Mountains, > 100 ka in the central section north of the mountains, and up to and > one Ma in the Empty Quarter. The 14C data were used to help calibrate the 4He and 36Cl data. Mixing models suggest that long open boreholes north of the mountains compromise 14C-only interpretations there, in contrast to 4He and 36Cl calculations that are less sensitive to borehole mixing. Thus, only the latter two tracers from these more distant wells were considered reliable. In addition to the age tracers, δ2H and δ18O data suggest that seasonal monsoon and infrequent tropical cyclones are both substantial contributors to the recharge. The study highlights the advantages of using multiple chemical and isotopic data when estimating groundwater travel times and recharge rates, and differentiating recharge mechanisms.
","language":"English","publisher":"International Association of Geochemistry and Cosmochemistry","publisherLocation":"Oxford","doi":"10.1016/j.apgeochem.2016.08.012","usgsCitation":"Muller, T., Osenbruck, K., Strauch, G., Pavetich, S., Al-Mashaikhi, K., Herb, C., Merchel, S., Rugel, G., Aeschbach, W., and Sanford, W.E., 2016, Use of multiple age tracers to estimate groundwater residence times and long-term recharge rates in arid southern Oman: Applied Geochemistry, v. 74, p. 67-83, https://doi.org/10.1016/j.apgeochem.2016.08.012.","productDescription":"17 p.","startPage":"67","endPage":"83","ipdsId":"IP-078864","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":338256,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Oman","otherGeospatial":"Dhofar Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": 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55.4644775390625,\n 17.82714499951342\n ],\n [\n 55.5853271484375,\n 17.86374708440522\n ],\n [\n 55.7391357421875,\n 17.895114303749143\n ],\n [\n 55.9149169921875,\n 17.900341634875257\n ],\n [\n 55.96435546875,\n 17.916022703877665\n ],\n [\n 56.0797119140625,\n 17.921249418623304\n ],\n [\n 56.15112304687499,\n 17.926475979176438\n ],\n [\n 56.1895751953125,\n 17.947380678685217\n ],\n [\n 56.2664794921875,\n 17.93170238549813\n ],\n [\n 56.3543701171875,\n 17.910795834978483\n ],\n [\n 56.3818359375,\n 17.95783210227242\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"74","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58d63036e4b05ec7991310d9","contributors":{"authors":[{"text":"Muller, Th.","contributorId":189781,"corporation":false,"usgs":false,"family":"Muller","given":"Th.","email":"","affiliations":[],"preferred":false,"id":686014,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Osenbruck, K.","contributorId":189782,"corporation":false,"usgs":false,"family":"Osenbruck","given":"K.","email":"","affiliations":[],"preferred":false,"id":686035,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Strauch, G.","contributorId":189783,"corporation":false,"usgs":false,"family":"Strauch","given":"G.","email":"","affiliations":[],"preferred":false,"id":686016,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pavetich, S.","contributorId":189784,"corporation":false,"usgs":false,"family":"Pavetich","given":"S.","email":"","affiliations":[],"preferred":false,"id":686017,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Al-Mashaikhi, K.-S.","contributorId":189785,"corporation":false,"usgs":false,"family":"Al-Mashaikhi","given":"K.-S.","email":"","affiliations":[],"preferred":false,"id":686036,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Herb, C.","contributorId":189786,"corporation":false,"usgs":false,"family":"Herb","given":"C.","email":"","affiliations":[],"preferred":false,"id":686019,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Merchel, S.","contributorId":189787,"corporation":false,"usgs":false,"family":"Merchel","given":"S.","email":"","affiliations":[],"preferred":false,"id":686020,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rugel, G.","contributorId":189788,"corporation":false,"usgs":false,"family":"Rugel","given":"G.","email":"","affiliations":[],"preferred":false,"id":686021,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Aeschbach, W.","contributorId":189789,"corporation":false,"usgs":false,"family":"Aeschbach","given":"W.","email":"","affiliations":[],"preferred":false,"id":686022,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sanford, Ward E. 0000-0002-6624-0280 wsanford@usgs.gov","orcid":"https://orcid.org/0000-0002-6624-0280","contributorId":2268,"corporation":false,"usgs":true,"family":"Sanford","given":"Ward","email":"wsanford@usgs.gov","middleInitial":"E.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":686013,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70185563,"text":"70185563 - 2016 - A comparison of observed and predicted ground motions from the 2015 MW7.8 Gorkha, Nepal, earthquake","interactions":[],"lastModifiedDate":"2017-03-24T10:43:50","indexId":"70185563","displayToPublicDate":"2017-03-24T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2822,"text":"Natural Hazards","active":true,"publicationSubtype":{"id":10}},"title":"A comparison of observed and predicted ground motions from the 2015 MW7.8 Gorkha, Nepal, earthquake","docAbstract":"We use 21 strong motion recordings from Nepal and India for the 25 April 2015 moment magnitude (MW) 7.8 Gorkha, Nepal, earthquake together with the extensive macroseismic intensity data set presented by Martin et al. (Seism Res Lett 87:957–962, 2015) to analyse the distribution of ground motions at near-field and regional distances. We show that the data are consistent with the instrumental peak ground acceleration (PGA) versus macroseismic intensity relationship developed by Worden et al. (Bull Seism Soc Am 102:204–221, 2012), and use this relationship to estimate peak ground acceleration from intensities (PGAEMS). For nearest-fault distances (RRUP < 200 km), PGAEMS is consistent with the Atkinson and Boore (Bull Seism Soc Am 93:1703–1729, 2003) subduction zone ground motion prediction equation (GMPE). At greater distances (RRUP > 200 km), instrumental PGA values are consistent with this GMPE, while PGAEMS is systematically higher. We suggest the latter reflects a duration effect whereby effects of weak shaking are enhanced by long-duration and/or long-period ground motions from a large event at regional distances. We use PGAEMS values within 200 km to investigate the variability of high-frequency ground motions using the Atkinson and Boore (Bull Seism Soc Am 93:1703–1729, 2003) GMPE as a baseline. Across the near-field region, PGAEMS is higher by a factor of 2.0–2.5 towards the northern, down-dip edge of the rupture compared to the near-field region nearer to the southern, up-dip edge of the rupture. Inferred deamplification in the deepest part of the Kathmandu valley supports the conclusion that former lake-bed sediments experienced a pervasive nonlinear response during the mainshock (Dixit et al. in Seismol Res Lett 86(6):1533–1539, 2015; Rajaure et al. in Tectonophysics, 2016. Ground motions were significantly amplified in the southern Gangetic basin, but were relatively low in the northern basin. The overall distribution of ground motions and damage during the Gorkha earthquake thus reflects a combination of complex source, path, and site effects. We also present a macroseismic intensity data set and analysis of ground motions for the MW7.3 Dolakha aftershock on 12 May 2015, which we compare to the Gorkha mainshock and conclude was likely a high stress-drop event.
","language":"English","publisher":"International Society for the Prevention and Mitigation of Natural Hazards","publisherLocation":"Dordrecht","doi":"10.1007/s11069-016-2505-8","usgsCitation":"Hough, S.E., Martin, S.S., Gahalaut, V., Joshi, A., Landes, M., and Bossu, R., 2016, A comparison of observed and predicted ground motions from the 2015 MW7.8 Gorkha, Nepal, earthquake: Natural Hazards, v. 84, no. 3, p. 1661-1684, https://doi.org/10.1007/s11069-016-2505-8.","productDescription":"24 p.","startPage":"1661","endPage":"1684","ipdsId":"IP-077448","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":470259,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1007/s11069-016-2505-8","text":"External Repository"},{"id":338271,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Nepal","otherGeospatial":"Gorkha District","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 79,\n 25.75\n ],\n [\n 88.25,\n 25.75\n ],\n [\n 88.25,\n 31\n ],\n [\n 79,\n 31\n ],\n [\n 79,\n 25.75\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"84","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-16","publicationStatus":"PW","scienceBaseUri":"58d63037e4b05ec7991310dd","contributors":{"authors":[{"text":"Hough, Susan E. 0000-0002-5980-2986 hough@usgs.gov","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":587,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"hough@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":685970,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, Stacey S.","contributorId":187758,"corporation":false,"usgs":false,"family":"Martin","given":"Stacey","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":685971,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gahalaut, V.","contributorId":189762,"corporation":false,"usgs":false,"family":"Gahalaut","given":"V.","email":"","affiliations":[],"preferred":false,"id":685972,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Joshi, A.","contributorId":189763,"corporation":false,"usgs":false,"family":"Joshi","given":"A.","email":"","affiliations":[],"preferred":false,"id":685973,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Landes, M.","contributorId":189764,"corporation":false,"usgs":false,"family":"Landes","given":"M.","email":"","affiliations":[],"preferred":false,"id":685974,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bossu, R.","contributorId":189765,"corporation":false,"usgs":false,"family":"Bossu","given":"R.","email":"","affiliations":[],"preferred":false,"id":685975,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70182786,"text":"70182786 - 2016 - Northern long-eared bat day-roosting and prescribed fire in the central Appalachians","interactions":[],"lastModifiedDate":"2017-03-14T09:58:10","indexId":"70182786","displayToPublicDate":"2017-03-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Northern long-eared bat day-roosting and prescribed fire in the central Appalachians","docAbstract":"The northern long-eared bat (Myotis septentrionalis Trovessart) is a cavity-roosting species that forages in cluttered upland and riparian forests throughout the oak-dominated Appalachian and Central Hardwoods regions. Common prior to white-nose syndrome, the population of this bat species has declined to functional extirpation in some regions in the Northeast and Mid-Atlantic, including portions of the central Appalachians. Our long-term research in the central Appalachians has shown that maternity colonies of this species form non-random assorting networks in patches of suitable trees that result from long- and short-term forest disturbance processes, and that roost loss can occur with these disturbances. Following two consecutive prescribed burns on the Fernow Experimental Forest in the central Appalachians, West Virginia, USA, in 2007 to 2008, post-fire counts of suitable black locust (Robinia pseudoacacia L.; the most selected species for roosting) slightly decreased by 2012. Conversely, post-fire numbers of suitable maple (Acer spp. L.), primarily red maple (Acer rubrum L.), increased by a factor of three, thereby ameliorating black locust reduction. Maternity colony network metrics such as roost degree (use) and network density for two networks in the burned compartment were similar to the single network observed in unburned forest. However, roost clustering and degree of roost centralization was greater for the networks in the burned forest area. Accordingly, the short-term effects of prescribed fire are slightly or moderately positive in impact to day-roost habitat for the northern long-eared bat in the central Appalachians from a social dynamic perspective. Listing of northern long-eared bats as federally threatened will bring increased scrutiny of immediate fire impacts from direct take as well as indirect impacts from long-term changes to roosting and foraging habitat in stands being returned to historic fire-return conditions. Unfortunately, definitive impacts will remain speculative owing to the species’ current rarity and the paucity of forest stand data that considers tree condition or that adequately tracks snags spatially and temporally.
","language":"English","publisher":"Association for Fire Ecology","doi":"10.4996/fireecology.1202013","usgsCitation":"Ford, W., Silvis, A., Johnson, J.B., Edwards, J.W., and Karp, M., 2016, Northern long-eared bat day-roosting and prescribed fire in the central Appalachians: Fire Ecology, v. 12, no. 2, p. 13-27, https://doi.org/10.4996/fireecology.1202013.","productDescription":"15 p.","startPage":"13","endPage":"27","ipdsId":"IP-066208","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":461978,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.4996/fireecology.1202013","text":"Publisher Index Page"},{"id":336785,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Appalachian Mountains","volume":"12","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-01","publicationStatus":"PW","scienceBaseUri":"58b7eba2e4b01ccd5500badd","contributors":{"authors":[{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":673748,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Silvis, Alexander","contributorId":171585,"corporation":false,"usgs":false,"family":"Silvis","given":"Alexander","email":"","affiliations":[{"id":26923,"text":"Virginia Polytechnic Institute, Blacksburg, VA","active":true,"usgs":false}],"preferred":false,"id":680475,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Joshua B.","contributorId":171598,"corporation":false,"usgs":false,"family":"Johnson","given":"Joshua","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":680476,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Edwards, John W.","contributorId":169827,"corporation":false,"usgs":false,"family":"Edwards","given":"John","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":680477,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Karp, Milu","contributorId":187455,"corporation":false,"usgs":false,"family":"Karp","given":"Milu","email":"","affiliations":[],"preferred":false,"id":680478,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70182787,"text":"70182787 - 2016 - Fire effects on wildlife in Central Hardwoods and Appalachian regions","interactions":[],"lastModifiedDate":"2017-03-14T10:00:52","indexId":"70182787","displayToPublicDate":"2017-03-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Fire effects on wildlife in Central Hardwoods and Appalachian regions","docAbstract":"Fire is being prescribed and used increasingly to promote ecosystem restoration (e.g., oak woodlands and savannas) and to manage wildlife habitat in the Central Hardwoods and Appalachian regions, USA. However, questions persist as to how fire affects hardwood forest communities and associated wildlife, and how fire should be used to achieve management goals. We provide an up-to-date review of fire effects on various wildlife species and their habitat in the Central Hardwoods and Appalachians. Documented direct effects (i.e., mortality) on wildlife are rare. Indirect effects (i.e., changes in habitat quality) are influenced greatly by light availability, fire frequency, and fire intensity. Unless fire intensity is great enough to kill a portion of the overstory, burning in closed-canopy forests has provided little benefit for most wildlife species in the region because it doesn’t result in enough sunlight penetration to elicit understory response. Canopy reduction through silvicultural treatment has enabled managers to use fire more effectively. Fire intensity must be kept low in hardwoods to limit damage to many species of overstory trees. However, wounding or killing trees with fire benefits many wildlife species by allowing increased sunlight to stimulate understory response, snag and subsequent cavity creation, and additions of large coarse woody debris. In general, a fire-return interval of 2 yr to 7 yr benefits a wide variety of wildlife species by providing a diverse structure in the understory; increasing browse, forage, and soft mast; and creating snags and cavities. Historically, dormant-season fire was most prevalent in these regions, and it still is when most prescribed fire is implemented in hardwood systems as burn-days are relatively few in the growing season of May through August because of shading from leaf cover and high fuel moisture. Late growing-season burning increases the window for burning, and better control on woody composition is possible. Early growing-season fire may pose increased risk for some species, especially herpetofauna recently emerged from winter hibernacula (April) or forest songbirds that nest in the understory (May to June). However, negative population-level effects are unlikely unless the burned area is relatively large and early growing-season fire is used continually. We did not find evidence that fire is leading to population declines for any species, including Endangered Species Act (ESA)-listed species (e.g., Indiana bat [Myotis sodalis Mill. Allen] or northern long-eared bat [M. septentrionalis Trouess.]). Instead, data indicate that fire can enhance habitat for bats by increasing suitability of foraging and day-roost sites. Similarly, concern over burning and displacement of woodland salamanders (Plethodontidae), another taxa of heightened conservation concern, is alleviated when fire is prescribed along ecologically appropriate aspect and slope gradients and not forced into mesic, high site index environments where salamanders are most common. Because topography across the Central Hardwoods and Appalachians is diverse, we contend that applying fire on positions best suited for burning is an effective approach to increase regional landscape heterogeneity and biological diversity. Herein, we offer prescriptive concepts for burning for various wildlife species and guilds in the Central Hardwoods and Appalachians.
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Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":673749,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lashley, Marcus A.","contributorId":187467,"corporation":false,"usgs":false,"family":"Lashley","given":"Marcus A.","affiliations":[],"preferred":false,"id":680521,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moorman, Christopher","contributorId":146485,"corporation":false,"usgs":false,"family":"Moorman","given":"Christopher","affiliations":[],"preferred":false,"id":680522,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stambaugh, Michael C.","contributorId":51202,"corporation":false,"usgs":true,"family":"Stambaugh","given":"Michael C.","affiliations":[],"preferred":false,"id":680523,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70182746,"text":"70182746 - 2016 - Fluid-faulting evolution in high definition: Connecting fault structure and frequency-magnitude variations during the 2014 Long Valley Caldera, California earthquake swarm","interactions":[],"lastModifiedDate":"2017-02-28T09:40:12","indexId":"70182746","displayToPublicDate":"2017-02-28T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Fluid-faulting evolution in high definition: Connecting fault structure and frequency-magnitude variations during the 2014 Long Valley Caldera, California earthquake swarm","docAbstract":"An extended earthquake swarm occurred beneath southeastern Long Valley Caldera between May and November 2014, culminating in three magnitude 3.5 earthquakes and 1145 cataloged events on 26 September alone. The swarm produced the most prolific seismicity in the caldera since a major unrest episode in 1997-1998. To gain insight into the physics controlling swarm evolution, we used large-scale cross-correlation between waveforms of cataloged earthquakes and continuous data, producing precise locations for 8494 events, more than 2.5 times the routine catalog. We also estimated magnitudes for 18,634 events (~5.5 times the routine catalog), using a principal component fit to measure waveform amplitudes relative to cataloged events. This expanded and relocated catalog reveals multiple episodes of pronounced hypocenter expansion and migration on a collection of neighboring faults. Given the rapid migration and alignment of hypocenters on narrow faults, we infer that activity was initiated and sustained by an evolving fluid pressure transient with a low-viscosity fluid, likely composed primarily of water and CO2 exsolved from underlying magma. Although both updip and downdip migration were observed within the swarm, downdip activity ceased shortly after activation, while updip activity persisted for weeks at moderate levels. Strongly migrating, single-fault episodes within the larger swarm exhibited a higher proportion of larger earthquakes (lower Gutenberg-Richter b value), which may have been facilitated by fluid pressure confined in two dimensions within the fault zone. In contrast, the later swarm activity occurred on an increasingly diffuse collection of smaller faults, with a much higher b value.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2015JB012719","usgsCitation":"Shelly, D.R., Ellsworth, W.L., and Hill, D.P., 2016, Fluid-faulting evolution in high definition: Connecting fault structure and frequency-magnitude variations during the 2014 Long Valley Caldera, California earthquake swarm: Journal of Geophysical Research, v. 212, no. 3, p. 1776-1795, https://doi.org/10.1002/2015JB012719.","productDescription":"20 p.","startPage":"1776","endPage":"1795","ipdsId":"IP-070982","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":470265,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jb012719","text":"Publisher Index Page"},{"id":336313,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":" Long Valley Caldera","volume":"212","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-08","publicationStatus":"PW","scienceBaseUri":"58b69a3fe4b01ccd54ff3f88","contributors":{"authors":[{"text":"Shelly, David R. dshelly@usgs.gov","contributorId":2978,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":673557,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ellsworth, William L. ellsworth@usgs.gov","contributorId":787,"corporation":false,"usgs":true,"family":"Ellsworth","given":"William","email":"ellsworth@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":673558,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hill, David P. hill@usgs.gov","contributorId":2600,"corporation":false,"usgs":true,"family":"Hill","given":"David","email":"hill@usgs.gov","middleInitial":"P.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":false,"id":673559,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}