Ground motion selection and scaling comprises undoubtedly the most important component of any seismic risk assessment study that involves time-history analysis. Ironically, this is also the single parameter with the least guidance provided in current building codes, resulting in the use of mostly subjective choices in design. The relevant research to date has been primarily on single-degree-of-freedom systems, with only a few studies using multi-degree-of-freedom systems. Furthermore, the previous research is based solely on numerical simulations with no experimental data available for the validation of the results. By contrast, the research effort described in this paper focuses on an experimental evaluation of selected ground motion scaling methods based on small-scale shake-table experiments of re-configurable linear-elastic and nonlinear multi-story building frame structure models. Ultimately, the experimental results will lead to the development of guidelines and procedures to achieve reliable demand estimates from nonlinear response history analysis in seismic design. In this paper, an overview of this research effort is discussed and preliminary results based on linear-elastic dynamic response are presented.
","largerWorkTitle":"Structures Congress 2011 - Proceedings of the 2011 Structures Congress","conferenceTitle":"Structures Congress 2011","conferenceDate":"April 14-16, 2011","conferenceLocation":"Las Vegas, NV","language":"English","publisher":"American Society of Civil Engineers","doi":"10.1061/41171(401)265","isbn":"9780784411711","usgsCitation":"O’Donnell, A.P., Beltsar, O., Kurama, Y., Kalkan, E., and Taflanidis, A., 2011, Evaluation of ground motion scaling methods for analysis of structural systems, in Structures Congress 2011 - Proceedings of the 2011 Structures Congress, Las Vegas, NV, April 14-16, 2011, p. 3045-3056, https://doi.org/10.1061/41171(401)265.","productDescription":"12 p.","startPage":"3045","endPage":"3056","numberOfPages":"12","costCenters":[],"links":[{"id":246539,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":218520,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1061/41171(401)265"}],"noUsgsAuthors":false,"publicationDate":"2012-04-26","publicationStatus":"PW","scienceBaseUri":"505a0c7fe4b0c8380cd52b91","contributors":{"authors":[{"text":"O’Donnell, A. P.","contributorId":107550,"corporation":false,"usgs":true,"family":"O’Donnell","given":"A.","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":455042,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beltsar, O.A.","contributorId":40459,"corporation":false,"usgs":true,"family":"Beltsar","given":"O.A.","email":"","affiliations":[],"preferred":false,"id":455039,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kurama, Y.C.","contributorId":89389,"corporation":false,"usgs":true,"family":"Kurama","given":"Y.C.","affiliations":[],"preferred":false,"id":455041,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kalkan, E. 0000-0002-9138-9407","orcid":"https://orcid.org/0000-0002-9138-9407","contributorId":8212,"corporation":false,"usgs":true,"family":"Kalkan","given":"E.","affiliations":[],"preferred":false,"id":455038,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Taflanidis, A.A.","contributorId":85450,"corporation":false,"usgs":true,"family":"Taflanidis","given":"A.A.","affiliations":[],"preferred":false,"id":455040,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70034937,"text":"70034937 - 2011 - Compositional diversity and geologic insights of the Aristarchus crater from Moon Mineralogy Mapper data","interactions":[],"lastModifiedDate":"2017-06-29T13:41:02","indexId":"70034937","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2317,"text":"Journal of Geophysical Research E: Planets","active":true,"publicationSubtype":{"id":10}},"title":"Compositional diversity and geologic insights of the Aristarchus crater from Moon Mineralogy Mapper data","docAbstract":"The Moon Mineralogy Mapper (M3) acquired high spatial and spectral resolution data of the Aristarchus Plateau with 140 m/pixel in 85 spectral bands from 0.43 to 3.0 m. The data were collected as radiance and converted to reflectance using the observational constraints and a solar spectrum scaled to the Moon-Sun distance. Summary spectral parameters for the area of mafic silicate 1 and 2 m bands were calculated from the M3 data and used to map the distribution of key units that were then analyzed in detail with the spectral data. This analysis focuses on five key compositional units in the region. (1) The central peaks are shown to be strongly enriched in feldspar and are likely from the upper plagioclase-rich crust of the Moon. (2) The impact melt is compositionally diverse with clear signatures of feldspathic crust, olivine, and glass. (3) The crater walls and ejecta show a high degree of spatial heterogeneity and evidence for massive breccia blocks. (4) Olivine, strongly concentrated on the rim, wall, and exterior of the southeastern quadrant of the crater, is commonly associated the impact melt. (5) There are at least two types of glass deposits observed: pyroclastic glass and impact glass. Copyright 2011 by the American Geophysical Union.","language":"English","publisher":"AGU","doi":"10.1029/2010JE003726","issn":"01480227","usgsCitation":"Mustard, J., Pieters, C., Isaacson, P., Head, J., Besse, S., Clark, R.N., Klima, R., Petro, N., Staid, M., Sunshine, J., Runyon, C., and Tompkins, S., 2011, Compositional diversity and geologic insights of the Aristarchus crater from Moon Mineralogy Mapper data: Journal of Geophysical Research E: Planets, v. 116, no. 5, Article E00G12; 17 p., https://doi.org/10.1029/2010JE003726.","productDescription":"Article E00G12; 17 p.","ipdsId":"IP-024467","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":475441,"rank":10000,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2010je003726","text":"Publisher Index Page"},{"id":243837,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":215998,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1029/2010JE003726"}],"volume":"116","issue":"5","noUsgsAuthors":false,"publicationDate":"2011-05-10","publicationStatus":"PW","scienceBaseUri":"5059f936e4b0c8380cd4d4d4","contributors":{"authors":[{"text":"Mustard, J.F.","contributorId":91605,"corporation":false,"usgs":true,"family":"Mustard","given":"J.F.","email":"","affiliations":[],"preferred":false,"id":448410,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pieters, C.M.","contributorId":48733,"corporation":false,"usgs":true,"family":"Pieters","given":"C.M.","email":"","affiliations":[{"id":16929,"text":"Brown University","active":true,"usgs":false}],"preferred":false,"id":448403,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Isaacson, P.J.","contributorId":63236,"corporation":false,"usgs":true,"family":"Isaacson","given":"P.J.","email":"","affiliations":[],"preferred":false,"id":448405,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Head, J.W.","contributorId":67982,"corporation":false,"usgs":true,"family":"Head","given":"J.W.","email":"","affiliations":[],"preferred":false,"id":448406,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Besse, S.","contributorId":79330,"corporation":false,"usgs":true,"family":"Besse","given":"S.","email":"","affiliations":[],"preferred":false,"id":448409,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Clark, R. N.","contributorId":6568,"corporation":false,"usgs":true,"family":"Clark","given":"R.","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":448399,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Klima, R.L.","contributorId":29238,"corporation":false,"usgs":true,"family":"Klima","given":"R.L.","email":"","affiliations":[],"preferred":false,"id":448402,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Petro, N.E.","contributorId":18999,"corporation":false,"usgs":true,"family":"Petro","given":"N.E.","email":"","affiliations":[],"preferred":false,"id":448400,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Staid, M.I.","contributorId":76505,"corporation":false,"usgs":true,"family":"Staid","given":"M.I.","email":"","affiliations":[],"preferred":false,"id":448408,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sunshine, J.M.","contributorId":74591,"corporation":false,"usgs":true,"family":"Sunshine","given":"J.M.","email":"","affiliations":[],"preferred":false,"id":448407,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Runyon, C.J.","contributorId":19398,"corporation":false,"usgs":true,"family":"Runyon","given":"C.J.","email":"","affiliations":[],"preferred":false,"id":448401,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Tompkins, S.","contributorId":51123,"corporation":false,"usgs":true,"family":"Tompkins","given":"S.","email":"","affiliations":[],"preferred":false,"id":448404,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70035119,"text":"70035119 - 2011 - Monitoring landscape change for LANDFIRE using multi-temporal satellite imagery and ancillary data","interactions":[],"lastModifiedDate":"2013-03-18T13:20:59","indexId":"70035119","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1942,"text":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring landscape change for LANDFIRE using multi-temporal satellite imagery and ancillary data","docAbstract":"LANDFIRE is a large interagency project designed to provide nationwide spatial data for fire management applications. As part of the effort, many 2000 vintage Landsat Thematic Mapper and Enhanced Thematic Mapper plus data sets were used in conjunction with a large volume of field information to generate detailed vegetation type and structure data sets for the entire United States. In order to keep these data sets current and relevant to resource managers, there was strong need to develop an approach for updating these products. We are using three different approaches for these purposes. These include: 1) updating using Landsat-derived historic and current fire burn information derived from the Monitoring Trends in Burn Severity project; 2) incorporating vegetation disturbance information derived from time series Landsat data analysis using the Vegetation Change Tracker; and 3) developing data products that capture subtle intra-state disturbance such as those related to insects and disease using either Landsat or the Moderate Resolution Imaging Spectroradiometer (MODIS). While no one single approach provides all of the land cover change and update information required, we believe that a combination of all three captures most of the disturbance conditions taking place that have relevance to the fire community.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Institute of Electrical and Electronics Engineers","publisherLocation":"New York, NY","doi":"10.1109/JSTARS.2010.2044478","usgsCitation":"Vogelmann, J., Kost, J.R., Tolk, B., Howard, S.M., Short, K., Chen, X., Huang, C., Pabst, K., and Rollins, M.G., 2011, Monitoring landscape change for LANDFIRE using multi-temporal satellite imagery and ancillary data: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 4, no. 2, p. 252-264, https://doi.org/10.1109/JSTARS.2010.2044478.","startPage":"252","endPage":"264","numberOfPages":"13","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":475055,"rank":10000,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.473.2217","text":"External Repository"},{"id":215417,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1109/JSTARS.2010.2044478"},{"id":243223,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a5dbae4b0c8380cd7056f","contributors":{"authors":[{"text":"Vogelmann, James E. 0000-0002-0804-5823 vogel@usgs.gov","orcid":"https://orcid.org/0000-0002-0804-5823","contributorId":649,"corporation":false,"usgs":true,"family":"Vogelmann","given":"James E.","email":"vogel@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":449369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kost, Jay R. jkost@usgs.gov","contributorId":3931,"corporation":false,"usgs":true,"family":"Kost","given":"Jay","email":"jkost@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":true,"id":449371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tolk, Brian 0000-0002-9060-0266","orcid":"https://orcid.org/0000-0002-9060-0266","contributorId":62426,"corporation":false,"usgs":true,"family":"Tolk","given":"Brian","affiliations":[],"preferred":false,"id":449377,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Howard, Stephen M. 0000-0001-5255-5882 smhoward@usgs.gov","orcid":"https://orcid.org/0000-0001-5255-5882","contributorId":3483,"corporation":false,"usgs":true,"family":"Howard","given":"Stephen","email":"smhoward@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":449370,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Short, Karen","contributorId":33940,"corporation":false,"usgs":true,"family":"Short","given":"Karen","affiliations":[],"preferred":false,"id":449375,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chen, Xuexia","contributorId":14213,"corporation":false,"usgs":true,"family":"Chen","given":"Xuexia","affiliations":[],"preferred":false,"id":449373,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huang, Chengquan","contributorId":25378,"corporation":false,"usgs":true,"family":"Huang","given":"Chengquan","affiliations":[],"preferred":false,"id":449374,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pabst, Kari","contributorId":12284,"corporation":false,"usgs":true,"family":"Pabst","given":"Kari","email":"","affiliations":[],"preferred":false,"id":449372,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rollins, Matthew G.","contributorId":54695,"corporation":false,"usgs":true,"family":"Rollins","given":"Matthew","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":449376,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70035118,"text":"70035118 - 2011 - The development of an EDSS: Lessons learned and implications for DSS research","interactions":[],"lastModifiedDate":"2021-06-14T19:51:32.448528","indexId":"70035118","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"The development of an EDSS: Lessons learned and implications for DSS research","docAbstract":"The Solar and Wind Energy Resource Assessment (SWERA) project is focused on providing renewable energy (RE) planning resources to the public. Examples include wind, solar, and hydro assessments. SWERA DSS consists of three major components. First, SWERA 'Product Archive' provides for a discovery DSS upon which users can find and access renewable energy data and supporting models. Second, the 'Renewable Resource EXplorer' (RREX) component serves as a web-based, GIS analysis tool for viewing RE resource data available through the SWERA Product Archive. Third, the SWERA web service provides computational access to the data available in the SWERA spatial database through a location based query, and is also utilized in the RREX component. We provide a discussion of various design decisions used in the construction of this EDSS, followed by project experiences and implications for EDSS and broader DSS research.
","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"Proceedings of the Annual Hawaii International Conference on System Sciences","largerWorkSubtype":{"id":19,"text":"Conference Paper"},"conferenceTitle":"44th Hawaii International Conference on System Sciences, HICSS-44 2010","conferenceDate":"January, 4-7,2011","conferenceLocation":"Koloa, Kauai, HI","language":"English","publisher":"IEEE","doi":"10.1109/HICSS.2011.405","issn":"15301605","isbn":"9780769542829","usgsCitation":"El-Gayar, O., Deokar, A., Michels, L., and Fosnight, E.A., 2011, The development of an EDSS: Lessons learned and implications for DSS research, in Proceedings of the Annual Hawaii International Conference on System Sciences, Koloa, Kauai, HI, January, 4-7,2011, 10 p., https://doi.org/10.1109/HICSS.2011.405.","productDescription":"10 p.","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":243192,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":215392,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1109/HICSS.2011.405"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505baaa4e4b08c986b3228ec","contributors":{"authors":[{"text":"El-Gayar, O.","contributorId":64914,"corporation":false,"usgs":true,"family":"El-Gayar","given":"O.","email":"","affiliations":[],"preferred":false,"id":449365,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Deokar, A.","contributorId":69802,"corporation":false,"usgs":true,"family":"Deokar","given":"A.","email":"","affiliations":[],"preferred":false,"id":449366,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Michels, L.","contributorId":92073,"corporation":false,"usgs":true,"family":"Michels","given":"L.","email":"","affiliations":[],"preferred":false,"id":449367,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fosnight, Eugene A. 0000-0002-8557-3697 fosnight@usgs.gov","orcid":"https://orcid.org/0000-0002-8557-3697","contributorId":2961,"corporation":false,"usgs":true,"family":"Fosnight","given":"Eugene","email":"fosnight@usgs.gov","middleInitial":"A.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":449368,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70034994,"text":"70034994 - 2011 - Transient changes in shallow groundwater chemistry during the MSU ZERT CO2 injection experiment","interactions":[],"lastModifiedDate":"2021-03-08T17:38:55.752959","indexId":"70034994","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Transient changes in shallow groundwater chemistry during the MSU ZERT CO2 injection experiment","docAbstract":"Food-grade CO2 was injected into a shallow aquifer through a perforated pipe placed horizontally 1–2 m below the water table at the Montana State University Zero Emission Research and Technology (MSU-ZERT) field site at Bozeman, Montana. The possible impact of elevated CO2 levels on groundwater quality was investigated by analyzing 80 water samples taken before, during, and following CO2 injection. Field determinations and laboratory analyses showed rapid and systematic changes in pH, alkalinity, and conductance, as well as increases in the aqueous concentrations of trace element species. The geochemical data were first evaluated using principal component analysis (PCA) in order to identify correlations between aqueous species. The PCA findings were then used in formulating a geochemical model to simulate the processes likely to be responsible for the observed increases in the concentrations of dissolved constituents. Modeling was conducted taking into account aqueous and surface complexation, cation exchange, and mineral precipitation and dissolution. Reasonable matches between measured data and model results suggest that: (1) CO2 dissolution in the groundwater causes calcite to dissolve. (2) Observed increases in the concentration of dissolved trace metals result likely from Ca+2-driven ion exchange with clays (smectites) and sorption/desorption reactions likely involving Fe (hydr)oxides. (3) Bicarbonate from CO2 dissolution appears to compete for sorption with anionic species such as HAsO4−2, potentially increasing dissolved As levels in groundwater.
This study investigates stable carbon isotopes (δ13C), opal phytolith assemblages, burnt phytoliths, microscopic charcoal and Sporormiella spores from modern soils and paleosols in Kansas and Oklahoma. Grass and dicot phytoliths in combination with δ13C are used as proxies for reconstructing the structure of grasslands and woodlands. Burnt grass phytoliths and microscopic charcoal are evaluated as proxies for reconstructing paleofire incidence. Concentrations of the fungal spore Sporormiella are used as a proxy for assessing large herbivore activity. These proxies were tested on various modern grassland communities of the central and southern Great Plains, including areas with bison, cattle, and small herbivores, and areas under different fire frequencies.
Opal phytolith assemblages and δ13C values show that before cal 11 ka, C3 grasses and woody plants predominated in areas that today are dominated by C4 grasses. The origin of the shortgrass prairie dates back to about cal 10 ka. The origin of the tallgrass prairie, however, is not clear as phytolith data show variable assemblages throughout the Holocene (mixed-grass, tallgrass, and tallgrass–woodland mosaic). Different proxies (burnt phytoliths vs. charcoal) reveal different fire frequencies, but it is apparent that microfossil evidence for fire incidence is closely related to the abundance of woody plants in the landscape.
Before cal 12 ka, soils show somewhat elevated concentration of Sporormiella, but lower concentrations than the modern high-density bison and cattle grazing areas. Throughout the Holocene, Sporormiella frequencies are low, which suggests lower large ungulate densities and perhaps high mobility.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.catena.2010.08.015","issn":"03418162","usgsCitation":"Cordova, C., Johnson, W., Mandel, R., and Palmer, M., 2011, Late Quaternary environmental change inferred from phytoliths and other soil-related proxies: Case studies from the central and southern Great Plains, USA: Catena, v. 85, no. 2, p. 87-108, https://doi.org/10.1016/j.catena.2010.08.015.","productDescription":"22 p.","startPage":"87","endPage":"108","costCenters":[],"links":[{"id":242852,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas, New Mexico, Oklahoma, Texas","otherGeospatial":"Central and southern Great Plains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.05078125,\n 31.728167146023935\n ],\n [\n -104.94140625,\n 30.90222470517144\n ],\n [\n -103.53515625,\n 29.152161283318915\n ],\n [\n -101.689453125,\n 29.916852233070173\n ],\n [\n -97.822265625,\n 25.799891182088334\n ],\n [\n -97.20703125,\n 27.527758206861886\n ],\n [\n -93.33984375,\n 29.916852233070173\n ],\n [\n -93.955078125,\n 32.69486597787505\n ],\n [\n -94.5703125,\n 35.460669951495305\n ],\n [\n -94.74609375,\n 39.436192999314095\n ],\n [\n -95.25146484374999,\n 40.06125658140474\n ],\n [\n -102.216796875,\n 40.17887331434696\n ],\n [\n -102.3046875,\n 41.11246878918088\n ],\n [\n -109.3359375,\n 40.97989806962013\n ],\n [\n -109.16015624999999,\n 31.27855085894653\n ],\n [\n -107.9296875,\n 31.27855085894653\n ],\n [\n -107.841796875,\n 31.80289258670676\n ],\n [\n -107.05078125,\n 31.728167146023935\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"85","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a4526e4b0c8380cd67098","contributors":{"authors":[{"text":"Cordova, C.E.","contributorId":8303,"corporation":false,"usgs":true,"family":"Cordova","given":"C.E.","email":"","affiliations":[],"preferred":false,"id":448939,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, W.C.","contributorId":68003,"corporation":false,"usgs":true,"family":"Johnson","given":"W.C.","email":"","affiliations":[],"preferred":false,"id":448941,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mandel, R.D.","contributorId":58000,"corporation":false,"usgs":true,"family":"Mandel","given":"R.D.","email":"","affiliations":[],"preferred":false,"id":448940,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Palmer, M.W.","contributorId":88703,"corporation":false,"usgs":true,"family":"Palmer","given":"M.W.","email":"","affiliations":[],"preferred":false,"id":448942,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70035027,"text":"70035027 - 2011 - Evaluating the effect of predators on white-tailed deer: Movement and diet of coyotes","interactions":[],"lastModifiedDate":"2017-04-06T13:43:08","indexId":"70035027","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the effect of predators on white-tailed deer: Movement and diet of coyotes","docAbstract":"Coyotes (Canis latrans) may affect adult and neonate white-tailed deer (Odocoileus virginianus) survival and have been implicated as a contributor to the decline of deer populations. Additionally, coyote diet composition is influenced by prey availability, season, and region. Because coyote movement and diet vary by region, local data are important to understand coyote population dynamics and their impact on prey species. In southeast Minnesota, we investigated the effect of coyotes on white-tailed deer populations by documenting movement rates, distances moved, and habitats searched by coyotes during fawning and nonfawning periods. Additionally, we determined survival, cause-specific mortality, and seasonal diet composition of coyotes. From 2001 to 2003, we captured and radiocollared 30 coyotes. Per-hour rate of movement averaged 0.87 km and was greater (P = 0.046) during the fawning (1.07 km) than the nonfawning period (0.80 km); areas searched were similar (P = 0.175) between seasons. Coyote habitat use differed during both seasons; habitats were not used in proportion to their availability (P < 0.001). Croplands were used more (P < 0.001) than their proportional availability during both seasons. Use of grasslands was greater during the fawning period (P = 0.030), whereas use of cropland was greater in the nonfawning period (P < 0.001). We collected 66 fecal samples during the nonfawning period; coyote diets were primarily composed of Microtus spp. (65.2%), and consumption of deer was 9.1%. During the study, 19 coyotes died; annual survival rate range was 0.33–0.41, which was low compared with other studies. Consumption of deer was low and coyotes searched open areas (i.e., cropland) more than fawning areas with dense cover. These factors in addition to high coyote mortality suggested that coyote predation was not likely limiting white-tailed deer populations in southeast Minnesota.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.109","issn":"0022541X","usgsCitation":"Turner, M., Rockhill, A., Deperno, C., Jenks, J., Klaver, R., Jarding, A., Grovenburg, T., and Pollock, K.H., 2011, Evaluating the effect of predators on white-tailed deer: Movement and diet of coyotes: Journal of Wildlife Management, v. 75, no. 4, p. 905-912, https://doi.org/10.1002/jwmg.109.","productDescription":"8 p.","startPage":"905","endPage":"912","numberOfPages":"8","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":242853,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":215081,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/jwmg.109"}],"volume":"75","issue":"4","noUsgsAuthors":false,"publicationDate":"2011-05-25","publicationStatus":"PW","scienceBaseUri":"505a0bf7e4b0c8380cd52983","contributors":{"authors":[{"text":"Turner, M.M.","contributorId":26895,"corporation":false,"usgs":true,"family":"Turner","given":"M.M.","email":"","affiliations":[],"preferred":false,"id":448943,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rockhill, A.P.","contributorId":70200,"corporation":false,"usgs":true,"family":"Rockhill","given":"A.P.","affiliations":[],"preferred":false,"id":448947,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Deperno, C.S.","contributorId":97870,"corporation":false,"usgs":true,"family":"Deperno","given":"C.S.","affiliations":[],"preferred":false,"id":448949,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jenks, J.A.","contributorId":31726,"corporation":false,"usgs":true,"family":"Jenks","given":"J.A.","email":"","affiliations":[],"preferred":false,"id":448944,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Klaver, R. W. 0000-0002-3263-9701","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":50267,"corporation":false,"usgs":true,"family":"Klaver","given":"R. W.","affiliations":[],"preferred":false,"id":448945,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jarding, A.R.","contributorId":108329,"corporation":false,"usgs":true,"family":"Jarding","given":"A.R.","affiliations":[],"preferred":false,"id":448950,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Grovenburg, T.W.","contributorId":78163,"corporation":false,"usgs":true,"family":"Grovenburg","given":"T.W.","affiliations":[],"preferred":false,"id":448948,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pollock, K. H.","contributorId":65184,"corporation":false,"usgs":false,"family":"Pollock","given":"K.","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":448946,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70034129,"text":"70034129 - 2011 - Geochemical heterogeneity in a small, stratigraphically complex moraine aquifer system (Ontario, Canada): Interpretation of flow and recharge using multiple geochemical parameters","interactions":[],"lastModifiedDate":"2026-01-28T14:29:34.295182","indexId":"70034129","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Geochemical heterogeneity in a small, stratigraphically complex moraine aquifer system (Ontario, Canada): Interpretation of flow and recharge using multiple geochemical parameters","docAbstract":"The Waterloo Moraine is a stratigraphically complex system and is the major water supply to the cities of Kitchener and Waterloo in Ontario, Canada. Despite over 30 years of investigation, no attempt has been made to unify existing geochemical data into a single database. A composite view of the moraine geochemistry has been created using the available geochemical information, and a framework created for geochemical data synthesis of other similar flow systems. Regionally, fluid chemistry is highly heterogeneous, with large variations in both water type and total dissolved solids content. Locally, upper aquifer units are affected by nitrate and chloride from fertilizer and road salt. Typical upper-aquifer fluid chemistry is dominated by calcium, magnesium, and bicarbonate, a result of calcite and dolomite dissolution. Evidence also suggests that ion exchange and diffusion from tills and bedrock units accounts for some elevated sodium concentrations. Locally, hydraulic “windows” cross connect upper and lower aquifer units, which are typically separated by a clay till. Lower aquifer units are also affected by dedolomitization, mixing with bedrock water, and locally, upward diffusion of solutes from the bedrock aquifers. A map of areas where aquifer units are geochemically similar was constructed to highlight areas with potential hydraulic windows.
","language":"English, French","doi":"10.1007/s10040-010-0628-7","issn":"14312174","usgsCitation":"Stotler, R., Frape, S., El Mugammar, H., Johnston, C., Judd-Henrey, I., Harvey, F., Drimmie, R., and Jones, J., 2011, Geochemical heterogeneity in a small, stratigraphically complex moraine aquifer system (Ontario, Canada): Interpretation of flow and recharge using multiple geochemical parameters: Hydrogeology Journal, v. 19, no. 1, p. 101-115, https://doi.org/10.1007/s10040-010-0628-7.","productDescription":"15 p.","startPage":"101","endPage":"115","numberOfPages":"15","costCenters":[],"links":[{"id":244770,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"1","noUsgsAuthors":false,"publicationDate":"2010-07-07","publicationStatus":"PW","scienceBaseUri":"505a1641e4b0c8380cd550ea","contributors":{"authors":[{"text":"Stotler, R.L.","contributorId":39596,"corporation":false,"usgs":true,"family":"Stotler","given":"R.L.","email":"","affiliations":[],"preferred":false,"id":444234,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Frape, S.K.","contributorId":105335,"corporation":false,"usgs":true,"family":"Frape","given":"S.K.","affiliations":[],"preferred":false,"id":444239,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"El Mugammar, H.T.","contributorId":84191,"corporation":false,"usgs":true,"family":"El Mugammar","given":"H.T.","email":"","affiliations":[],"preferred":false,"id":444236,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnston, C.","contributorId":92892,"corporation":false,"usgs":true,"family":"Johnston","given":"C.","email":"","affiliations":[],"preferred":false,"id":444237,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Judd-Henrey, I.","contributorId":28457,"corporation":false,"usgs":true,"family":"Judd-Henrey","given":"I.","email":"","affiliations":[],"preferred":false,"id":444233,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Harvey, F.E.","contributorId":46161,"corporation":false,"usgs":true,"family":"Harvey","given":"F.E.","email":"","affiliations":[],"preferred":false,"id":444235,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Drimmie, R.","contributorId":16679,"corporation":false,"usgs":true,"family":"Drimmie","given":"R.","affiliations":[],"preferred":false,"id":444232,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jones, J.P.","contributorId":101093,"corporation":false,"usgs":true,"family":"Jones","given":"J.P.","email":"","affiliations":[],"preferred":false,"id":444238,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70190330,"text":"70190330 - 2011 - Elevated gas hydrate saturation within silt and silty clay sediments in the Shenhu area, South China Sea","interactions":[],"lastModifiedDate":"2017-08-27T10:26:24","indexId":"70190330","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Elevated gas hydrate saturation within silt and silty clay sediments in the Shenhu area, South China Sea","docAbstract":"Gas hydrate saturations were estimated using five different methods in silt and silty clay foraminiferous sediments from drill hole SH2 in the South China Sea. Gas hydrate saturations derived from observed pore water chloride values in core samples range from 10 to 45% of the pore space at 190–221 m below seafloor (mbsf). Gas hydrate saturations estimated from resistivity (Rt) using wireline logging results are similar and range from 10 to 40.5% in the pore space. Gas hydrate saturations were also estimated by P wave velocity obtained during wireline logging by using a simplified three-phase equation (STPE) and effective medium theory (EMT) models. Gas hydrate saturations obtained from the STPE velocity model (41.0% maximum) are slightly higher than those calculated with the EMT velocity model (38.5% maximum). Methane analysis from a 69 cm long depressurized core from the hydrate-bearing sediment zone indicates that gas hydrate saturation is about 27.08% of the pore space at 197.5 mbsf. Results from the five methods show similar values and nearly identical trends in gas hydrate saturations above the base of the gas hydrate stability zone at depths of 190 to 221 mbsf. Gas hydrate occurs within units of clayey slit and silt containing abundant calcareous nannofossils and foraminifer, which increase the porosities of the fine-grained sediments and provide space for enhanced gas hydrate formation. In addition, gas chimneys, faults, and fractures identified from three-dimensional (3-D) and high-resolution two-dimensional (2-D) seismic data provide pathways for fluids migrating into the gas hydrate stability zone which transport methane for the formation of gas hydrate. Sedimentation and local canyon migration may contribute to higher gas hydrate saturations near the base of the stability zone.
","language":"English","publisher":"Journal of Geophysical Research","doi":"10.1029/2010JB007944","usgsCitation":"Wang, X., Hutchinson, D.R., Wu, S., Yang, S., and Guo, Y., 2011, Elevated gas hydrate saturation within silt and silty clay sediments in the Shenhu area, South China Sea: Journal of Geophysical Research B: Solid Earth, v. 116, no. B5, Article B05102; 18 p., https://doi.org/10.1029/2010JB007944.","productDescription":"Article B05102; 18 p.","ipdsId":"IP-022714","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":475432,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2010jb007944","text":"Publisher Index Page"},{"id":345173,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"South China Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 105,\n 15\n ],\n [\n 121,\n 15\n ],\n [\n 121,\n 24\n ],\n [\n 105,\n 24\n ],\n [\n 105,\n 15\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"116","issue":"B5","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2011-05-25","publicationStatus":"PW","scienceBaseUri":"59a3da31e4b077f005673227","contributors":{"authors":[{"text":"Wang, Xiujuan","contributorId":195861,"corporation":false,"usgs":false,"family":"Wang","given":"Xiujuan","affiliations":[{"id":34424,"text":"Chinese Academy of Sciences, Qingdao, China","active":true,"usgs":false}],"preferred":false,"id":708559,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hutchinson, Deborah R. 0000-0002-2544-5466 dhutchinson@usgs.gov","orcid":"https://orcid.org/0000-0002-2544-5466","contributorId":521,"corporation":false,"usgs":true,"family":"Hutchinson","given":"Deborah","email":"dhutchinson@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":708560,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wu, Shiguo","contributorId":195862,"corporation":false,"usgs":false,"family":"Wu","given":"Shiguo","affiliations":[{"id":34424,"text":"Chinese Academy of Sciences, Qingdao, China","active":true,"usgs":false}],"preferred":false,"id":708561,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yang, Shengxiong","contributorId":195863,"corporation":false,"usgs":false,"family":"Yang","given":"Shengxiong","email":"","affiliations":[{"id":34423,"text":"Guangzhou Marine Geological Survey, Guangzhou, China","active":true,"usgs":false}],"preferred":false,"id":708562,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Guo, Yiqun","contributorId":68659,"corporation":false,"usgs":false,"family":"Guo","given":"Yiqun","affiliations":[{"id":34423,"text":"Guangzhou Marine Geological Survey, Guangzhou, China","active":true,"usgs":false}],"preferred":false,"id":708563,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70032669,"text":"70032669 - 2011 - The influence of fine-scale habitat features on regional variation in population performance of alpine White-tailed Ptarmigan","interactions":[],"lastModifiedDate":"2012-03-12T17:21:22","indexId":"70032669","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1318,"text":"Condor","active":true,"publicationSubtype":{"id":10}},"title":"The influence of fine-scale habitat features on regional variation in population performance of alpine White-tailed Ptarmigan","docAbstract":"It is often assumed (explicitly or implicitly) that animals select habitat features to maximize fitness. However, there is often a mismatch between preferred habitats and indices of individual and population measures of performance. We examined the influence of fine-scale habitat selection on the overall population performance of the White-tailed Ptarmigan (Lagopus leucura), an alpine specialist, in two subdivided populations whose habitat patches are configured differently. The central region of Vancouver Island, Canada, has more continuous and larger habitat patches than the southern region. In 2003 and 2004, using paired logistic regression between used (n = 176) and available (n = 324) sites, we identified food availability, distance to standing water, and predator cover as preferred habitat components . We then quantified variation in population performance in the two regions in terms of sex ratio, age structure (n = 182 adults and yearlings), and reproductive success (n = 98 females) on the basis of 8 years of data (1995-1999, 2002-2004). Region strongly influenced females' breeding success, which, unsuccessful hens included, was consistently higher in the central region (n = 77 females) of the island than in the south (n = 21 females, P = 0.01). The central region also had a much higher proportion of successful hens (87%) than did the south (55%, P < 0.001). In light of our findings, we suggest that population performance is influenced by a combination of fine-scale habitat features and coarse-scale habitat configuration. ?? The Cooper Ornithological Society 2011.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Condor","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","doi":"10.1525/cond.2011.100070","issn":"00105422","usgsCitation":"Fedy, B., and Martin, K., 2011, The influence of fine-scale habitat features on regional variation in population performance of alpine White-tailed Ptarmigan: Condor, v. 113, no. 2, p. 306-315, https://doi.org/10.1525/cond.2011.100070.","startPage":"306","endPage":"315","numberOfPages":"10","costCenters":[],"links":[{"id":475087,"rank":10000,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1525/cond.2011.100070","text":"Publisher Index Page"},{"id":213982,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1525/cond.2011.100070"},{"id":241660,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"113","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505bad21e4b08c986b3239cd","contributors":{"authors":[{"text":"Fedy, B.","contributorId":30461,"corporation":false,"usgs":true,"family":"Fedy","given":"B.","email":"","affiliations":[],"preferred":false,"id":437371,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, K.","contributorId":82666,"corporation":false,"usgs":true,"family":"Martin","given":"K.","affiliations":[],"preferred":false,"id":437372,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70032481,"text":"70032481 - 2011 - Beach monitoring criteria: reading the fine print","interactions":[],"lastModifiedDate":"2013-02-05T15:49:14","indexId":"70032481","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Beach monitoring criteria: reading the fine print","docAbstract":"Beach monitoring programs aim to decrease swimming-related illnesses resulting from exposure to harmful microbes in recreational waters, while providing maximum beach access. Managers are advised by the U.S. EPA to estimate microbiological water quality based on a 5-day geometric mean of fecal indicator bacteria (FIB) concentrations or on a jurisdiction-specific single-sample maximum; however, most opt instead to apply a default single-sample maximum to ease application. We examined whether re-evaluation of the U.S. EPA ambient water quality criteria (AWQC) and the epidemiological studies on which they are based could increase public beach access without affecting presumed health risk. Single-sample maxima were calculated using historic monitoring data for 50 beaches along coastal Lake Michigan on various temporal and spatial groupings to assess flexibility in the application of the AWQC. No calculation on either scale was as low as the default maximum (235 CFU/100 mL) that managers typically use, indicating that current applications may be more conservative than the outlined AWQC. It was notable that beaches subject to point source FIB contamination had lower variation, highlighting the bias in the standards for these beaches. Until new water quality standards are promulgated, more site-specific application of the AWQC may benefit beach managers by allowing swimmers greater access to beaches. This issue will be an important consideration in addressing the forthcoming beach monitoring standards.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Environmental Science and Technology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"ACS Publications (American Chemical Society)","publisherLocation":"Washington, D.C.","doi":"10.1021/es202568f","issn":"0013936X","usgsCitation":"Nevers, M.B., and Whitman, R.L., 2011, Beach monitoring criteria: reading the fine print: Environmental Science & Technology, v. 45, no. 24, p. 10315-10321, https://doi.org/10.1021/es202568f.","productDescription":"7 p.","startPage":"10315","endPage":"10321","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":213752,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1021/es202568f"},{"id":241409,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"45","issue":"24","noUsgsAuthors":false,"publicationDate":"2011-11-17","publicationStatus":"PW","scienceBaseUri":"5059f02fe4b0c8380cd4a62f","contributors":{"authors":[{"text":"Nevers, Meredith B.","contributorId":91803,"corporation":false,"usgs":true,"family":"Nevers","given":"Meredith","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":436406,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Whitman, Richard L. rwhitman@usgs.gov","contributorId":542,"corporation":false,"usgs":true,"family":"Whitman","given":"Richard","email":"rwhitman@usgs.gov","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":436405,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70032674,"text":"70032674 - 2011 - NETPATH-WIN: an interactive user version of the mass-balance model, NETPATH","interactions":[],"lastModifiedDate":"2020-01-09T19:36:27","indexId":"70032674","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1861,"text":"Ground Water","active":true,"publicationSubtype":{"id":10}},"title":"NETPATH-WIN: an interactive user version of the mass-balance model, NETPATH","docAbstract":"NETPATH-WIN is an interactive user version of NETPATH, an inverse geochemical modeling code used to find mass-balance reaction models that are consistent with the observed chemical and isotopic composition of waters from aquatic systems. NETPATH-WIN was constructed to migrate NETPATH applications into the Microsoft WINDOWS® environment. The new version facilitates model utilization by eliminating difficulties in data preparation and results analysis of the DOS version of NETPATH, while preserving all of the capabilities of the original version. 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These regions correspond with the U.S. Geological Survey's National Water Quality Assessment (NAWQA) Program Major River Basin (MRB) study units 2, 3, 4, 5, and 7 (Preston and others, 2009). MRB2, covers the South Atlantic-Gulf and Tennessee River basins. MRB3, covers the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins. MRB4, covers the Missouri River basins. MRB5, covers the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins. MRB7, covers the Pacific Northwest River basins. The digital hydrologic network described here represents surface-water pathways (MRB_E2RF1) and associated catchments (MRB_E2RF1WS). It serves as the fundamental framework to spatially reference and summarize explanatory information supporting nutrient SPARROW models (Brakebill and others, 2011; Wieczorek and LaMotte, 2011). The principal geospatial dataset used to support this regional effort was based on an enhanced version of a 1:500,000 scale digital stream-reach network (ERF1_2) (Nolan et al., 2002). Enhancements included associating over 3,500 water-quality monitoring sites to the reach network, improving physical locations of stream reaches at or near monitoring locations, and generating drainage catchments based on 100m elevation data. A unique number (MRB_ID) identifies each reach as a single unit. This unique number is also shared by the catchment area drained by the reach, thus spatially linking the hydrologically connected streams and the respective drainage area characteristics. 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In response, citizen action led to the formation of a public- and private-sector partnership (the Tampa Bay Estuary Program), which adopted a number of management objectives to support the restoration and protection of the bay’s living resources. These included numeric chlorophyll a and water-clarity targets, as well as long-term goals addressing the spatial extent of seagrasses and other selected habitat types, to support estuarine-dependent faunal guilds.
Over the past three decades, nitrogen controls involving sources such as wastewater treatment plants, stormwater conveyance systems, fertilizer manufacturing and shipping operations, and power plants have been undertaken to meet these and other management objectives. Cumulatively, these controls have resulted in a 60% reduction in annual total nitrogen (TN) loads relative to earlier worse-case (latter 1970s) conditions. As a result, annual water-clarity and chlorophyll a targets are currently met in most years, and seagrass cover measured in 2008 was the highest recorded since 1950.
Factors that have contributed to the observed improvements in Tampa Bay over the past several decades include the following: (1) Development of numeric, science-based water-quality targets to meet a long-term goal of restoring seagrass acreage to 1950s levels. Empirical and mechanistic models found that annual average chlorophyll a concentrations were a primary manageable factor affecting light attenuation. The models also quantified relationships between TN loads, chlorophyll a concentrations, light attenuation, and fluctuations in seagrass cover. The availability of long-term monitoring data, and a systematic process for using the data to evaluate the effectiveness of management actions, has allowed managers to track progress and make adaptive changes when needed. (2) Citizen involvement, that is, the initial reductions in TN loads, which occurred in the late 1970s and early 1980s, was a result of state regulations that were developed in response to citizens’ call for action. Improved water clarity and better fishing and swimming conditions were identified as primary goals by citizens again in the early 1990s, and led to development of numeric water-quality targets and seagrass restoration goals. More recent citizen actions, from pet waste campaigns to support of reductions in residential fertilizer use, are important elements of the nitrogen management strategy. (3) Collaborative actions, that is, in addition to numerous other collaborative ventures that have benefitted Tampa Bay, the public/private Nitrogen Management Consortium, which includes more than 40 participating organizations, has implemented over 250 nutrient-reduction projects. These projects have addressed stormwater treatment, fertilizer manufacturing and shipping, agricultural practices, reclaimed water use, and atmospheric emissions from local power stations, providing more than 300 tons of TN load reductions since 1995. (4) State and federal regulatory programs, that is, regulatory requirements, such as state statutes and rules requiring compliance with advanced wastewater treatment standards by municipal sewerage works, have played a key role in Tampa Bay management efforts. The technical basis and implementation plan of the Tampa Bay nitrogen management strategy have been developed in cooperation with state and federal regulatory agencies, and the strategy has been recognized by them as an appropriate tool for meeting water-quality standards, including federally mandated total maximum daily loads.
Subsequent management efforts have focused on maintaining and extending those improvements in Tampa Bay’s environmental resources by addressing water and sediment quality and habitat protection and restoration. Implementation of a collaborative, watershed-based management process, driven by an integrated science approach, has played a central role in supporting progress toward the achievement of science-based estuary management goals.
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S.","contributorId":111948,"corporation":false,"usgs":true,"family":"McLusky","given":"Donald","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":570864,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Morrison, Gerold","contributorId":58150,"corporation":false,"usgs":true,"family":"Morrison","given":"Gerold","email":"","affiliations":[],"preferred":false,"id":570860,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Greening, Holly","contributorId":64299,"corporation":false,"usgs":true,"family":"Greening","given":"Holly","email":"","affiliations":[],"preferred":false,"id":570861,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yates, Kimberly K. 0000-0001-8764-0358 kyates@usgs.gov","orcid":"https://orcid.org/0000-0001-8764-0358","contributorId":420,"corporation":false,"usgs":true,"family":"Yates","given":"Kimberly","email":"kyates@usgs.gov","middleInitial":"K.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":570862,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192767,"text":"70192767 - 2011 - Coal resources for part of the Wilcox group (Paleocene through Eocene), central Texas","interactions":[],"lastModifiedDate":"2020-10-22T16:46:06.73634","indexId":"70192767","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"seriesTitle":{"id":5382,"text":"AAPG Studies in Geology","active":false,"publicationSubtype":{"id":24}},"chapter":"10","title":"Coal resources for part of the Wilcox group (Paleocene through Eocene), central Texas","docAbstract":"The Wilcox Group of central Texas contains shallow (less than 500 ft) coal deposits that are mined for use in mine-mouth electric power generating plants. These coal deposits range in apparent rank from lignite to sub-bituminous (Pierce et al., 2011) and are similar in rank and composition to shallow coal deposits in the northeast and south Texas areas (Figure 1). The coal zones and associated strata in the central Texas assessment area generally dip to the southeast toward the Gulf of Mexico coastline and basin center. The central Texas resource assessment area includes parts of eight counties (Figure 2). The assessment area was selected to encompass current mining areas and areas with available subsurface stratigraphic data. The assessment area is roughly 160 miles long and 5 to 25 miles wide and generally follows the outcrop of the Paleocene to Eocene Wilcox Group in central Texas (Figures 1, 2). Approximately 1800 subsurface stratigraphic records from rotary and core drill holes were used to assess the resources of the central Texas assessment area. Of the 1800 drill holes, only 167 are public data points and are primarily located in the areas that have been permitted for surface mining (Figure 2; Appendix 1). The remaining 1632 drill holes, which are distributed throughout the assessment area, were provided to the U.S. Geological Survey (USGS) on a confidential basis by various coal companies for use in regional studies.
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\"}}]}","volume":"62","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a743587e4b0a9a2e9e25cbe","contributors":{"editors":[{"text":"Warwick, Peter D. 0000-0002-3152-7783 pwarwick@usgs.gov","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":762,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter","email":"pwarwick@usgs.gov","middleInitial":"D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":726433,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Karlsen, Alexander K.","contributorId":44089,"corporation":false,"usgs":false,"family":"Karlsen","given":"Alexander K.","affiliations":[],"preferred":false,"id":726434,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Merrill, Matthew D. 0000-0003-3766-847X 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pwarwick@usgs.gov","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":762,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter","email":"pwarwick@usgs.gov","middleInitial":"D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":716864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aubourg, Claire E.","contributorId":87192,"corporation":false,"usgs":true,"family":"Aubourg","given":"Claire","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":716865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Suitt, Stephen E. ssuitt@usgs.gov","contributorId":3952,"corporation":false,"usgs":true,"family":"Suitt","given":"Stephen","email":"ssuitt@usgs.gov","middleInitial":"E.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":716866,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Podwysocki, Steven M.","contributorId":90352,"corporation":false,"usgs":true,"family":"Podwysocki","given":"Steven","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":716867,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schultz, Adam C.","contributorId":82752,"corporation":false,"usgs":true,"family":"Schultz","given":"Adam","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":716868,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70194902,"text":"70194902 - 2011 - Waste isolation and contaminant migration - Tools and techniques for monitoring the saturated zone-unsaturated zone-plant-atmosphere continuum","interactions":[],"lastModifiedDate":"2018-01-27T11:31:43","indexId":"70194902","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"seriesNumber":"NUREG/CP-0195","chapter":"3.5.1","title":"Waste isolation and contaminant migration - Tools and techniques for monitoring the saturated zone-unsaturated zone-plant-atmosphere continuum","docAbstract":"The terrestrial carbon sink has been large in recent decades, but its size and location remain uncertain. Using forest inventory data and long-term ecosystem carbon studies, we estimate a total forest sink of 2.4 ± 0.4 petagrams of carbon per year (Pg C year–1) globally for 1990 to 2007. We also estimate a source of 1.3 ± 0.7 Pg C year–1 from tropical land-use change, consisting of a gross tropical deforestation emission of 2.9 ± 0.5 Pg C year–1 partially compensated by a carbon sink in tropical forest regrowth of 1.6 ± 0.5 Pg C year–1. Together, the fluxes comprise a net global forest sink of 1.1 ± 0.8 Pg C year–1, with tropical estimates having the largest uncertainties. Our total forest sink estimate is equivalent in magnitude to the terrestrial sink deduced from fossil fuel emissions and land-use change sources minus ocean and atmospheric sinks.
The ability to predict amphibian breeding across landscapes is important for informing land management decisions and helping biologists better understand and remediate factors contributing to declines in amphibian populations. We built geospatial models of likely breeding habitats for each of four amphibian species that breed in Yellowstone National Park (YNP). We used field data collected in 2000–2002 from 497 sites among 16 basins and predictor variables from geospatial models produced from remotely sensed data (e.g., digital elevation model, complex topographic index, landform data, wetland probability, and vegetative cover). Except for 31 sites in one basin that were surveyed in both 2000 and 2002, all sites were surveyed once. We used polytomous regression to build statistical models for each species of amphibian from (1) field survey site data only, (2) field data combined with data from geospatial models, and (3) data from geospatial models only. Based on measures of receiver operating characteristic (ROC) scores, models of the second type best explained likely breeding habitat because they contained the most information (ROC values ranged from 0.70 to 0.88). However, models of the third type could be applied to the entire YNP landscape and produced maps that could be verified with reserve field data. Accuracy rates for models built for single years were highly variable, ranging from 0.30 to 0.78. Accuracy rates for models built with data combined from multiple years were higher and less variable, ranging from 0.60 to 0.80. Combining results from the geospatial multiyear models yielded maps of “core” breeding areas (areas with high probability values for all three years) surrounded by areas that scored high for only one or two years, providing an estimate of variability among years. Such information can highlight landscape options for amphibian conservation. For example, our models identify alternative areas that could be protected for each species, including 6828–10 764 ha for tiger salamanders, 971–3017 ha for western toads, 4732–16 696 ha for boreal chorus frogs, and 4940–19 690 ha for Columbia spotted frogs.
","language":"English","publisher":"Ecological Society of America","doi":"10.1890/10-1261.1","issn":"10510761","usgsCitation":"Bartelt, P.E., Gallant, A.L., Klaver, R.W., Wright, C., Patla, D.A., and Peterson, C.R., 2011, Predicting breeding habitat for amphibians: A spatiotemporal analysis across Yellowstone National Park: Ecological Applications, v. 21, no. 7, p. 2530-2547, https://doi.org/10.1890/10-1261.1.","productDescription":"18 p.","startPage":"2530","endPage":"2547","costCenters":[],"links":[{"id":475420,"rank":10000,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1890/10-1261.1","text":"Publisher Index Page"},{"id":244495,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":216614,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1890/10-1261.1"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.0772705078125,\n 44.18614312298759\n ],\n [\n -109.8907470703125,\n 44.18614312298759\n ],\n [\n -109.8907470703125,\n 45.092913646051144\n ],\n [\n -111.0772705078125,\n 45.092913646051144\n ],\n [\n -111.0772705078125,\n 44.18614312298759\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","issue":"7","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a81aae4b0c8380cd7b670","contributors":{"authors":[{"text":"Bartelt, Paul E.","contributorId":18895,"corporation":false,"usgs":true,"family":"Bartelt","given":"Paul","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":445202,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gallant, Alisa L. 0000-0002-3029-6637 gallant@usgs.gov","orcid":"https://orcid.org/0000-0002-3029-6637","contributorId":2940,"corporation":false,"usgs":true,"family":"Gallant","given":"Alisa","email":"gallant@usgs.gov","middleInitial":"L.","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":445200,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":445204,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wright, C.K.","contributorId":25780,"corporation":false,"usgs":true,"family":"Wright","given":"C.K.","affiliations":[],"preferred":false,"id":445201,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Patla, Debra A.","contributorId":40059,"corporation":false,"usgs":true,"family":"Patla","given":"Debra","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":445203,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Peterson, Charles R.","contributorId":95738,"corporation":false,"usgs":true,"family":"Peterson","given":"Charles","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":445199,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70003447,"text":"70003447 - 2011 - Estimation of demographic parameters in a tiger population from long-term camera trap data","interactions":[],"lastModifiedDate":"2017-01-31T12:49:05","indexId":"70003447","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Estimation of demographic parameters in a tiger population from long-term camera trap data","docAbstract":"Chapter 7 (Karanth et al.) illustrated the use of camera trapping in combination with closed population capture–recapture (CR) models to estimate densities of tigers Panthera tigris. Such estimates can be very useful for investigating variation across space for a particular species (e.g., Karanth et al. 2004) or variation among species at a specific location. In addition, estimates of density continued at the same site(s) over multiple years are very useful for understanding and managing populations of large carnivores. Such multi-year studies can yield estimates of rates of change in abundance. Additionally, because the fates of marked individuals are tracked through time, biologists can delve deeper into factors driving changes in abundance such as rates of survival, recruitment and movement (Williams et al. 2002). Fortunately, modern CR approaches permit the modeling of populations that change between sampling occasions as a result of births, deaths, immigration and emigration (Pollock et al. 1990; Nichols 1992). Some of these early “open population” models focused on estimation of survival rates and, to a lesser extent, abundance, but more recent models permit estimation of recruitment and movement rates as well.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Camera traps in animal ecology","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-4-431-99495-4_9","usgsCitation":"Karanth, K.U., and Nichols, J., 2011, Estimation of demographic parameters in a tiger population from long-term camera trap data, chap. of Camera traps in animal ecology, p. 145-161, https://doi.org/10.1007/978-4-431-99495-4_9.","productDescription":"17 p.","startPage":"145","endPage":"161","numberOfPages":"17","ipdsId":"IP-020298","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":334463,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5891b0aae4b072a7ac1298f9","contributors":{"editors":[{"text":"O’Connell, Allan F. 0000-0001-7032-7023 aoconnell@usgs.gov","orcid":"https://orcid.org/0000-0001-7032-7023","contributorId":471,"corporation":false,"usgs":true,"family":"O’Connell","given":"Allan","email":"aoconnell@usgs.gov","middleInitial":"F.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":661970,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Nichols, James D. jnichols@usgs.gov","contributorId":139087,"corporation":false,"usgs":true,"family":"Nichols","given":"James D.","email":"jnichols@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":661971,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Karanth, K. Ullas","contributorId":6984,"corporation":false,"usgs":true,"family":"Karanth","given":"K.","email":"","middleInitial":"Ullas","affiliations":[],"preferred":false,"id":661972,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Karanth, K. Ullas","contributorId":6984,"corporation":false,"usgs":true,"family":"Karanth","given":"K.","email":"","middleInitial":"Ullas","affiliations":[],"preferred":false,"id":661968,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nichols, James D. jnichols@usgs.gov","contributorId":139087,"corporation":false,"usgs":true,"family":"Nichols","given":"James D.","email":"jnichols@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":661969,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70032226,"text":"70032226 - 2011 - Integration of paleoseismic data from multiple sites to develop an objective earthquake chronology: Application to the Weber segment of the Wasatch fault zone, Utah","interactions":[],"lastModifiedDate":"2012-03-12T17:21:24","indexId":"70032226","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Integration of paleoseismic data from multiple sites to develop an objective earthquake chronology: Application to the Weber segment of the Wasatch fault zone, Utah","docAbstract":"We present a method to evaluate and integrate paleoseismic data from multiple sites into a single, objective measure of earthquake timing and recurrence on discrete segments of active faults. We apply this method to the Weber segment (WS) of the Wasatch fault zone using data from four fault-trench studies completed between 1981 and 2009. After systematically reevaluating the stratigraphic and chronologic data from each trench site, we constructed time-stratigraphic OxCal models that yield site probability density functions (PDFs) of the times of individual earthquakes. We next qualitatively correlated the site PDFs into a segment-wide earthquake chronology, which is supported by overlapping site PDFs, large per-event displacements, and prominent segment boundaries. For each segment-wide earthquake, we computed the product of the site PDF probabilities in common time bins, which emphasizes the overlap in the site earthquake times, and gives more weight to the narrowest, best-defined PDFs. The product method yields smaller earthquake-timing uncertainties compared to taking the mean of the site PDFs, but is best suited to earthquakes constrained by broad, overlapping site PDFs. We calculated segment-wide earthquake recurrence intervals and uncertainties using a Monte Carlo model. Five surface-faulting earthquakes occurred on the WS at about 5.9, 4.5, 3.1, 1.1, and 0.6 ka. With the exception of the 1.1-ka event, we used the product method to define the earthquake times. The revised WS chronology yields a mean recurrence interval of 1.3 kyr (0.7-1.9-kyr estimated two-sigma [2??] range based on interevent recurrence). These data help clarify the paleoearthquake history of the WS, including the important question of the timing and rupture extent of the most recent earthquake, and are essential to the improvement of earthquake-probability assessments for the Wasatch Front region.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Bulletin of the Seismological Society of America","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","doi":"10.1785/0120110102","issn":"00371106","usgsCitation":"DuRoss, C., Personius, S., Crone, A.J., Olig, S., and Lund, W., 2011, Integration of paleoseismic data from multiple sites to develop an objective earthquake chronology: Application to the Weber segment of the Wasatch fault zone, Utah: Bulletin of the Seismological Society of America, v. 101, no. 6, p. 2765-2781, https://doi.org/10.1785/0120110102.","startPage":"2765","endPage":"2781","numberOfPages":"17","costCenters":[],"links":[{"id":214826,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1785/0120110102"},{"id":242578,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"101","issue":"6","noUsgsAuthors":false,"publicationDate":"2011-12-08","publicationStatus":"PW","scienceBaseUri":"505a3c8de4b0c8380cd62e2e","contributors":{"authors":[{"text":"DuRoss, C. B.","contributorId":86003,"corporation":false,"usgs":true,"family":"DuRoss","given":"C. B.","affiliations":[],"preferred":false,"id":435134,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Personius, S. F. 0000-0001-8347-7370","orcid":"https://orcid.org/0000-0001-8347-7370","contributorId":31408,"corporation":false,"usgs":true,"family":"Personius","given":"S. F.","affiliations":[],"preferred":false,"id":435130,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crone, A. J.","contributorId":84363,"corporation":false,"usgs":true,"family":"Crone","given":"A.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":435133,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olig, S.S.","contributorId":68905,"corporation":false,"usgs":true,"family":"Olig","given":"S.S.","email":"","affiliations":[],"preferred":false,"id":435132,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lund, W.R.","contributorId":58781,"corporation":false,"usgs":true,"family":"Lund","given":"W.R.","email":"","affiliations":[],"preferred":false,"id":435131,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70036729,"text":"70036729 - 2011 - A simple graphical approach to quantitative monitoring of rangelands","interactions":[],"lastModifiedDate":"2013-02-26T18:47:55","indexId":"70036729","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3230,"text":"Rangelands","active":true,"publicationSubtype":{"id":10}},"title":"A simple graphical approach to quantitative monitoring of rangelands","docAbstract":"The article reviews graphical interpretation of the four monitoring methods that can be used to generate a variety of indicators of rangeland ecosystem function. Data for all four of the monitoring methods can be recorded on a single data sheet that is designed to be usable by somebody with minimal literacy. Indicators of plant and ground cover are central to most long-term monitoring systems. Plant and ground-cover data inform managers about forage availability, plant community composition and structure, and risk of runoff and erosion. The spatial arrangement of plants at a site in addition to the percent of the ground that is covered by plants is an important determinant of erosion potential. Vertical vegetation structure can be monitored by capturing data on maximum plant height at each stick location. Plant density method can provide an early indicator of future changes in plant cover, forage, quality, and habitat structure.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Rangelands","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Society for Range Management","publisherLocation":"Lawrence, KS","doi":"10.2111/1551-501X-33.4.6","issn":"01900528","usgsCitation":"Riginos, C., Herrick, J.E., Sundaresan, S., Farley, C., and Belnap, J., 2011, A simple graphical approach to quantitative monitoring of rangelands: Rangelands, v. 33, no. 4, p. 6-13, https://doi.org/10.2111/1551-501X-33.4.6.","productDescription":"8 p.","startPage":"6","endPage":"13","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":475300,"rank":10000,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10150/639818","text":"External Repository"},{"id":245458,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":217507,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.2111/1551-501X-33.4.6"}],"volume":"33","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059e58ee4b0c8380cd46e0c","contributors":{"authors":[{"text":"Riginos, C.","contributorId":54437,"corporation":false,"usgs":true,"family":"Riginos","given":"C.","email":"","affiliations":[],"preferred":false,"id":457550,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herrick, J. E.","contributorId":84709,"corporation":false,"usgs":true,"family":"Herrick","given":"J.","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":457552,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sundaresan, S.R.","contributorId":95307,"corporation":false,"usgs":true,"family":"Sundaresan","given":"S.R.","affiliations":[],"preferred":false,"id":457553,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Farley, C.","contributorId":72622,"corporation":false,"usgs":true,"family":"Farley","given":"C.","email":"","affiliations":[],"preferred":false,"id":457551,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Belnap, J. 0000-0001-7471-2279","orcid":"https://orcid.org/0000-0001-7471-2279","contributorId":23872,"corporation":false,"usgs":true,"family":"Belnap","given":"J.","affiliations":[],"preferred":false,"id":457549,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70034132,"text":"70034132 - 2011 - Changes in agricultural cropland areas between a water-surplus year and a water-deficit year impacting food security, determined using MODIS 250 m time-series data and spectral matching techniques, in the Krishna river basin (India)","interactions":[],"lastModifiedDate":"2018-02-22T16:16:51","indexId":"70034132","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2068,"text":"International Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Changes in agricultural cropland areas between a water-surplus year and a water-deficit year impacting food security, determined using MODIS 250 m time-series data and spectral matching techniques, in the Krishna river basin (India)","docAbstract":"The objective of this study was to investigate the changes in cropland areas as a result of water availability using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series data and spectral matching techniques (SMTs). The study was conducted in the Krishna River basin in India, a very large river basin with an area of 265 752 km2 (26 575 200 ha), comparing a water-surplus year (2000–2001) and a water-deficit year (2002–2003). The MODIS 250 m time-series data and SMTs were found ideal for agricultural cropland change detection over large areas and provided fuzzy classification accuracies of 61–100% for various land‐use classes and 61–81% for the rain-fed and irrigated classes. The most mixing change occurred between rain-fed cropland areas and informally irrigated (e.g. groundwater and small reservoir) areas. Hence separation of these two classes was the most difficult. The MODIS 250 m-derived irrigated cropland areas for the districts were highly correlated with the Indian Bureau of Statistics data, with R 2-values between 0.82 and 0.86.
The change in the net area irrigated was modest, with an irrigated area of 8 669 881 ha during the water-surplus year, as compared with 7 718 900 ha during the water-deficit year. However, this is quite misleading as most of the major changes occurred in cropping intensity, such as changing from higher intensity to lower intensity (e.g. from double crop to single crop). The changes in cropping intensity of the agricultural cropland areas that took place in the water-deficit year (2002–2003) when compared with the water-surplus year (2000–2001) in the Krishna basin were: (a) 1 078 564 ha changed from double crop to single crop, (b) 1 461 177 ha changed from continuous crop to single crop, (c) 704 172 ha changed from irrigated single crop to fallow and (d) 1 314 522 ha changed from minor irrigation (e.g. tanks, small reservoirs) to rain-fed. These are highly significant changes that will have strong impact on food security. Such changes may be expected all over the world in a changing climate.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431161003749485","issn":"01431161","usgsCitation":"Gumma, M., Thenkabail, P.S., Muralikrishna, I., Velpuri, N.M., Gangadhararao, P., Dheeravath, V., Biradar, C., Nalan, S., and Gaur, A., 2011, Changes in agricultural cropland areas between a water-surplus year and a water-deficit year impacting food security, determined using MODIS 250 m time-series data and spectral matching techniques, in the Krishna river basin (India): International Journal of Remote Sensing, v. 32, no. 12, p. 3495-3520, https://doi.org/10.1080/01431161003749485.","productDescription":"26 p.","startPage":"3495","endPage":"3520","numberOfPages":"26","costCenters":[],"links":[{"id":216904,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1080/01431161003749485"},{"id":244805,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"12","noUsgsAuthors":false,"publicationDate":"2011-06-28","publicationStatus":"PW","scienceBaseUri":"5059f409e4b0c8380cd4bad7","contributors":{"authors":[{"text":"Gumma, Murali Krishna","contributorId":50426,"corporation":false,"usgs":true,"family":"Gumma","given":"Murali Krishna","affiliations":[],"preferred":false,"id":444246,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":444252,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muralikrishna, I.V.","contributorId":31234,"corporation":false,"usgs":true,"family":"Muralikrishna","given":"I.V.","email":"","affiliations":[],"preferred":false,"id":444248,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Velpuri, Naga Manohar 0000-0002-6370-1926 nvelpuri@usgs.gov","orcid":"https://orcid.org/0000-0002-6370-1926","contributorId":4441,"corporation":false,"usgs":true,"family":"Velpuri","given":"Naga","email":"nvelpuri@usgs.gov","middleInitial":"Manohar","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":444251,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gangadhararao, P.T.","contributorId":19406,"corporation":false,"usgs":true,"family":"Gangadhararao","given":"P.T.","email":"","affiliations":[],"preferred":false,"id":444247,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dheeravath, V.","contributorId":55234,"corporation":false,"usgs":true,"family":"Dheeravath","given":"V.","affiliations":[],"preferred":false,"id":444250,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Biradar, C.M.","contributorId":35563,"corporation":false,"usgs":true,"family":"Biradar","given":"C.M.","email":"","affiliations":[],"preferred":false,"id":444249,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nalan, S.A.","contributorId":7110,"corporation":false,"usgs":true,"family":"Nalan","given":"S.A.","email":"","affiliations":[],"preferred":false,"id":444245,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gaur, A.","contributorId":74603,"corporation":false,"usgs":true,"family":"Gaur","given":"A.","email":"","affiliations":[],"preferred":false,"id":444253,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ]}