We used the Mahalanobis distance statistic and a raster geographic information system (GIS) to model potential black bear (Ursus americanus) denning habitat in the Ouachita Mountains of Arkansas. The Mahalanobis distance statistic was used to represent the standard squared distance between sample variates in the GIS database (forest cover type, elevation, slope, aspect, distance to streams, distance to roads, and forest cover richness) and variates at known bear dens. Two models were developed: a generalized model for all den locations and another specific to dens in rock cavities. Differences between habitat at den sites and habitat across the study area were represented in 2 new GIS themes as Mahalanobis distance values. Cells similar to the mean vector derived from the known dens had low Mahalanobis distance values, and dissimilar cells had high values. The reliability of the predictive model was tested by overlaying den locations collected subsequent to original model development on the resultant den habitat themes. Although the generalized model demonstrated poor reliability, the model specific to rock dens had good reliability. Bears were more likely to choose rock den locations with low Mahalanobis distance values and less likely to choose those with high values. The model can be used to plan the timing and extent of management actions (e.g., road building, prescribed fire, timber harvest) most appropriate for those sites with high or low denning potential.
","language":"English","publisher":"International Association for Bear Research and Management","usgsCitation":"Clark, J.D., Hayes, S., and Pledger, J., 1998, A female black bear denning habitat model using a geographic information system: Ursus, v. 10, p. 181-185.","productDescription":"5 p.","startPage":"181","endPage":"185","numberOfPages":"5","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":129344,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"Dry Creek Wilderness Area, Ouachita Mountain region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.8067626953125,\n 35.092945313732635\n ],\n [\n -93.74633789062499,\n 35.088450570365396\n ],\n [\n -93.680419921875,\n 35.088450570365396\n ],\n [\n -93.61175537109375,\n 35.088450570365396\n ],\n [\n -93.49227905273438,\n 35.099686964274724\n ],\n [\n -93.48129272460936,\n 35.080584173400815\n ],\n [\n -93.4771728515625,\n 35.023248960913385\n ],\n [\n -93.48129272460936,\n 34.93885938523973\n ],\n [\n -93.52935791015625,\n 34.918592949176926\n ],\n [\n -93.61175537109375,\n 34.88367790965999\n ],\n [\n -93.76144409179688,\n 34.84987503195418\n ],\n [\n -93.84246826171875,\n 34.81154831029378\n ],\n [\n -93.9276123046875,\n 34.78899484825181\n ],\n [\n -94.0869140625,\n 34.785611296793306\n ],\n [\n -94.26544189453125,\n 34.8025276659169\n ],\n [\n -94.28741455078125,\n 34.83522280367885\n ],\n [\n -94.31350708007812,\n 34.88818391007525\n ],\n [\n -94.31076049804688,\n 34.942236637841184\n ],\n [\n -94.31076049804688,\n 34.98275281869196\n ],\n [\n -94.295654296875,\n 35.02662273458687\n ],\n [\n -94.22836303710938,\n 35.064849103829204\n ],\n [\n -94.12399291992188,\n 35.08957427943165\n ],\n [\n -93.99627685546874,\n 35.1041810882765\n ],\n [\n -93.91937255859375,\n 35.113168592954004\n ],\n [\n -93.8067626953125,\n 35.092945313732635\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b24e4b07f02db6aecb0","contributors":{"authors":[{"text":"Clark, J. D.","contributorId":85911,"corporation":false,"usgs":true,"family":"Clark","given":"J.","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":321065,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hayes, S.G.","contributorId":97043,"corporation":false,"usgs":true,"family":"Hayes","given":"S.G.","email":"","affiliations":[],"preferred":false,"id":321066,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pledger, J.M.","contributorId":59393,"corporation":false,"usgs":true,"family":"Pledger","given":"J.M.","email":"","affiliations":[],"preferred":false,"id":321064,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":1012931,"text":"1012931 - 1998 - Estimates of brown bear abundance on Kodiak Island, Alaska","interactions":[],"lastModifiedDate":"2012-02-02T00:04:06","indexId":"1012931","displayToPublicDate":"1998-01-01T00:00:00","publicationYear":"1998","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3671,"text":"Ursus","active":true,"publicationSubtype":{"id":10}},"title":"Estimates of brown bear abundance on Kodiak Island, Alaska","docAbstract":"During 1987-94 we used capture-mark-resight (CMR) methodology and\r\nrates of observation (bears/hour and bears/100 km2) of unmarked brown bears\r\n(Ursus arctos middendorffi) during intensive aerial surveys (IAS) to estimate\r\nabundance of brown bears on Kodiak Island and to establish a baseline for\r\nmonitoring population trends. CMR estimates were obtained on 3 study areas;\r\ndensity ranged from 216-234 bears/1,000 km2 for independent animals and 292-342\r\nbears/1,000 km2 including dependent offspring. Rates of observation during IAS\r\nranged from 1.4-5.4 independent bears/hour and 2.9-18.0 independent bears/100\r\nkm2. Density estimates for independent bears on each IAS area were obtained by\r\ndividing mean number of bears observed during replicate surveys by estimated\r\nsightability (based on CMR-derived sightability in areas with similar habitat. \r\nBrown bear abundance on 21 geographic units of Kodiak Island and 3 nearby\r\nislands was estimated by extrapolation from CMR and IAS data using comparisons\r\nof habitat characteristics and sport harvest information. Population estimates\r\nfor independent and total bears were 1,800 and 2,600. The CMR and IAS\r\nprocedures offer alternative means, depending on management objective and\r\navailable resources, of measuring population trend of brown bears on Kodiak\r\nIsland.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ursus","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","usgsCitation":"Barnes, V., and Smith, R.B., 1998, Estimates of brown bear abundance on Kodiak Island, Alaska: Ursus, v. 10, p. 1-9.","productDescription":"pp. 1-9","startPage":"1","endPage":"9","numberOfPages":"9","costCenters":[{"id":106,"text":"Alaska Biological Science Center","active":false,"usgs":true}],"links":[{"id":128558,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a01e4b07f02db5f7f16","contributors":{"authors":[{"text":"Barnes, V.G. Jr.","contributorId":55765,"corporation":false,"usgs":true,"family":"Barnes","given":"V.G.","suffix":"Jr.","email":"","affiliations":[],"preferred":false,"id":318442,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, R. B.","contributorId":64589,"corporation":false,"usgs":true,"family":"Smith","given":"R.","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":318443,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":25104,"text":"fs00798 - 1998 - Pesticides in surface waters of the Santee River basin and coastal drainages, North and South Carolina","interactions":[],"lastModifiedDate":"2019-11-11T11:19:30","indexId":"fs00798","displayToPublicDate":"1998-01-01T00:00:00","publicationYear":"1998","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"007-98","title":"Pesticides in surface waters of the Santee River basin and coastal drainages, North and South Carolina","docAbstract":"This report summarizes the available pesticide data for surface waters in the Santee River Basin and coastal drainages (SANT) study area, as part of the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Program. Data from the U.S. Environmental Protection Agency Storage and Retrieval database and data collected by the USGS in the SANT NAWQA study area were assessed. A description of the study area is followed by an estimate of pesticide application data. Detected pesticides and their reported maximum concentrations are summarized. Pesticide concentrations are compared with applicable water-quality standards. Seasonality of pesticide concentrations in surface water in the SANT NAWQA study area also is assessed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs00798","usgsCitation":"Maluk, T.L., and Kelley, R.E., 1998, Pesticides in surface waters of the Santee River basin and coastal drainages, North and South Carolina: U.S. Geological Survey Fact Sheet 007-98, 6 p., https://doi.org/10.3133/fs00798.","productDescription":"6 p.","additionalOnlineFiles":"Y","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":121788,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_007_98.jpg"},{"id":268265,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/1998/0007/","text":"Report HTML"}],"country":"United States","state":"North Carolina, South Carolina","otherGeospatial":"Santee River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.8319091796875,\n 31.99875937194732\n ],\n [\n -80.3265380859375,\n 32.48196313217176\n ],\n [\n -79.95574951171875,\n 32.62549671451373\n ],\n [\n -79.8431396484375,\n 32.7503226078097\n ],\n [\n -79.57122802734375,\n 32.91648534731439\n ],\n [\n -79.54925537109375,\n 33.01096671579776\n ],\n [\n -79.38720703125,\n 33.04550781490999\n ],\n [\n -79.38446044921875,\n 33.075432481213326\n ],\n [\n -79.156494140625,\n 33.17434155100208\n ],\n [\n -80.04638671875,\n 33.54139466898275\n ],\n [\n -80.123291015625,\n 33.797408767572485\n ],\n [\n -80.013427734375,\n 33.87953701355924\n ],\n [\n -80.2001953125,\n 34.1890858311724\n ],\n [\n -80.15625,\n 34.829586636768205\n ],\n [\n -80.0079345703125,\n 34.92197103616377\n ],\n [\n -79.9749755859375,\n 34.99625375979014\n ],\n [\n -80.10406494140625,\n 35.20298910562885\n ],\n [\n -80.0738525390625,\n 35.31960740586162\n ],\n [\n -80.101318359375,\n 35.380092992092145\n ],\n [\n -80.562744140625,\n 35.817813158696616\n ],\n [\n -80.9033203125,\n 35.68407153314097\n ],\n [\n -81.2109375,\n 36.12900165569652\n ],\n [\n -81.683349609375,\n 36.11125252076156\n ],\n [\n -81.9140625,\n 35.505400093441324\n ],\n [\n -82.276611328125,\n 35.48751102385376\n ],\n [\n -83.14453125,\n 35.003003395276714\n ],\n [\n -83.14727783203125,\n 34.97375095431686\n ],\n [\n -83.26812744140625,\n 34.863397850419524\n ],\n [\n -83.36975097656249,\n 34.773203753940734\n ],\n [\n -83.37799072265624,\n 34.712266941280404\n ],\n [\n -83.33404541015625,\n 34.66709959253004\n ],\n [\n -83.2049560546875,\n 34.59930237746263\n ],\n [\n -83.155517578125,\n 34.56312121279482\n ],\n [\n -83.05938720703125,\n 34.48165602990584\n ],\n [\n -82.97149658203125,\n 34.474863669009004\n ],\n [\n -82.88909912109375,\n 34.45448326886294\n ],\n [\n -82.81219482421875,\n 34.338900400404995\n ],\n [\n -82.77374267578125,\n 34.268566186749894\n ],\n [\n -82.7215576171875,\n 34.134541681937364\n ],\n [\n -82.6556396484375,\n 34.02990029603907\n ],\n [\n -82.474365234375,\n 33.85673152928873\n ],\n [\n -82.28759765625,\n 33.76544869849223\n ],\n [\n -82.2271728515625,\n 33.669496972795535\n ],\n [\n -82.1502685546875,\n 33.58259116393916\n ],\n [\n -82.056884765625,\n 33.53223722395908\n ],\n [\n -81.990966796875,\n 33.41310221370827\n ],\n [\n -81.990966796875,\n 33.367237465838315\n ],\n [\n -81.8206787109375,\n 33.22030778968541\n ],\n [\n -81.80419921875,\n 33.14675022877648\n ],\n [\n -81.529541015625,\n 33.02248191961359\n ],\n [\n -81.5350341796875,\n 32.95797741405952\n ],\n [\n -81.474609375,\n 32.87036022808352\n ],\n [\n -81.4306640625,\n 32.71797709835758\n ],\n [\n -81.4471435546875,\n 32.648625783736726\n ],\n [\n -81.4251708984375,\n 32.59310597426537\n ],\n [\n -81.3262939453125,\n 32.55607364492026\n ],\n [\n -81.2274169921875,\n 32.47732919639942\n ],\n [\n -81.1614990234375,\n 32.319633552035214\n ],\n [\n -81.1395263671875,\n 32.23603621746476\n ],\n [\n -81.0296630859375,\n 32.08257455954592\n ],\n [\n -80.8319091796875,\n 31.99875937194732\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
Existing data on base-flow and groundwater nitrate loads were compiled and analyzed to assess the significance of groundwater discharge as a source of the nitrate load to nontidal streams of the Chesapeake Bay watershed. These estimates were then related to hydrogeomorphic settings based on lithology and physiographic province to provide insight on the areal distribution of ground-water discharge. Base-flow nitrate load accounted for 26 to about 100 percent of total-flow nitrate load, with a median value of 56 percent, and it accounted for 17 to 80 percent of total-flow total-nitrogen load, with a median value of 48 percent.
Hydrograph separations were conducted on continuous streamflow records from 276 gaging stations within the watershed. The values for base flow thus calculated were considered an estimate of ground-water discharge. The ratio of base flow to total flow provided an estimate of the relative importance of ground-water discharge within a basin.
Base-flow nitrate loads, total-flow nitrate loads, and total-flow total-nitrogen loads were previously computed from water-quality and discharge measurements by use of a regression model. Base-flow nitrate loads were available from 78 stations, total-flow nitrate loads were available from 86 stations, and total-flow total-nitrogen loads were available for 48 stations. The percentage of base-flow nitrate load to total-flow nitrate load could be computed for 57 stations, whereas the percentage of base-flow nitrate load to totalflow total-nitrogen load could be computed for 36 stations. These loads were divided by the basin area to obtain yields, which were used to compare the nitrate discharge from basins of different sizes.
The results indicate that ground-water discharge is a significant source of water and nitrate to the total streamflow and nitrate load. Base flow accounted for 16 to 92 percent of total streamflow at the 276 sampling sites, with a median value of 54 percent. It is estimated that of the 50 billion gallons of water that reaches the Chesapeake Bay each day, nearly 27 billion gallons is base flow.
Generalized lithology (siliciclastic, carbonate, crystalline, and unconsolidated) was combined with physiographic province (the Appalachian Plateau, the Valley and Ridge, the Blue Ridge, the Piedmont, including the Mesozoic Lowland section, and the Coastal Plain) to delineate 11 hydrogeomorphic regions. Areal variation of base flow and base-flow nitrate yield were assessed by means of nonparametric, one-way analysis of variance on basins grouped by the dominant hydrogeomorphic region and by correlation analysis of base flow or base-flow nitrate yield with the percentage of land area of a given hydrogeomorphic region within a basin.
Base flow appeared to have a significant relation to the hydrogeomorphic regions. The highest percentages of base flow were found in areas underlain by carbonate rock, crystalline rock with relatively low relief, and unconsolidated sediments. Lower percentages were found in areas underlain by siliclastic rocks and crystalline rocks with relatively high relief.
The relation between base-flow nitrate yield and hydrogeomorphic region is less clear. Although there is a relation between low nitrate yields and areas underlain by highrelief siliciclastic rocks, and a relation between high yields and carbonate rocks, much of this relation can be explained by the strong association between the hydrogeomorphic units and land use. In addition, most basins are mixtures of several hydrogeomorphic regions, so the nitrate yield from a basin depends on a large number of complex interacting factors. These unclear results indicate that the sample of available data used here may not be adequate to fully assess the relation between base-flow nitrate yield and the hydrogeomorphic setting of the basin. The results appear to show, however, that ground-water discharge is an important component of the total nontidal streamflow, and that ground-water discharge varies according to the hydrogeomorphic regions. Environmental management of the nontidal streams in the Chesapeake Bay watershed will thus have to consider the prevention of nutrient infiltration into aquifers as well as prevention of overland runoff of high-nitrogen waters.
","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/wri984059","usgsCitation":"Bachman, L.J., Lindsey, B., Brakebill, J.W., and Powars, D.S., 1998, Ground-water discharge and base-flow nitrate loads of nontidal streams, and their relation to a hydrogeomorphic classification of the Chesapeake Bay watershed, middle Atlantic Coast: U.S. Geological Survey Water-Resources Investigations Report 98-4059, iv, 71 p., https://doi.org/10.3133/wri984059.","productDescription":"iv, 71 p.","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":415596,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_48941.htm","linkFileType":{"id":5,"text":"html"}},{"id":1894,"rank":2,"type":{"id":15,"text":"Index 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Joseph","contributorId":33304,"corporation":false,"usgs":true,"family":"Bachman","given":"L.","email":"","middleInitial":"Joseph","affiliations":[],"preferred":false,"id":194155,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lindsey, Bruce D. 0000-0002-7180-4319 blindsey@usgs.gov","orcid":"https://orcid.org/0000-0002-7180-4319","contributorId":434,"corporation":false,"usgs":true,"family":"Lindsey","given":"Bruce D.","email":"blindsey@usgs.gov","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":false,"id":194152,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brakebill, John W. 0000-0001-9235-6810 jwbrakeb@usgs.gov","orcid":"https://orcid.org/0000-0001-9235-6810","contributorId":1061,"corporation":false,"usgs":true,"family":"Brakebill","given":"John","email":"jwbrakeb@usgs.gov","middleInitial":"W.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":194153,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Powars, David S. 0000-0002-6787-8964 dspowars@usgs.gov","orcid":"https://orcid.org/0000-0002-6787-8964","contributorId":1181,"corporation":false,"usgs":true,"family":"Powars","given":"David","email":"dspowars@usgs.gov","middleInitial":"S.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":194154,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70019868,"text":"70019868 - 1998 - Scaling laws from geomagnetic time series","interactions":[],"lastModifiedDate":"2024-02-09T23:13:37.865293","indexId":"70019868","displayToPublicDate":"1998-01-01T00:00:00","publicationYear":"1998","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Scaling laws from geomagnetic time series","docAbstract":"The notion of extended self-similarity (ESS) is applied here for the X-component time series of geomagnetic field fluctuations. Plotting nth order structure functions against the fourth order structure function we show that low-frequency geomagnetic fluctuations up to the order n = 10 follow the same scaling laws as MHD fluctuations in solar wind, however, for higher frequencies (f > 1/5[h]) a clear departure from the expected universality is observed for n >6. ESS does not allow to make an unambiguous statement about the non triviality of scaling laws in ”geomagnetic“ turbulence. However, we suggest to use higher order moments as promising diagnostic tools for mapping the contributions of various remote magnetospheric sources to local observatory data.
The U.S. Fish and Wildlife Service and the Alaska Department of Fish and Game have cooperated since 1984 to develop and evaluate satellite telemetry as a means of overcoming the high costs and logistical problems of conventional VHF (very high frequency) radiotelemetry systems. Detailed locational and behavioral data on caribou (Rangifer tarandus), polar bears (Ursus maritimus), and other large mammals in Alaska have been obtained using the Argos Data Collection and Location System (DCLS). The Argos system, a cooperative project of the Centre National d'Études Spatiales of France, the National Oceanic and Atmospheric Administration, and the National Aeronautics and Space Administration, is designed to acquire environmental data on a routine basis from anywhere on earth. Transmitters weighing 1.6-2.0 kg and functioning approximately 12-18 months operated on a frequency of 401.650 MHz. Signals from the transmitters were received by Argos DCLS instruments aboard two Tiros-N weather satellites in sun-synchronous, nearpolar orbits. Data from the satellites were received at tracking stations, transferred to processing centers in Maryland and France, and made available to users via computer tape, printouts, or telephone links.
During 1985 and 1986, more than 25,000 locations and an additional 28,000 sets of sensor data (transmitter temperature and short-term and long-term indices of animal activity) were acquired for caribou and polar bears. Locations were calculated from the Doppler shift in the transmitted signal as the satellite approached and then moved away from the transmitter. The mean locational error for transmitters at known locations (n - 1,265) was 829 m; 90% of the calculated locations were within 1,700 m of the true location. Caribou transmitters provided a mean of 3.1 (+5.0. SD) locations per day during 6h of daily operation, and polar bear transmitters provided 1.7 (+6.9SD) locations during 12h of operation every third day. During the first 6 months of operation, the UHF (ultra-high frequency) signal failed on three of 32 caribou transmitters and 10 of 36 polar bear transmitters.
A geographic information system (GIS) incorporating other databases (e.g., land cover, elevation, slope, aspect, hydrology, ice distribution) was used to analyze and display detailed locational and behavioral data collected via satellite. Examples of GIS applications to research projects using satellite telemetry and examples of detailed movement patterns of caribou and polar bears are presented. This report includes documentation for computer software packages for processing Argos data and presents developments, as of March 1987, in transmitter design, data retrieval using a local user terminal, computer software, and sensor development and calibration.
","language":"English","publisher":"U.S. Fish and Wildlife Service","publisherLocation":"Washington, D.C.","usgsCitation":"Fancy, S.G., Pank, L.F., Douglas, D.C., Curby, C.H., Garner, G.W., Amstrup, S.C., and Regelin, W.L., 1998, Satellite telemetry: A new tool for wildlife research and management: Resource Publication 172, 54 p.","productDescription":"54 p.","numberOfPages":"61","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":338146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":382535,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://ecos.fws.gov/ServCat/DownloadFile/105285","linkFileType":{"id":1,"text":"pdf"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58d38d3be4b0236b68f98eec","contributors":{"authors":[{"text":"Fancy, Steven G.","contributorId":176135,"corporation":false,"usgs":false,"family":"Fancy","given":"Steven","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":685817,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pank, Larry F.","contributorId":82767,"corporation":false,"usgs":true,"family":"Pank","given":"Larry","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":685818,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":685819,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Curby, Catherine H.","contributorId":189722,"corporation":false,"usgs":false,"family":"Curby","given":"Catherine","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":685820,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garner, Gerald W.","contributorId":149918,"corporation":false,"usgs":false,"family":"Garner","given":"Gerald","email":"","middleInitial":"W.","affiliations":[{"id":13117,"text":"Institute of Arctic Biology, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":685821,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Amstrup, Steven C.","contributorId":67034,"corporation":false,"usgs":false,"family":"Amstrup","given":"Steven","email":"","middleInitial":"C.","affiliations":[{"id":13182,"text":"Polar Bears International","active":true,"usgs":false}],"preferred":false,"id":685822,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Regelin, Wayne L.","contributorId":111763,"corporation":false,"usgs":false,"family":"Regelin","given":"Wayne","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":685823,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70020061,"text":"70020061 - 1998 - Well log evaluation of gas hydrate saturations","interactions":[],"lastModifiedDate":"2012-03-12T17:19:18","indexId":"70020061","displayToPublicDate":"1998-01-01T00:00:00","publicationYear":"1998","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Well log evaluation of gas hydrate saturations","docAbstract":"The amount of gas sequestered in gas hydrates is probably enormous, but estimates are highly speculative due to the lack of previous quantitative studies. Gas volumes that may be attributed to a gas hydrate accumulation within a given geologic setting are dependent on a number of reservoir parameters; one of which, gas-hydrate saturation, can be assessed with data obtained from downhole well logging devices. The primary objective of this study was to develop quantitative well-log evaluation techniques which will permit the calculation of gas-hydrate saturations in gas-hydrate-bearing sedimentary units. The \"standard\" and \"quick look\" Archie relations (resistivity log data) yielded accurate gas-hydrate and free-gas saturations within all of the gas hydrate accumulations assessed in the field verification phase of the study. Compressional wave acoustic log data have been used along with the Timur, modified Wood, and the Lee weighted average acoustic equations to calculate accurate gas-hydrate saturations in all of the gas hydrate accumulations assessed in this study. The well log derived gas-hydrate saturations calculated in the field verification phase of this study, which range from as low as 2% to as high as 97%, confirm that gas hydrates represent a potentially important source of natural gas.","largerWorkTitle":"Transactions of the SPWLA Annual Logging Symposium (Society of Professional Well Log Analysts)","conferenceTitle":"39th Annual Logging Symposium","conferenceDate":"26 May 1998 through 29 May 1998","conferenceLocation":"Keystone, CO","language":"English","issn":"00811718","usgsCitation":"Collett, T.S., 1998, Well log evaluation of gas hydrate saturations, in Transactions of the SPWLA Annual Logging Symposium (Society of Professional Well Log Analysts), Keystone, CO, 26 May 1998 through 29 May 1998.","costCenters":[],"links":[{"id":228035,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505bcfd6e4b08c986b32eb2f","contributors":{"authors":[{"text":"Collett, T. S. 0000-0002-7598-4708","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":86342,"corporation":false,"usgs":true,"family":"Collett","given":"T.","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":384866,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70020079,"text":"70020079 - 1998 - Modeling tidal hydrodynamics of San Diego Bay, California","interactions":[],"lastModifiedDate":"2019-02-04T09:50:09","indexId":"70020079","displayToPublicDate":"1998-01-01T00:00:00","publicationYear":"1998","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Modeling tidal hydrodynamics of San Diego Bay, California","docAbstract":"In 1983, current data were collected by the National Oceanic and Atmospheric Administration using mechanical current meters. During 1992 through 1996, acoustic Doppler current profilers as well as mechanical current meters and tide gauges were used. These measurements not only document tides and tidal currents in San Diego Bay, but also provide independent data sets for model calibration and verification. A high resolution (100-m grid), depth-averaged, numerical hydrodynamic model has been implemented for San Diego Bay to describe essential tidal hydrodynamic processes in the bay. The model is calibrated using the 1983 data set and verified using the more recent 1992-1996 data. Discrepancies between model predictions and field data in beth model calibration and verification are on the order of the magnitude of uncertainties in the field data. The calibrated and verified numerical model has been used to quantify residence time and dilution and flushing of contaminant effluent into San Diego Bay. Furthermore, the numerical model has become an important research tool in ongoing hydrodynamic and water quality studies and in guiding future field data collection programs.
","language":"English","publisher":"American Water Resources Assoc","doi":"10.1111/j.1752-1688.1998.tb04159.x","issn":"1093474X","usgsCitation":"Wang, P., Cheng, R.T., Richter, K., Gross, E., Sutton, D., and Gartner, J.W., 1998, Modeling tidal hydrodynamics of San Diego Bay, California: Journal of the American Water Resources Association, v. 34, no. 5, p. 1123-1140, https://doi.org/10.1111/j.1752-1688.1998.tb04159.x.","productDescription":"18 p.","startPage":"1123","endPage":"1140","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":5079,"text":"Pacific Regional Director's Office","active":true,"usgs":true}],"links":[{"id":227703,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Diego Bay","volume":"34","issue":"5","noUsgsAuthors":false,"publicationDate":"2007-06-08","publicationStatus":"PW","scienceBaseUri":"505a5c54e4b0c8380cd6fbea","contributors":{"authors":[{"text":"Wang, P.-F.","contributorId":25311,"corporation":false,"usgs":true,"family":"Wang","given":"P.-F.","email":"","affiliations":[],"preferred":false,"id":384940,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cheng, R. T.","contributorId":23138,"corporation":false,"usgs":false,"family":"Cheng","given":"R.","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":384939,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richter, K.","contributorId":72146,"corporation":false,"usgs":true,"family":"Richter","given":"K.","email":"","affiliations":[],"preferred":false,"id":384943,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gross, E.S.","contributorId":62353,"corporation":false,"usgs":true,"family":"Gross","given":"E.S.","email":"","affiliations":[],"preferred":false,"id":384941,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sutton, D.","contributorId":70133,"corporation":false,"usgs":true,"family":"Sutton","given":"D.","email":"","affiliations":[],"preferred":false,"id":384942,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gartner, J. W.","contributorId":81903,"corporation":false,"usgs":false,"family":"Gartner","given":"J.","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":384944,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70021334,"text":"70021334 - 1998 - Hydrology of prairie pothole wetlands during drought and deluge: A 17-year study of the Cottonwood Lake wetland complex in North Dakota in the perspective of longer term measured and proxy hydrological records","interactions":[],"lastModifiedDate":"2012-03-12T17:19:50","indexId":"70021334","displayToPublicDate":"1998-01-01T00:00:00","publicationYear":"1998","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Hydrology of prairie pothole wetlands during drought and deluge: A 17-year study of the Cottonwood Lake wetland complex in North Dakota in the perspective of longer term measured and proxy hydrological records","docAbstract":"From 1988 to 1992 the north-central plains of North America had a drought that was followed by a wet period that continues to the present (1997). Data on the hydrology of the Cottonwood Lake area (CWLA) collected for nearly 10 years before, and during, the recent dry and wet periods indicate that some prairie pothole wetlands served only a recharge function under all climate conditions. Transpiration from groundwater around the perimeter of groundwater discharge wetlands drew water from the wetlands by the end of summer, even during very wet years. Long-term records of a climate index (Palmer Drought Severity Index), stream discharge (Pembina River), and lake level (Devils Lake) were used to put the 17-year CWLA record into a longer term perspective. In addition, proxy records of climate determined from fossils in the sediments of Devils Lake were also used. These data indicate that the drought of 1988-92 may have been the second worst of the 20th century, but that droughts of that magnitude, and worse, were common during the past 500 years. In contrast, the present wet period may be the wettest it has been during the past 130 years, or possibly the past 500 years.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Climatic Change","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","doi":"10.1023/A:1005448416571","issn":"01650009","usgsCitation":"Winter, T.C., and Rosenberry, D., 1998, Hydrology of prairie pothole wetlands during drought and deluge: A 17-year study of the Cottonwood Lake wetland complex in North Dakota in the perspective of longer term measured and proxy hydrological records: Climatic Change, v. 40, no. 2, p. 189-209, https://doi.org/10.1023/A:1005448416571.","startPage":"189","endPage":"209","numberOfPages":"21","costCenters":[],"links":[{"id":206525,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1023/A:1005448416571"},{"id":230106,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a3733e4b0c8380cd60ce1","contributors":{"authors":[{"text":"Winter, T. C.","contributorId":23485,"corporation":false,"usgs":true,"family":"Winter","given":"T.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":389507,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosenberry, D.O. 0000-0003-0681-5641","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":38500,"corporation":false,"usgs":true,"family":"Rosenberry","given":"D.O.","affiliations":[],"preferred":true,"id":389508,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70021154,"text":"70021154 - 1998 - Stochastic calibration of an orographic percipitation model","interactions":[],"lastModifiedDate":"2024-03-26T11:22:45.857956","indexId":"70021154","displayToPublicDate":"1998-01-01T00:00:00","publicationYear":"1998","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Stochastic calibration of an orographic percipitation model","docAbstract":"In this study a stochastic approach to calibration of an orographic precipitation model (Rhea, 1978) was applied in the Gunnison River Basin of south-western Colorado. The stochastic approach to model calibration was used to determine: (1) the model parameter uncertainty and sensitivity; (2) the grid-cell resolution to run the model (10 or 5 km grids); (3) the model grid rotation increment; and (4) the basin subdivision by elevation band for parameter definition. Results from the stochastic calibration are location and data dependent. Uncertainty, sensitivity and range in the final parameter sets were found to vary by grid-cell resolution and elevation. Ten km grids were found to be a more robust model configuration than 5 km grids. Grid rotation increment, tested using only 10 km grids, indicated increments of less than 10 degrees to be superior. Basin subdivision into two elevation bands was found to produce 'optimal' results for both 10 and 5 km grids.
","language":"English","publisher":"Wiley","issn":"08856087","usgsCitation":"Hay, L., 1998, Stochastic calibration of an orographic percipitation model: Hydrological Processes, v. 12, no. 4, p. 613-634.","productDescription":"22 p.","startPage":"613","endPage":"634","numberOfPages":"22","costCenters":[],"links":[{"id":230218,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505b984be4b08c986b31bf66","contributors":{"authors":[{"text":"Hay, L.E.","contributorId":54253,"corporation":false,"usgs":true,"family":"Hay","given":"L.E.","email":"","affiliations":[],"preferred":false,"id":388822,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70046038,"text":"70046038 - 1998 - Superfund GIS - Physiographic Provinces, Aquifer Outcrops and Recharge Rates in Tennessee","interactions":[],"lastModifiedDate":"2013-05-21T11:33:52","indexId":"70046038","displayToPublicDate":"1998-01-01T00:00:00","publicationYear":"1998","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":6,"text":"USGS Unnumbered Series"},"title":"Superfund GIS - Physiographic Provinces, Aquifer Outcrops and Recharge Rates in Tennessee","docAbstract":"This dataset is a coverage of the physiographic provinces, aquifer outcrops and recharge rates for Tennessee. Each polygon is attributed with its associated\nphysiographic region name (Miller, 1974), aquifer type and composition (Connell and Barron, 1993, p. 2), and aquifer recharge rates (Hoos, 1990 p. 19)","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/70046038","usgsCitation":"Greene, D., and Wolfe, W., 1998, Superfund GIS - Physiographic Provinces, Aquifer Outcrops and Recharge Rates in Tennessee, Dataset, https://doi.org/10.3133/70046038.","productDescription":"Dataset","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[],"links":[{"id":272525,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":272524,"type":{"id":16,"text":"Metadata"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/aquiphys.xml"}],"country":"United States","state":"Tennessee","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -90.31071472,34.98269653 ], [ -90.31071472,36.67922211 ], [ -81.64540863,36.67922211 ], [ -81.64540863,34.98269653 ], [ -90.31071472,34.98269653 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"519c9764e4b0ce6c26df81af","contributors":{"authors":[{"text":"Greene, D.J.","contributorId":108008,"corporation":false,"usgs":true,"family":"Greene","given":"D.J.","email":"","affiliations":[],"preferred":false,"id":478739,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolfe, W.J.","contributorId":10069,"corporation":false,"usgs":true,"family":"Wolfe","given":"W.J.","email":"","affiliations":[],"preferred":false,"id":478738,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70021321,"text":"70021321 - 1998 - Production of bromoform and dibromomethane by Giant Kelp: Factors affecting release and comparison to anthropogenic bromine sources","interactions":[],"lastModifiedDate":"2012-03-12T17:19:51","indexId":"70021321","displayToPublicDate":"1998-01-01T00:00:00","publicationYear":"1998","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Production of bromoform and dibromomethane by Giant Kelp: Factors affecting release and comparison to anthropogenic bromine sources","docAbstract":"Macrocystis pyrifera (Giant Kelp), a dominant macroalgal species in southern California, produced 171 ng per g fresh wt (gfwt) per day of CHBr3 and 48 ng gfwt-1 d-1 of CH2Br2 during laboratory incubations of whole blades. Comparable rates were measured during in situ incubations of intact fronds. Release of CHBr3 and CH2Br2 by M. pyrifera was affected by light and algal photosynthetic activity, suggesting that environmental factors influencing kelp physiology can affect halomethane release to the atmosphere. Data from H2O2 additions suggest that brominated methane production during darkness is limited by bromide oxidant supply. A bromine budget constructed for a region of southern California indicated that bromine emitted from the use of CH3Br as a fumigant (1 x 108 g Br yr-1) dominates macroalgal sources (3 x 106 g Br yr-1). Global projections, however, suggest that combined emissions of marine algae (including microalgae) contribute substantial amounts of bromine to the global cycle, perhaps on the same order of magnitude as anthropogenic sources.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Limnology and Oceanography","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","issn":"00243590","usgsCitation":"Goodwin, K., North, W., and Lidstrom, M., 1998, Production of bromoform and dibromomethane by Giant Kelp: Factors affecting release and comparison to anthropogenic bromine sources: Limnology and Oceanography, v. 42, no. 8, p. 1725-1734.","startPage":"1725","endPage":"1734","numberOfPages":"10","costCenters":[],"links":[{"id":229908,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"42","issue":"8","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a8ddfe4b0c8380cd7eea7","contributors":{"authors":[{"text":"Goodwin, K.D.","contributorId":45472,"corporation":false,"usgs":true,"family":"Goodwin","given":"K.D.","email":"","affiliations":[],"preferred":false,"id":389466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"North, W.J.","contributorId":93340,"corporation":false,"usgs":true,"family":"North","given":"W.J.","email":"","affiliations":[],"preferred":false,"id":389468,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lidstrom, M.E.","contributorId":93207,"corporation":false,"usgs":true,"family":"Lidstrom","given":"M.E.","email":"","affiliations":[],"preferred":false,"id":389467,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70019725,"text":"70019725 - 1998 - Trace element abundances of high-MgO glasses from Kilauea, Mauna Loa and Haleakala volcanoes, Hawaii","interactions":[],"lastModifiedDate":"2020-10-01T18:14:40.897507","indexId":"70019725","displayToPublicDate":"1998-01-01T00:00:00","publicationYear":"1998","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1336,"text":"Contributions to Mineralogy and Petrology","active":true,"publicationSubtype":{"id":10}},"title":"Trace element abundances of high-MgO glasses from Kilauea, Mauna Loa and Haleakala volcanoes, Hawaii","docAbstract":"We performed an ion-microprobe study of eleven high-MgO (6.7-14.8 wt%) tholeiite glasses from the Hawaiian volcanoes Kilauea, Mauna Loa and Haleakala. We determined the rare earth (RE), high field strength, and other selected trace element abundances of these glasses, and used the data to establish their relationship to typical Hawaiian shield tholeiite and to infer characteristics of their source. The glasses have trace element abundance characteristics generally similar to those of typical shield tholeiites, e.g. L(light)REE/H(heavy)REE(C1) > 1. The Kilauea and Mauna Loa glasses, however, display trace and major element characteristics that cross geochemical discriminants observed between Kilauea and Mauna Loa shield lavas. The glasses contain a blend of these discriminating chemical characteristics, and are not exactly like the typical shield lavas from either volcano. The production of these hybrid magmas likely requires a complexly zoned source, rather than two unique sources. When corrected for olivine fractionation, the glass data show correlations between CaO concentration and incompatible trace element abundances, indicating that CaO may behave incompatibly during melting of the tholeiite source. Furthermore, the tholeiite source must contain residual garnet and clinopyroxene to account for the variation in trace element abundances of the Kilauea glasses. Inversion modeling indicates that the Kilauea source is flat relative to C1 chondrites, and has a higher bulk distribution coefficient for the HREE than the LREE.","language":"English","publisher":"Springer","doi":"10.1007/s004100050375","issn":"00107999","usgsCitation":"Wagner, T., Clague, D.A., Hauri, E., and Grove, T., 1998, Trace element abundances of high-MgO glasses from Kilauea, Mauna Loa and Haleakala volcanoes, Hawaii: Contributions to Mineralogy and Petrology, v. 131, no. 1, p. 13-21, https://doi.org/10.1007/s004100050375.","productDescription":"9 p.","startPage":"13","endPage":"21","numberOfPages":"9","costCenters":[],"links":[{"id":227763,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea volcano, Mauna Loa volcano, Haleakala volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.26815795898438,\n 20.594223204225184\n ],\n [\n -156.03195190429688,\n 20.594223204225184\n ],\n [\n -156.03195190429688,\n 20.78564668820214\n ],\n [\n -156.26815795898438,\n 20.78564668820214\n ],\n [\n -156.26815795898438,\n 20.594223204225184\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.32745361328125,\n 19.31114335506464\n ],\n [\n -155.15579223632812,\n 19.31114335506464\n ],\n [\n -155.15579223632812,\n 19.475655495911568\n ],\n [\n -155.32745361328125,\n 19.475655495911568\n ],\n [\n -155.32745361328125,\n 19.31114335506464\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"131","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505bb632e4b08c986b326b06","contributors":{"authors":[{"text":"Wagner, T.P.","contributorId":29143,"corporation":false,"usgs":true,"family":"Wagner","given":"T.P.","email":"","affiliations":[],"preferred":false,"id":383717,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clague, David A.","contributorId":77105,"corporation":false,"usgs":false,"family":"Clague","given":"David","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":383718,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hauri, E.H.","contributorId":66009,"corporation":false,"usgs":true,"family":"Hauri","given":"E.H.","email":"","affiliations":[],"preferred":false,"id":383719,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grove, T.L.","contributorId":22088,"corporation":false,"usgs":true,"family":"Grove","given":"T.L.","email":"","affiliations":[],"preferred":false,"id":383716,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70019806,"text":"70019806 - 1998 - DNA-labeled clay: A sensitive new method for tracing particle transport","interactions":[],"lastModifiedDate":"2024-01-17T00:55:16.79172","indexId":"70019806","displayToPublicDate":"1998-01-01T00:00:00","publicationYear":"1998","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"DNA-labeled clay: A sensitive new method for tracing particle transport","docAbstract":"The behavior of mobile colloids and sediment in most natural environments remains poorly understood, in part because characteristics of existing sediment tracers limit their widespread use. Here we describe the development of a new approach that uses a DNA-labeled montmorillonite clay as a highly sensitive and selective sediment tracer that can potentially characterize sediment and colloid transport in a wide variety of environments, including marine, wetland, ground-water, and atmospheric systems. Characteristics of DNA in natural systems render it unsuitable as an aqueous tracer but admirably suited as a label for tracing particulates. The DNA-labeled-clay approach, using techniques developed from molecular biology, has extremely low detection limits, very specific detection, and a virtually infinite number of tracer signatures. Furthermore, DNA-labeled clay has the same physical characteristics as the particles it is designed to trace, it is environmentally benign, and it can be relatively inexpensively produced and detected. Our initial results show that short (500 base pair) strands of synthetically produced DNA reversibly adsorb to both Na-montmorillonite and powdered silica surfaces via a magnesium bridge. The DNA-montmorillonite surface complexes are stable in calcium-bicarbonate spring waters for periods of up to 18 days and only slowly desorb to the aqueous phase, whereas the silica surface complex is stable only in distilled water. Both materials readily release the adsorbed DNA in dilute EDTA solutions for amplification by the polymerase chain reaction (PCR) and quantification. The stability of the DNA-labeled clay complex suggests that this material would be appropriate for use as an extremely sensitive sediment tracer for flow periods of as long as 2 weeks, and possibly longer.
We compared strip transect and radio-tracking methods of determining foraging range of Black-legged Kittiwakes (Rissa tridactyla). The mean distance birds were observed from their colony determined by radio-tracking was significantly greater than the mean value calculated from strip transects. We determined that this difference was due to two sources of bias: (1) as distance from the colony increased, the area of available habitat also increased resulting in decreasing bird densities (bird spreading). Consequently, the probability of detecting birds during transect surveys also would decrease as distance from the colony increased, and (2) the maximum distance birds were observed from the colony during radio-tracking exceeded the extent of the strip transect survey. We compared the observed number of birds seen on the strip transect survey to the predictions of a model of the decreasing probability of detection due to bird spreading. Strip transect data were significantly different from modeled data; however, the field data were consistently equal to or below the model predictions, indicating a general conformity to the concept of declining detection at increasing distance. We conclude that radio-tracking data gave a more representative indication of foraging distances than did strip transect sampling. Previous studies of seabirds that have used strip transect sampling without accounting for bird spreading or the effects of study-area limitations probably underestimated foraging range.
","language":"English","publisher":"Cooper Ornithological Society","doi":"10.2307/1369753","usgsCitation":"Ostrand, W.D., Drew, G., Suryan, R., and McDonald, L., 1998, Evaluation of radio-tracking and strip transect methods for determining foraging ranges of Black-Legged Kittiwakes: The Condor, v. 100, no. 4, p. 709-718, https://doi.org/10.2307/1369753.","productDescription":"10 p.","startPage":"709","endPage":"718","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":487360,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2307/1369753","text":"Publisher Index Page"},{"id":230069,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"100","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a0cb4e4b0c8380cd52c74","contributors":{"authors":[{"text":"Ostrand, William D.","contributorId":90898,"corporation":false,"usgs":false,"family":"Ostrand","given":"William","email":"","middleInitial":"D.","affiliations":[{"id":609,"text":"Utah Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":389499,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Drew, G.S.","contributorId":95415,"corporation":false,"usgs":true,"family":"Drew","given":"G.S.","email":"","affiliations":[],"preferred":false,"id":389500,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Suryan, R.M.","contributorId":52919,"corporation":false,"usgs":true,"family":"Suryan","given":"R.M.","email":"","affiliations":[],"preferred":false,"id":389498,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McDonald, L.L.","contributorId":19906,"corporation":false,"usgs":true,"family":"McDonald","given":"L.L.","email":"","affiliations":[],"preferred":false,"id":389497,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70021383,"text":"70021383 - 1998 - Sensitivity of condition indices to changing density in a white-tailed deer population","interactions":[],"lastModifiedDate":"2024-07-02T11:26:17.838032","indexId":"70021383","displayToPublicDate":"1998-01-01T00:00:00","publicationYear":"1998","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2507,"text":"Journal of Wildlife Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Sensitivity of condition indices to changing density in a white-tailed deer population","docAbstract":"The ways in which comprehensive condition profiles, incorporating morphometric, histologic, physiologic, and diet quality indices, responded to changes in density of a white-tailed deer (Odocoileus virginianus) population were examined. Changes in these condition indices were monitored in a northeastern Oklahoma deer herd as density declined from peaks of 80 and 72 deer/km2 in 1989 and 1990 (high-density) to lows of 39 and 41 deer/km2 in 1991 and 1992 (reduced-density), respectively. Compared to a reference population (6 deer/km2), deer sampled during high-density exhibited classic signs of nutritional stress such as low body and visceral organ masses (except elevated adrenal gland mass), low fecal nitrogen levels, reduced concentrations of serum albumin, elevated serum creatinine concentrations, and a high prevalence of parasitic infections. Although density declined by one half over the 4-yr study, gross indices of condition (in particular body mass and size) remained largely unchanged. However, selected organ masses, serum albumin and non-protein nitrogen constituents, and fecal nitrogen indices reflected improvements in nutritional status with reductions in density. Many commonly used indices of deer condition (fat reserves, hematocrit, total serum protein, and blood urea nitrogen) were not responsive to fluctuations in density.
Monte Carlo simulations were conducted to evaluate robustness of four tests to detect density dependence, from series of population abundances, to the addition of sampling variance. Population abundances were generated from random walk, stochastic exponential growth, and density-dependent population models. Population abundance estimates were generated with sampling variances distributed as lognormal and constant coefficients of variation (cv) from 0.00 to 1.00. In general, when data were generated under a random walk, Type I error rates increased rapidly for Bulmer's R, Pollard et al.'s, and Dennis and Taper's tests with increasing magnitude of sampling variance for n > 5 yr and all values of process variation. Bulmer's R* test maintained a constant 5% Type I error rate for n > 5 yr and all magnitudes of sampling variance in the population abundance estimates. When abundances were generated from two stochastic exponential growth models (R = 0.05 and R = 0.10), Type I errors again increased with increasing sampling variance; magnitude of Type I error rates were higher for the slower growing population. Therefore, sampling error inflated Type I error rates, invalidating the tests, for all except Bulmer's R* test. Comparable simulations for abundance estimates generated from a density-dependent growth rate model were conducted to estimate power of the tests. Type II error rates were influenced by the relationship of initial population size to carrying capacity (K), length of time series, as well as sampling error. Given the inflated Type I error rates for all but Bulmer's R*, power was overestimated for the remaining tests, resulting in density dependence being detected more often than it existed. Population abundances of natural populations are almost exclusively estimated rather than censused, assuring sampling error. Therefore, because these tests have been shown to be either invalid when only sampling variance occurs in the population abundances (Bulmer's R, Pollard et al.'s, and Dennis and Taper's tests) or lack power (Bulmer's R* test), little justification exists for use of such tests to support or refute the hypothesis of density dependence.
","language":"English","publisher":"Ecological Society of America","doi":"10.1890/0012-9615(1998)068[0445:SVEODD]2.0.CO;2","usgsCitation":"Shenk, T.M., White, G.C., and Burnham, K.P., 1998, Sampling-variance effects on detecting density dependence from temporal trends in natural populations: Ecological Monographs, v. 68, no. 3, p. 445-463, https://doi.org/10.1890/0012-9615(1998)068[0445:SVEODD]2.0.CO;2.","productDescription":"19 p.","startPage":"445","endPage":"463","numberOfPages":"19","costCenters":[{"id":189,"text":"Colorado Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"links":[{"id":230070,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"68","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505ab097e4b0c8380cd87bb8","contributors":{"authors":[{"text":"Shenk, Tanya M","contributorId":221010,"corporation":false,"usgs":false,"family":"Shenk","given":"Tanya","email":"","middleInitial":"M","affiliations":[{"id":40309,"text":"NPS, Lincoln, NE","active":true,"usgs":false}],"preferred":false,"id":389623,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Gary C.","contributorId":26256,"corporation":false,"usgs":true,"family":"White","given":"Gary","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":389622,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burnham, Kenneth P.","contributorId":95025,"corporation":false,"usgs":true,"family":"Burnham","given":"Kenneth","email":"","middleInitial":"P.","affiliations":[{"id":189,"text":"Colorado Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":389624,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70021367,"text":"70021367 - 1998 - Mapping the global land surface using 1 km AVHRR data","interactions":[],"lastModifiedDate":"2017-04-07T15:07:20","indexId":"70021367","displayToPublicDate":"1998-01-01T00:00:00","publicationYear":"1998","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3455,"text":"Space Technology","active":true,"publicationSubtype":{"id":10}},"title":"Mapping the global land surface using 1 km AVHRR data","docAbstract":"The scientific requirements for mapping the global land surface using 1 km advanced very high resolution radiometer (AVHRR) data have been set forth by the U.S. Global Change Research Program; the International Geosphere Biosphere Programme (IGBP); The United Nations; the National Oceanic and Atmospheric Administration (NOAA); the Committee on Earth Observations Satellites; and the National Aeronautics and Space Administration (NASA) mission to planet Earth (MTPE) program. Mapping the global land surface using 1 km AVHRR data is an international effort to acquire, archive, process, and distribute 1 km AVHRR data to meet the needs of the international science community. A network of AVHRR receiving stations, along with data recorded by NOAA, has been acquiring daily global land coverage since April 1, 1992. A data set of over 70,000 AVHRR images is archived and distributed by the United States Geological Survey (USGS) EROS Data Center, and the European Space Agency. Under the guidance of the IGBP, processing standards have been developed for calibration, atmospheric correction, geometric registration, and the production of global 10-day maximum normalized difference vegetation index (NDVI) composites. The major uses of the composites are for the study of surface vegetation condition, mapping land cover, and deriving biophysical characteristics of terrestrial ecosystems. A time-series of 54 10-day global vegetation index composites for the period of April 1, 1992 through September 1993 has been produced. The production of a time-series of 33 10-day global vegetation index composites using NOAA-14 data for the period of February 1, 1995 through December 31, 1995 is underway. The data products are available from the USGS, in cooperation with NASA's MTPE program and other international organizations.","language":"English","publisher":"Elsevier","issn":"08929270","usgsCitation":"Lauer, D.T., and Eidenshink, J., 1998, Mapping the global land surface using 1 km AVHRR data: Space Technology, v. 18, no. 1-2, p. 71-76.","productDescription":"6 p.","startPage":"71","endPage":"76","numberOfPages":"6","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":230031,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","issue":"1-2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a5082e4b0c8380cd6b724","contributors":{"authors":[{"text":"Lauer, D. T.","contributorId":47907,"corporation":false,"usgs":true,"family":"Lauer","given":"D.","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":389621,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eidenshink, J.C.","contributorId":11747,"corporation":false,"usgs":true,"family":"Eidenshink","given":"J.C.","email":"","affiliations":[],"preferred":false,"id":389620,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":1001716,"text":"1001716 - 1998 - Professionals in environmental education: Helping kids learn about forestry","interactions":[],"lastModifiedDate":"2024-04-10T16:09:24.174354","indexId":"1001716","displayToPublicDate":"1998-01-01T00:00:00","publicationYear":"1998","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2297,"text":"Journal of Forestry","onlineIssn":"1938-3746","printIssn":"0022-1201","active":true,"publicationSubtype":{"id":10}},"title":"Professionals in environmental education: Helping kids learn about forestry","docAbstract":"A K--8 school in a suburb of St. Paul has formed a partnership with natural resource professionals to create a school forest for environmental education in the field. The university and agency professionals worked with students to teach them the skills necessary to map and inventory their school grounds. Students then used the inventory data to develop a school forest plan. Over the past four years students have been implementing their plans for both field and classroom work and cross-curricular activities. With the program entering its fifth year, we can advise others looking for similar opportunities.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jof/96.2.25","usgsCitation":"Anderson, D., Thompson, J., and Jakes, P., 1998, Professionals in environmental education: Helping kids learn about forestry: Journal of Forestry, v. 96, no. 2, p. 25-29, https://doi.org/10.1093/jof/96.2.25.","productDescription":"5 p.","startPage":"25","endPage":"29","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":479740,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jof/96.2.25","text":"Publisher Index Page"},{"id":129040,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"96","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a9be4b07f02db65e2ad","contributors":{"authors":[{"text":"Anderson, D.H.","contributorId":24304,"corporation":false,"usgs":true,"family":"Anderson","given":"D.H.","email":"","affiliations":[],"preferred":false,"id":311573,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thompson, J.L.","contributorId":33258,"corporation":false,"usgs":true,"family":"Thompson","given":"J.L.","email":"","affiliations":[],"preferred":false,"id":311574,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jakes, P.J.","contributorId":69925,"corporation":false,"usgs":true,"family":"Jakes","given":"P.J.","email":"","affiliations":[],"preferred":false,"id":311575,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70019788,"text":"70019788 - 1998 - Oyster resource zones of the Barataria and Terrebonne estuaries of Louisiana","interactions":[],"lastModifiedDate":"2012-03-12T17:19:19","indexId":"70019788","displayToPublicDate":"1998-01-01T00:00:00","publicationYear":"1998","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2455,"text":"Journal of Shellfish Research","active":true,"publicationSubtype":{"id":10}},"title":"Oyster resource zones of the Barataria and Terrebonne estuaries of Louisiana","docAbstract":"A 1:100,000 scale map delineating the subtidal oyster resource zones within the Barataria and Terrebonne estuaries was developed. Strategies to accomplish the task included interviews with Louisiana oystermen and state biologists to develop a draft map, field sampling to document oyster (Crassostrea virginica), Dermo (Perkinsus marinus), and oyster drill (Stramonita haemastoma) abundances, use of historical salinity data to aid in map verification, and public meetings to allow comment on a draft before final map preparation. Four oyster resource zones were delineated on the final map: a dry zone where subtidal oysters may be found when salinities increase, a wet zone where subtidal oysters may be found when salinities are suppressed, a wet-dry zone where subtidal oysters may be consistently found due to favorable salinities, and a high-salinity zone where natural oyster populations are predominantly found in intertidal and shallow waters. The dry zone is largely coincident with the brackish-marsh habitat, with some intermediate-type marsh. The wet-dry zone is found at the interface of the brackish and saline marshes, but extends further seaward than up-estuary. The wet zone and the high salinity zones are areas of mostly open water fringed by salt marshes. The dry zone encompasses 91,775 hectares, of which 48,788 hectares are water (53%). The wet zone encompasses 83,525 hectares, of which 66,958 hectares are water (80%). The wet-dry zone encompasses 171,893 hectares, of which 104,733 hectares are water (61%). The high salinity zone encompasses 125,705 hectares, of which 113,369 hectares are water (90%). There is a clear trend of increasing water habitat in the four zones over the past 30 years, and oysters are now cultivated on bottoms that were once marsh. The map should be useful in managing the effects upon oysters of freshwater diversions into the estuaries. It provides a pre-diversion record of the location of oyster resource zones and should prove helpful in the seaward relocation of oysters leases.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Shellfish Research","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","issn":"07308000","usgsCitation":"Melancon, E., Soniat, T., Cheramie, V., Dugas, R., Barras, J., and Lagarde, M., 1998, Oyster resource zones of the Barataria and Terrebonne estuaries of Louisiana: Journal of Shellfish Research, v. 17, no. 4, p. 1143-1148.","startPage":"1143","endPage":"1148","numberOfPages":"6","costCenters":[],"links":[{"id":228176,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a72d0e4b0c8380cd76cf2","contributors":{"authors":[{"text":"Melancon, E. Jr.","contributorId":42732,"corporation":false,"usgs":true,"family":"Melancon","given":"E.","suffix":"Jr.","email":"","affiliations":[],"preferred":false,"id":383912,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soniat, T.","contributorId":72148,"corporation":false,"usgs":true,"family":"Soniat","given":"T.","email":"","affiliations":[],"preferred":false,"id":383915,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cheramie, V.","contributorId":33865,"corporation":false,"usgs":true,"family":"Cheramie","given":"V.","email":"","affiliations":[],"preferred":false,"id":383910,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dugas, R.","contributorId":54360,"corporation":false,"usgs":true,"family":"Dugas","given":"R.","email":"","affiliations":[],"preferred":false,"id":383914,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barras, J.","contributorId":35488,"corporation":false,"usgs":true,"family":"Barras","given":"J.","email":"","affiliations":[],"preferred":false,"id":383911,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lagarde, M.","contributorId":42733,"corporation":false,"usgs":true,"family":"Lagarde","given":"M.","email":"","affiliations":[],"preferred":false,"id":383913,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70020835,"text":"70020835 - 1998 - Continuous lake-sediment records of glaciation in the Sierra Nevada between 52,600 and 12,500 14C yr B.P.","interactions":[],"lastModifiedDate":"2018-09-19T10:42:29","indexId":"70020835","displayToPublicDate":"1998-01-01T00:00:00","publicationYear":"1998","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3218,"text":"Quaternary Research","active":true,"publicationSubtype":{"id":10}},"title":"Continuous lake-sediment records of glaciation in the Sierra Nevada between 52,600 and 12,500 14C yr B.P.","docAbstract":"The chemistry of the carbonate-free clay-size fraction of Owens Lake sediments supports the use of total organic carbon and magnetic susceptibility as indicators of stadial-interstadial oscillations. Owens Lake records of total organic carbon, magnetic susceptibility, and chemical composition of the carbonate-free, clay-size fraction indicate that Tioga glaciation began ~24,500 and ended by ~13,600 14C yr B.P. Many of the components of glacial rock flour (e.g., TiO2, MnO, BaO) found in Owens Lake sediments achieved maximum values during the Tioga glaciation when valley glaciers reached their greatest extent. Total organic carbon and SiO2 (amorphous) concentrations reached minimum values during Tioga glaciation, resulting from decreases in productivity that accompanied the introduction of rock flour into the surface waters of Owens Lake. At least 20 stadial-interstadial oscillations occurred in the Sierra Nevada between 52,600 and 14,000 14C yr B.P. Total organic carbon data from a Pyramid Lake sediment core also indicate oscillations in glacier activity between >39,500 and ~13,600 14C yr B.P. Alpine glacier oscillations occurred on a frequency of ???1900 yr in both basins, suggesting that millennial-scale oscillations occurred in California and Nevada during most of the past 52,600 yr.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Quaternary Research","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","doi":"10.1006/qres.1998.1993","issn":"00335894","usgsCitation":"Benson, L.V., May, H.M., Antweiler, R.C., Brinton, T., Kashgarian, M., Smoot, J.P., and Lund, S., 1998, Continuous lake-sediment records of glaciation in the Sierra Nevada between 52,600 and 12,500 14C yr B.P.: Quaternary Research, v. 50, no. 2, p. 113-127, https://doi.org/10.1006/qres.1998.1993.","startPage":"113","endPage":"127","numberOfPages":"15","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":229797,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":206449,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1006/qres.1998.1993"}],"volume":"50","issue":"2","noUsgsAuthors":false,"publicationDate":"2017-01-20","publicationStatus":"PW","scienceBaseUri":"5059fa5ce4b0c8380cd4da83","contributors":{"authors":[{"text":"Benson, L. V.","contributorId":50159,"corporation":false,"usgs":true,"family":"Benson","given":"L.","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":387700,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"May, Howard M.","contributorId":27202,"corporation":false,"usgs":true,"family":"May","given":"Howard","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":387698,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Antweiler, Ronald C. 0000-0001-5652-6034 antweil@usgs.gov","orcid":"https://orcid.org/0000-0001-5652-6034","contributorId":1481,"corporation":false,"usgs":true,"family":"Antweiler","given":"Ronald","email":"antweil@usgs.gov","middleInitial":"C.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":387699,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brinton, T.I.","contributorId":93922,"corporation":false,"usgs":true,"family":"Brinton","given":"T.I.","affiliations":[],"preferred":false,"id":387703,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kashgarian, Michaele","contributorId":68473,"corporation":false,"usgs":true,"family":"Kashgarian","given":"Michaele","email":"","affiliations":[],"preferred":false,"id":387702,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smoot, J. P.","contributorId":65878,"corporation":false,"usgs":true,"family":"Smoot","given":"J.","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":387701,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lund, S.P.","contributorId":98054,"corporation":false,"usgs":true,"family":"Lund","given":"S.P.","email":"","affiliations":[],"preferred":false,"id":387704,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70020130,"text":"70020130 - 1998 - Regionalization of precipitation characteristics in Montana using L-moments","interactions":[],"lastModifiedDate":"2026-05-01T16:09:46.918438","indexId":"70020130","displayToPublicDate":"1998-01-01T00:00:00","publicationYear":"1998","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3647,"text":"Transportation Research Record","active":true,"publicationSubtype":{"id":10}},"title":"Regionalization of precipitation characteristics in Montana using L-moments","docAbstract":"Dimensionless precipitation-frequency curves for estimating precipitation depths having small exceedance probabilities were developed for 2-, 6-, and 24-hour storm durations for three homogeneous regions in Montana. L-moment statistics were used to help define the homogeneous regions. The generalized extreme value distribution was used to construct the frequency curves for each duration within each region. The effective record length for each duration in each region was estimated using a graphical method and was found to range from 500 years for 6-hour duration data in Region 2 to 5,100 years for 24-hour duration data in Region 3. The temporal characteristics of storms were analyzed, and methods for estimating synthetic storm hyetographs were developed. Dimensionless depth-duration data were grouped by independent duration (2, 6, and 24 hours) and by region, and the beta distribution was fit to dimensionless depth data for various incremental time intervals. Ordinary least-squares regression was used to develop relations between dimensionless depths for a key, short duration—termed the kernel duration—and dimensionless depths for other durations. The regression relations were used, together with the probabilistic dimensionless depth data for the kernel duration, to calculate dimensionless depth-duration curves for exceedance probabilities from .1 to .9. Dimensionless storm hyetographs for each independent duration in each region were constructed for median value conditions based on an exceedance probability of .5.
","language":"English","publisher":"National Academy of Sciences","doi":"10.3141/1647-06","issn":"03611981","usgsCitation":"Parrett, C., 1998, Regionalization of precipitation characteristics in Montana using L-moments: Transportation Research Record, v. 1647, no. 1, p. 43-52, https://doi.org/10.3141/1647-06.","productDescription":"10 p.","startPage":"43","endPage":"52","costCenters":[],"links":[{"id":227869,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.00740965823002,\n 48.997475327628905\n ],\n [\n -116.1125230650945,\n 48.101129731356835\n ],\n [\n -114.65654820719288,\n 45.59696530186227\n ],\n [\n -113.44841301400788,\n 44.381503081195234\n ],\n [\n -111.11484302399928,\n 44.50670930070478\n ],\n [\n -110.9703879589953,\n 44.94974414709357\n ],\n [\n -104.10350153767746,\n 44.954221683738865\n ],\n [\n -104.10350153767746,\n 48.997475327628905\n ],\n [\n -116.00740965823002,\n 48.997475327628905\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"1647","issue":"1","noUsgsAuthors":false,"publicationDate":"1998-01-01","publicationStatus":"PW","scienceBaseUri":"50e4a5ace4b0e8fec6cdbedc","contributors":{"authors":[{"text":"Parrett, C.","contributorId":43400,"corporation":false,"usgs":true,"family":"Parrett","given":"C.","email":"","affiliations":[],"preferred":false,"id":385140,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}