{"pageNumber":"842","pageRowStart":"21025","pageSize":"25","recordCount":40783,"records":[{"id":97765,"text":"ds69U - 2009 - Total Petroleum Systems and Geologic Assessment of Oil and Gas Resources in the Powder River Basin Province, Wyoming and Montana","interactions":[],"lastModifiedDate":"2017-08-29T18:45:56","indexId":"ds69U","displayToPublicDate":"2009-08-18T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"69","chapter":"U","title":"Total Petroleum Systems and Geologic Assessment of Oil and Gas Resources in the Powder River Basin Province, Wyoming and Montana","docAbstract":"The U.S. Geological Survey completed an assessment of the undiscovered oil and gas potential of the Powder River Basin in 2006. The assessment of undiscovered oil and gas used the total petroleum system concept, which includes mapping the distribution of potential source rocks and known petroleum accumulations and determining the timing of petroleum generation and migration. Geologically based, it focuses on source and reservoir rock stratigraphy, timing of tectonic events and the configuration of resulting structures, formation of traps and seals, and burial history modeling. The total petroleum system is subdivided into assessment units based on similar geologic characteristics and accumulation and petroleum type. In chapter 1 of this report, five total petroleum systems, eight conventional assessment units, and three continuous assessment units were defined and the undiscovered oil and gas resources within each assessment unit quantitatively estimated. \r\n\r\nChapter 2 describes data used in support of the process being applied by the U.S. Geological Survey (USGS) National Oil and Gas Assessment (NOGA) project. Digital tabular data used in this report and archival data that permit the user to perform further analyses are available elsewhere on this CD-ROM. Computers and software may import the data without transcription from the Portable Document Format files (.pdf files) of the text by the reader. Because of the number and variety of platforms and software available, graphical images are provided as .pdf files and tabular data are provided in a raw form as tab-delimited text files (.tab files).","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ds69U","usgsCitation":"2009, Total Petroleum Systems and Geologic Assessment of Oil and Gas Resources in the Powder River Basin Province, Wyoming and Montana: U.S. Geological Survey Data Series 69, Available online and on CD-ROM, https://doi.org/10.3133/ds69U.","productDescription":"Available online and on CD-ROM","additionalOnlineFiles":"Y","costCenters":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"links":[{"id":118580,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_69_u.jpg"},{"id":12932,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/dds/dds-069/dds-069-u/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -109,42 ], [ -109,47 ], [ -103,47 ], [ -103,42 ], [ -109,42 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e49fce4b07f02db5f547b","contributors":{"compilers":[{"text":"Anna, L. O.","contributorId":65472,"corporation":false,"usgs":true,"family":"Anna","given":"L.","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":708984,"contributorType":{"id":3,"text":"Compilers"},"rank":1}]}}
,{"id":97759,"text":"ofr20091095 - 2009 - Finding Trapped Miners by Using a Prototype Seismic Recording System Made from Music-Recording Hardware","interactions":[],"lastModifiedDate":"2012-02-02T00:15:07","indexId":"ofr20091095","displayToPublicDate":"2009-08-18T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2009-1095","title":"Finding Trapped Miners by Using a Prototype Seismic Recording System Made from Music-Recording Hardware","docAbstract":"The goal of this project was to use off-the-shelf music recording equipment to build and test a prototype seismic system to listen for people trapped in underground chambers (mines, caves, collapsed buildings). Previous workers found that an array of geophones is effective in locating trapped miners; displaying the data graphically, as well as playing it back into an audio device (headphones) at high speeds, was found to be effective for locating underground tapping. The desired system should record the data digitally to allow for further analysis, be capable of displaying the data graphically, allow for rudimentary analysis (bandpass filter, deconvolution), and allow the user to listen to the data at varying speeds. \r\n\r\nAlthough existing seismic reflection systems are adequate to record, display and analyze the data, they are relatively expensive and difficult to use and do not have an audio playback option. This makes it difficult for individual mines to have a system waiting on the shelf for an emergency. In contrast, music recording systems, like the one I used to construct the prototype system, can be purchased for about 20 percent of the cost of a seismic reflection system and are designed to be much easier to use. The prototype system makes use of an ~$3,000, 16-channel music recording system made by Presonus, Inc., of Baton Rouge, Louisiana. Other manufacturers make competitive systems that would serve equally well. Connecting the geophones to the recording system required the only custom part of this system - a connector that takes the output from the geophone cable and breaks it into 16 microphone inputs to be connected to the music recording system. The connector took about 1 day of technician time to build, using about $300 in off-the-shelf parts. \r\n\r\nComparisons of the music recording system and a standard seismic reflection system (A 24-channel 'Geode' system manufactured by Geometrics, Inc., of San Jose, California) were carried out at two locations. Initial recordings of small hammer taps were carried out in a small field in Seattle, Washington; more elaborate tests were carried out at the San Juan Coal Mine in San Juan, New Mexico, in which miners underground were signaling. The comparisons demonstrate that the recordings made by the two systems are nearly identical, indicating that either system adequately records the data from the geophones. In either system the data can quickly be converted to a format (Society of Exploration Geophysicists 'Y' format; 'SEGY') to allow for filtering and other signal processing. With a modest software development effort, it is clear that either system could produce equivalent data products (SEGY data and audio data) within a few minutes of finishing the recording. \r\n\r\nThe two systems both have significant advantages and drawbacks. With the seismograph, the tapping was distinctly visible when it occurred during a time window that was displayed. I have not identified or developed software for converting the resulting data to sound recordings that can be heard, but this limitation could be overcome with a trivial software development effort. The main drawbacks to the seismograph are that it does not allow for real-time listening, it is expensive to purchase, and it contains many features that are not utilized for this application. The music recording system is simple to use (it is designed for a general user, rather than a trained technician), allows for listening during recording, and has the advantage of using inexpensive, off-the-shelf components. It also allows for quick (within minutes) playback of the audio data at varying speeds. The data display by the software in the prototype system, however, is clearly inferior to the display on the seismograph. The music system also has the drawback of substantially oversampling the data by a factor of 24 (48,000 samples per second versus 2,000 samples per second) because the user interface only allows limited subsampling. This latte","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20091095","usgsCitation":"Pratt, T.L., 2009, Finding Trapped Miners by Using a Prototype Seismic Recording System Made from Music-Recording Hardware: U.S. Geological Survey Open-File Report 2009-1095, Report: iii, 35 p.; Sound Files, https://doi.org/10.3133/ofr20091095.","productDescription":"Report: iii, 35 p.; Sound Files","additionalOnlineFiles":"Y","costCenters":[{"id":648,"text":"Western Earthquake Hazards","active":false,"usgs":true}],"links":[{"id":126859,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2009_1095.jpg"},{"id":12926,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2009/1095/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e49fbe4b07f02db5f46f5","contributors":{"authors":[{"text":"Pratt, Thomas L. 0000-0003-3131-3141 tpratt@usgs.gov","orcid":"https://orcid.org/0000-0003-3131-3141","contributorId":3279,"corporation":false,"usgs":true,"family":"Pratt","given":"Thomas","email":"tpratt@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":303063,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":97760,"text":"ofr20091157 - 2009 - Geophysical Studies in the Vicinity of the Warner Mountains and Surprise Valley, Northeast California, Northwest Nevada, and Southern Oregon","interactions":[],"lastModifiedDate":"2012-02-10T00:11:54","indexId":"ofr20091157","displayToPublicDate":"2009-08-18T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2009-1157","title":"Geophysical Studies in the Vicinity of the Warner Mountains and Surprise Valley, Northeast California, Northwest Nevada, and Southern Oregon","docAbstract":"From May 2006 to August 2007, the U.S. Geological Survey (USGS) collected 793 gravity stations, about 102 line-kilometers of truck-towed and ground magnetometer data, and about 325 physical-property measurements in northeastern California, northwestern Nevada, and southern Oregon. Gravity, magnetic, and physical-property data were collected to study regional crustal structures and geology as an aid to understanding the geologic framework of the Surprise Valley geothermal area and, in general, geothermal systems throughout the Great Basin. \r\n\r\nThe Warner Mountains and Surprise Valley mark the transition from the extended Basin and Range province to the unextended Modoc Plateau. This transition zone, in the northwestern corner of the Basin and Range, is relatively diffuse compared to other, more distinct boundaries, such as the Wasatch front in Utah and the eastern Sierran range front. In addition, this transition zone is the site of a geothermal system with potential for development, and previous studies have revealed a complex structural setting consisting of several obliquely oriented fault sets. As a result, this region has been the subject of several recent geological and geophysical investigations. The gravity and magnetic data presented here support and supplement those studies, and although the study area is composed predominantly of Tertiary volcanic rocks of the Modoc Plateau rocks, the physical properties of these and others rocks create a distinguishable pattern of gravity and magnetic anomalies that can be used to infer subsurface geologic structure.","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20091157","usgsCitation":"Ponce, D.A., Glen, J., Egger, A.E., Bouligand, C., Watt, J.T., and Morin, R.L., 2009, Geophysical Studies in the Vicinity of the Warner Mountains and Surprise Valley, Northeast California, Northwest Nevada, and Southern Oregon: U.S. Geological Survey Open-File Report 2009-1157, Report: vi, 19 p.; Data Tables, https://doi.org/10.3133/ofr20091157.","productDescription":"Report: vi, 19 p.; Data Tables","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2006-05-01","temporalEnd":"2007-08-31","costCenters":[{"id":671,"text":"Western Region Geology and Geophysics Science Center","active":false,"usgs":true}],"links":[{"id":125476,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2009_1157.jpg"},{"id":12927,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2009/1157/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -121,41 ], [ -121,42.5 ], [ -119,42.5 ], [ -119,41 ], [ -121,41 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4ac9e4b07f02db67c409","contributors":{"authors":[{"text":"Ponce, David A. 0000-0003-4785-7354 ponce@usgs.gov","orcid":"https://orcid.org/0000-0003-4785-7354","contributorId":1049,"corporation":false,"usgs":true,"family":"Ponce","given":"David","email":"ponce@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":303064,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glen, Jonathan M. G.","contributorId":45756,"corporation":false,"usgs":true,"family":"Glen","given":"Jonathan M. G.","affiliations":[],"preferred":false,"id":303066,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Egger, Anne E.","contributorId":48669,"corporation":false,"usgs":true,"family":"Egger","given":"Anne","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":303067,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bouligand, Claire","contributorId":71662,"corporation":false,"usgs":true,"family":"Bouligand","given":"Claire","affiliations":[],"preferred":false,"id":303068,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Watt, Janet T. 0000-0002-4759-3814","orcid":"https://orcid.org/0000-0002-4759-3814","contributorId":8564,"corporation":false,"usgs":true,"family":"Watt","given":"Janet","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":303065,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Morin, Robert L.","contributorId":82671,"corporation":false,"usgs":true,"family":"Morin","given":"Robert","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":303069,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":97763,"text":"ofr20091159 - 2009 - Land-Cover Change in the Central Irregular Plains, 1973-2000","interactions":[],"lastModifiedDate":"2012-02-10T00:11:48","indexId":"ofr20091159","displayToPublicDate":"2009-08-18T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2009-1159","title":"Land-Cover Change in the Central Irregular Plains, 1973-2000","docAbstract":"Spearheaded by the Geographic Analysis and Monitoring Program of the U.S. Geological Survey (USGS) in collaboration with the U.S. Environmental Protection Agency (EPA) and the National Aeronautics and Space Administration (NASA), the Land Cover Trends is a research project focused on understanding the rates, trends, causes, and consequences of contemporary United States land-use and land-cover change. Using the EPA Level III ecoregions as the geographic framework, scientists process geospatial data collected between 1973 and 2000 to characterize ecosystem responses to land-use changes. The 27-year study period was divided into five temporal periods: 1973-1980, 1980-1986, 1986-1992, 1992-2000 and 1973-2000. General land-cover classes for these periods were interpreted from Landsat Multispectral Scanner, Thematic Mapper, and Enhanced Thematic Mapper Plus imagery to categorize land-cover change and evaluate using a modified Anderson Land Use Land Cover Classification System for image interpretation.\r\n\r\nThe rates of land-cover change are estimated using a stratified, random sampling of 10-kilometer (km) by 10-km blocks allocated within each ecoregion. For each sample block, satellite images are used to interpret land-cover change. Additionally, historical aerial photographs from similar timeframes and other ancillary data such as census statistics and published literature are used. The sample block data are then incorporated into statistical analyses to generate an overall change matrix for the ecoregion. These change statistics are applicable for different levels of scale, including total change for the individual sample blocks and change estimates for the entire ecoregion. The results illustrate that there is no single profile of land-cover change but instead point to geographic variability that results from land uses within ecoregions continuously adapting to various factors including environmental, technological, and socioeconomic.","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20091159","usgsCitation":"Karstensen, K.A., 2009, Land-Cover Change in the Central Irregular Plains, 1973-2000: U.S. Geological Survey Open-File Report 2009-1159, iv, 8 p., https://doi.org/10.3133/ofr20091159.","productDescription":"iv, 8 p.","temporalStart":"1973-01-01","temporalEnd":"2000-12-31","costCenters":[{"id":383,"text":"Mid-Continent Geographic Science Center","active":true,"usgs":true}],"links":[{"id":125477,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2009_1159.jpg"},{"id":12930,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2009/1159/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -98,35 ], [ -98,42 ], [ -90.5,42 ], [ -90.5,35 ], [ -98,35 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b23e4b07f02db6adf83","contributors":{"authors":[{"text":"Karstensen, Krista A. kkarstensen@usgs.gov","contributorId":286,"corporation":false,"usgs":true,"family":"Karstensen","given":"Krista","email":"kkarstensen@usgs.gov","middleInitial":"A.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":303076,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70038090,"text":"70038090 - 2009 - Laboratory evaluation of an OTT acoustic digital current meter and a SonTek Laboratory acoustic Doppler velocimeter","interactions":[],"lastModifiedDate":"2015-08-26T13:14:14","indexId":"70038090","displayToPublicDate":"2009-08-14T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Laboratory evaluation of an OTT acoustic digital current meter and a SonTek Laboratory acoustic Doppler velocimeter","docAbstract":"<p>Recently, an acoustic current meter known as the OTT * acoustic digital current meter (ADC) was introduced as an alternative instrument for stream gaging measurements. The Bureau of Reclamation and the U.S. Geological Survey collaborated on a side- by-side evaluation of the ADC and a SonTek/YSI acoustic Doppler velocimeter (ADV). Measurements were carried out in a laboratory flume to evaluate the performance characteristics of the ADC under a range of flow and boundary conditions. The flume contained a physical model of a mountain river with a diversion dam and variety of bed materials ranging from smooth mortar to a cobble bed. The instruments were installed on a trolley system that allowed them to be easily moved within the flume while maintaining a consistent probe orientation. More than 50 comparison measurements were made in an effort to verify the manufacturer&rsquo;s performance specifications and to evaluate potential boundary disturbance for near-bed and vertical boundary measurements. Data and results from this evaluation are presented and discussed.&nbsp;</p>","conferenceTitle":"33rd  International Association of Hydraulic Engineering and Research Congress","conferenceDate":"August 9-14, 2009","conferenceLocation":"Vancouver, BC","language":"English","usgsCitation":"Vermeyen, T., Oberg, K.A., and Jackson, P.R., 2009, Laboratory evaluation of an OTT acoustic digital current meter and a SonTek Laboratory acoustic Doppler velocimeter, 33rd  International Association of Hydraulic Engineering and Research Congress, Vancouver, BC, August 9-14, 2009, p. 1-8.","productDescription":"8 p.","startPage":"1","endPage":"8","numberOfPages":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-013290","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":307542,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":307541,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.usbr.gov/tsc/hydlab/pubs/PAP/PAP-0990.pdf","size":"160kb","linkFileType":{"id":1,"text":"pdf"}}],"country":"UNITED STATES","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57fe8425e4b0824b2d148eb3","contributors":{"authors":[{"text":"Vermeyen, T.B.","contributorId":112473,"corporation":false,"usgs":false,"family":"Vermeyen","given":"T.B.","email":"","affiliations":[{"id":6736,"text":"Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":570129,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oberg, Kevin A. kaoberg@usgs.gov","contributorId":928,"corporation":false,"usgs":true,"family":"Oberg","given":"Kevin","email":"kaoberg@usgs.gov","middleInitial":"A.","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":570130,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jackson, Patrick Ryan","contributorId":34043,"corporation":false,"usgs":true,"family":"Jackson","given":"Patrick","email":"","middleInitial":"Ryan","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":570131,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":97754,"text":"ofr20091139 - 2009 - Carbonatites of the world, explored deposits of Nb and REE— Database and grade and tonnage models","interactions":[],"lastModifiedDate":"2021-08-24T18:17:30.570152","indexId":"ofr20091139","displayToPublicDate":"2009-08-13T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2009-1139","title":"Carbonatites of the world, explored deposits of Nb and REE— Database and grade and tonnage models","docAbstract":"This report is based on published tonnage and grade data on 58 Nb- and rare-earth-element (REE)-bearing carbonatite deposits that are mostly well explored and are partially mined or contain resources of these elements. The deposits represent only a part of the known 527 carbonatites around the world, but they are characterized by reliable quantitative data on ore tonnages and grades of niobium and REE. \r\n\r\nGrade and tonnage models are an important component of mineral resource assessments. Carbonatites present one of the main natural sources of niobium and rare-earth elements, the economic importance of which grows consistently. A purpose of this report is to update earlier publications. New information about known deposits, as well as data on new deposits published during the last decade, are incorporated in the present paper. The compiled database (appendix 1; linked to right) contains 60 explored Nb- and REE-bearing carbonatite deposits - resources of 55 of these deposits are taken from publications. In the present updated grade-tonnage model we have added 24 deposits comparing with the previous model of Singer (1998). Resources of most deposits are residuum ores in the upper part of carbonatite bodies. \r\n\r\nMineral-deposit models are important in exploration planning and quantitative resource assessments for two reasons: (1) grades and tonnages among deposit types vary significantly, and (2) deposits of different types are present in distinct geologic settings that can be identified from geologic maps. Mineral-deposit models combine the diverse geoscience information on geology, mineral occurrences, geophysics, and geochemistry used in resource assessments and mineral exploration. Globally based deposit models allow recognition of important features and demonstrate how common different features are. Well-designed deposit models allow geologists to deduce possible mineral-deposit types in a given geologic environment, and the grade and tonnage models allow economists to estimate the possible economic viability of these resources. Thus, mineral-deposit models play a central role in presenting geoscience information in a useful form to policy makers. The foundation of mineral-deposit models is information about known deposits. This publication presents the latest geologic information and newly developed grade and tonnage models for Nb- and REE-carbonatite deposits in digital form. The publication contains computer files with information on deposits from around the world. It also contains a text file allowing locations of all deposits to be plotted in geographic information system (GIS) programs. The data are presented in FileMaker Pro as well as in .xls and text files to make the information available to a broadly based audience. The value of this information and any derived analyses depends critically on the consistent manner of data gathering. For this reason, we first discuss the rules used in this compilation. Next, the fields of the database are explained. Finally, we provide new grade and tonnage models and analysis of the information in the file.","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20091139","usgsCitation":"Berger, V.I., Singer, D.A., and Orris, G.J., 2009, Carbonatites of the world, explored deposits of Nb and REE— Database and grade and tonnage models: U.S. Geological Survey Open-File Report 2009-1139, iii, 17 p., https://doi.org/10.3133/ofr20091139.","productDescription":"iii, 17 p.","additionalOnlineFiles":"Y","costCenters":[{"id":660,"text":"Western Mineral Resources Science Center","active":false,"usgs":true}],"links":[{"id":125472,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2009_1139.jpg"},{"id":388437,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_86951.htm"},{"id":12920,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2009/1139/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124,-31 ], [ -124,71 ], [ 127,71 ], [ 127,-31 ], [ -124,-31 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e49e5e4b07f02db5e6a09","contributors":{"authors":[{"text":"Berger, Vladimir I.","contributorId":15246,"corporation":false,"usgs":true,"family":"Berger","given":"Vladimir","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":303050,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Singer, Donald A. dsinger@usgs.gov","contributorId":5601,"corporation":false,"usgs":true,"family":"Singer","given":"Donald","email":"dsinger@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":303049,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Orris, Greta J. 0000-0002-2340-9955 greta@usgs.gov","orcid":"https://orcid.org/0000-0002-2340-9955","contributorId":3472,"corporation":false,"usgs":true,"family":"Orris","given":"Greta","email":"greta@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":303048,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":97755,"text":"tm6A30 - 2009 - Revised multi-node well (MNW2) package for MODFLOW ground-water flow model","interactions":[],"lastModifiedDate":"2019-08-13T14:25:27","indexId":"tm6A30","displayToPublicDate":"2009-08-13T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"6-A30","title":"Revised multi-node well (MNW2) package for MODFLOW ground-water flow model","docAbstract":"Wells that are open to multiple aquifers can provide preferential pathways to flow and solute transport that short-circuit normal fluid flowlines. Representing these features in a regional flow model can produce a more realistic and reliable simulation model. This report describes modifications to the Multi-Node Well (MNW) Package of the U.S. Geological Survey (USGS) three-dimensional ground-water flow model (MODFLOW). The modifications build on a previous version and add several new features, processes, and input and output options. The input structure of the revised MNW (MNW2) is more well-centered than the original verion of MNW (MNW1) and allows the user to easily define hydraulic characteristics of each multi-node well. MNW2 also allows calculations of additional head changes due to partial penetration effects, flow into a borehole through a seepage face, changes in well discharge related to changes in lift for a given pump, and intraborehole flows with a pump intake located at any specified depth within the well. MNW2 also offers an improved capability to simulate nonvertical wells. A new output option allows selected multi-node wells to be designated as 'observation wells' for which changes in selected variables with time will be written to separate output files to facilitate postprocessing. MNW2 is compatible with the MODFLOW-2000 and MODFLOW-2005 versions of MODFLOW and with the version of MODFLOW that includes the Ground-Water Transport process (MODFLOW-GWT).","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Chapter 6 of Section A, Ground water, Book 30, Modeling Techniques","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/tm6A30","isbn":"9781411324886","usgsCitation":"Konikow, L.F., Hornberger, G.Z., Halford, K.J., Hanson, R.T., and Harbaugh, A.W., 2009, Revised multi-node well (MNW2) package for MODFLOW ground-water flow model: U.S. Geological Survey Techniques and Methods 6-A30, viii, 67 p., https://doi.org/10.3133/tm6A30.","productDescription":"viii, 67 p.","costCenters":[{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"links":[{"id":118599,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/tm_6_a30.gif"},{"id":12922,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/tm/tm6a30/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4ad3e4b07f02db6828ab","contributors":{"authors":[{"text":"Konikow, Leonard F. 0000-0002-0940-3856 lkonikow@usgs.gov","orcid":"https://orcid.org/0000-0002-0940-3856","contributorId":158,"corporation":false,"usgs":true,"family":"Konikow","given":"Leonard","email":"lkonikow@usgs.gov","middleInitial":"F.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":303051,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hornberger, George Z.","contributorId":45806,"corporation":false,"usgs":true,"family":"Hornberger","given":"George","email":"","middleInitial":"Z.","affiliations":[],"preferred":false,"id":303055,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Halford, Keith J. 0000-0002-7322-1846 khalford@usgs.gov","orcid":"https://orcid.org/0000-0002-7322-1846","contributorId":1374,"corporation":false,"usgs":true,"family":"Halford","given":"Keith","email":"khalford@usgs.gov","middleInitial":"J.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":303054,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanson, Randall T. 0000-0002-9819-7141 rthanson@usgs.gov","orcid":"https://orcid.org/0000-0002-9819-7141","contributorId":801,"corporation":false,"usgs":true,"family":"Hanson","given":"Randall","email":"rthanson@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":303053,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harbaugh, Arlen W. harbaugh@usgs.gov","contributorId":426,"corporation":false,"usgs":true,"family":"Harbaugh","given":"Arlen","email":"harbaugh@usgs.gov","middleInitial":"W.","affiliations":[],"preferred":true,"id":303052,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70208539,"text":"70208539 - 2009 - Ignoring detailed fast-changing dynamics of land use overestimates regional terrestrial carbon sequestration","interactions":[],"lastModifiedDate":"2020-02-20T10:13:47","indexId":"70208539","displayToPublicDate":"2009-08-12T10:13:41","publicationYear":"2009","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1011,"text":"Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Ignoring detailed fast-changing dynamics of land use overestimates regional terrestrial carbon sequestration","docAbstract":"<p><span>Land use change is critical in determining the distribution, magnitude and mechanisms of terrestrial carbon budgets at the local to global scales. To date, almost all regional to global carbon cycle studies are driven by a static land use map or land use change statistics with decadal time intervals. The biases in quantifying carbon exchange between the terrestrial ecosystems and the atmosphere caused by using such land use change information have not been investigated. Here, we used the General Ensemble biogeochemical Modeling System (GEMS), along with consistent and spatially explicit land use change scenarios with different intervals (1 yr, 5 yrs, 10 yrs and static, respectively), to evaluate the impacts of land use change data frequency on estimating regional carbon sequestration in the southeastern United States. Our results indicate that ignoring the detailed fast-changing dynamics of land use can lead to a significant overestimation of carbon uptake by the terrestrial ecosystem. Regional carbon sequestration increased from 0.27 to 0.69, 0.80 and 0.97 Mg C ha</span><sup>−1</sup><span>&nbsp;yr</span><sup>−1</sup><span>&nbsp;when land use change data frequency shifting from 1 year to 5 years, 10 years interval and static land use information, respectively. Carbon removal by forest harvesting and prolonged cumulative impacts of historical land use change on carbon cycle accounted for the differences in carbon sequestration between static and dynamic land use change scenarios. The results suggest that it is critical to incorporate the detailed dynamics of land use change into local to global carbon cycle studies. Otherwise, it is impossible to accurately quantify the geographic distributions, magnitudes, and mechanisms of terrestrial carbon sequestration at the local to global scales.</span></p>","language":"English","publisher":"European Geosciences Union","doi":"10.5194/bg-6-1647-2009","usgsCitation":"Zhao, S., Liu, S., and Li, Z., 2009, Ignoring detailed fast-changing dynamics of land use overestimates regional terrestrial carbon sequestration: Biogeosciences, v. 6, p. 1647-1654, https://doi.org/10.5194/bg-6-1647-2009.","productDescription":"8 p.","startPage":"1647","endPage":"1654","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":476068,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/bg-6-1647-2009","text":"Publisher Index Page"},{"id":372343,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Georgia","county":"Chattahoochee County, Marion County, Muscogee County, Russell County","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -85.45440673828125,\n              32.06628317261135\n            ],\n            [\n              -84.32281494140625,\n              32.06628317261135\n            ],\n            [\n              -84.32281494140625,\n              32.669436832605314\n            ],\n            [\n              -85.45440673828125,\n              32.669436832605314\n            ],\n            [\n              -85.45440673828125,\n              32.06628317261135\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"6","noUsgsAuthors":false,"publicationDate":"2009-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhao, S.Q.","contributorId":63235,"corporation":false,"usgs":true,"family":"Zhao","given":"S.Q.","email":"","affiliations":[],"preferred":false,"id":782342,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liu, S.","contributorId":149250,"corporation":false,"usgs":false,"family":"Liu","given":"S.","email":"","affiliations":[],"preferred":false,"id":782343,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Zhengpeng","contributorId":222506,"corporation":false,"usgs":true,"family":"Li","given":"Zhengpeng","email":"","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":782345,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":97748,"text":"sir20095056 - 2009 - Simulation of the Groundwater-Flow System in Pierce, Polk, and St. Croix Counties, Wisconsin","interactions":[],"lastModifiedDate":"2012-03-08T17:16:30","indexId":"sir20095056","displayToPublicDate":"2009-08-12T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2009-5056","title":"Simulation of the Groundwater-Flow System in Pierce, Polk, and St. Croix Counties, Wisconsin","docAbstract":"Groundwater is the sole source of residential water supply in Pierce, Polk, and St. Croix Counties, Wisconsin. A regional three-dimensional groundwater-flow model and three associated demonstration inset models were developed to simulate the groundwater-flow systems in the three-county area. The models were developed by the U.S. Geological Survey in cooperation with the three county governments. The objectives of the regional model of Pierce, Polk, and St. Croix Counties were to improve understanding of the groundwaterflow system and to develop a tool suitable for evaluating the effects of potential water-management programs.\r\n\r\nThe regional groundwater-flow model described in this report simulates the major hydrogeologic features of the modeled area, including bedrock and surficial aquifers, groundwater/surface-water interactions, and groundwater withdrawals from high-capacity wells. Results from the regional model indicate that about 82 percent of groundwater in the three counties is from recharge within the counties; 15 percent is from surface-water sources, consisting primarily of recirculated groundwater seepage in areas with abrupt surface-water-level changes, such as near waterfalls, dams, and the downgradient side of reservoirs and lakes; and 4 percent is from inflow across the county boundaries. Groundwater flow out of the counties is to streams (85 percent), outflow across county boundaries (14 percent), and pumping wells (1 percent). These results demonstrate that the primary source of groundwater withdrawn by pumping wells is water that recharges within the counties and would otherwise discharge to local streams and lakes.\r\n\r\nUnder current conditions, the St. Croix and Mississippi Rivers are groundwater discharge locations (gaining reaches) and appear to function as 'fully penetrating' hydraulic boundaries such that groundwater does not cross between Wisconsin and Minnesota beneath them. Being hydraulic boundaries, however, they can change in response to water withdrawals. Tributary rivers act as 'partially penetrating' hydraulic boundaries such that groundwater can flow underneath them through the deep sandstone aquifers. The model also demonstrates the effects of development on groundwater in the study area. Water-level declines since predevelopment (no withdrawal wells) are most pronounced where pumping is greatest and flow between layered aquifers is impeded by confining units or faults. The maximum simulated water-level decline is about 40 feet in the deep Mount Simon aquifer below the city of Hudson, Wisconsin.\r\n\r\nThree inset models were extracted from the regional model to demonstrate the process and additional capabilities of the U.S. Geological Survey MODFLOW code. Although the inset models were designed to provide information about the groundwater-flow system, results from the inset models are presented for demonstration purposes only and are not sufficiently detailed or calibrated to be used for decisionmaking purposes without refinement. Simulation of groundwater/lake-water interaction around Twin Lakes near Roberts, in St. Croix County, Wisconsin, showed that groundwater represents approximately 5 to 20 percent of the overall lake-water budget. Groundwater-contributing areas to streams in western Pierce County are generally similar in size to the surface-water-contributing areas but do not necessarily correspond to the same land area. Transient streamflow simulations of Osceola Creek in Polk County demonstrate how stream base flow can be influenced not only by seasonal precipitation and recharge variability but also by systematic changes to the system, such as groundwater withdrawal from wells.","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/sir20095056","isbn":"9781411324299","collaboration":"Prepared in cooperation with Pierce, Polk, and St. Croix Counties","usgsCitation":"Juckem, P.F., 2009, Simulation of the Groundwater-Flow System in Pierce, Polk, and St. Croix Counties, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2009-5056, vi, 54 p., https://doi.org/10.3133/sir20095056.","productDescription":"vi, 54 p.","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":125590,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2009_5056.jpg"},{"id":12914,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2009/5056/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -93.25,44.25 ], [ -93.25,46 ], [ -91.75,46 ], [ -91.75,44.25 ], [ -93.25,44.25 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b00e4b07f02db69835e","contributors":{"authors":[{"text":"Juckem, Paul F. 0000-0002-3613-1761 pfjuckem@usgs.gov","orcid":"https://orcid.org/0000-0002-3613-1761","contributorId":1905,"corporation":false,"usgs":true,"family":"Juckem","given":"Paul","email":"pfjuckem@usgs.gov","middleInitial":"F.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":303038,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":97749,"text":"pp1767 - 2009 - Brine migration from a flooded salt mine in the Genesee Valley, Livingston County, New York: Geochemical modeling and simulation of variable-density flow","interactions":[],"lastModifiedDate":"2023-12-14T20:22:52.539518","indexId":"pp1767","displayToPublicDate":"2009-08-12T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1767","title":"Brine migration from a flooded salt mine in the Genesee Valley, Livingston County, New York: Geochemical modeling and simulation of variable-density flow","docAbstract":"<p>The Retsof salt mine in upstate New York was flooded from 1994 to 1996 after two roof collapses created rubble chimneys in overlying bedrock that intersected a confined aquifer in glacial sediments. The mine now contains about 60 billion liters of saturated halite brine that is slowly being displaced as the weight of overlying sediments causes the mine cavity to close, a process that could last several hundred years. Saline water was detected in the confined aquifer in 2002, and a brine-mitigation project that includes pumping followed by onsite desalination was implemented in 2006 to prevent further migration of saline water from the collapse area. A study was conducted by the U.S. Geological Survey using geochemical and variable-density flow modeling to determine sources of salinity in the confined aquifer and to assess (1) processes that control movement and mixing of waters in the collapse area, (2) the effect of pumping on salinity, and (3) the potential for anhydrite dissolution and subsequent land subsidence resulting from mixing of waters induced by pumping.</p><p>The primary source of salinity in the collapse area is halite brine that was displaced from the flooded mine and transported upward by advection and dispersion through the rubble chimneys and surrounding deformation zone. Geochemical and variable-density modeling indicate that salinity in the upper part of the collapse area is partly derived from inflow of saline water from bedrock fracture zones during water-level recovery (January 1996 through August 2006). The lateral diversion of brine into bedrock fracture zones promoted the upward migration of mine water through mixing with lower density waters. The relative contributions of mine water, bedrock water, and aquifer water to the observed salinity profile within the collapse area are controlled by the rates of flow to and from bedrock fracture zones. Variable-density simulations of water-level recovery indicate that saline water has probably not migrated beyond the collapse area, while simulations of pumping indicate that further upward migration of brine and saline water is now prevented by groundwater withdrawals under the brine-mitigation project. Geochemical modeling indicates that additional land subsidence as a result of anhydrite dissolution in the collapse area is not a concern, as long as the rate of brine pumping is less than the rate of upward flow of brine from the flooded mine.</p><p>The collapse area above the flooded salt mine is within a glacially scoured bedrock valley that is filled with more than 150 meters of glacial drift. A confined aquifer at the bottom of the glacial sediments (referred to as the lower confined aquifer, or LCA) was the source of most of the water that flooded the mine. Two rubble chimneys that formed above the roof collapses in 1994 hydraulically connect the flooded mine to the LCA through 180 meters of sedimentary rock. From 1996 through 2006, water levels in the aquifer system recovered and the brine-displacement rate ranged from 4.4 to 1.6 liters per second, as estimated from land-surface subsidence above the mine. A zone of fracturing within the bedrock (the deformation zone) formed around the rubble chimneys as rock layers sagged toward the mine cavity after the roof collapses. Borehole geophysical surveys have identified three saline-water-bearing fracture zones in the bedrock: at stratigraphic contacts between the Onondaga and Bertie Limestones (O/B-FZ) and the Bertie Limestone and the Camillus Shale (B/C-FZ), and in the Syracuse Formation (Syr-FZ). The only outlets for brine displaced from the mine are through the rubble chimneys, but some of the brine could be diverted laterally into fracture zones in the rocks that lie between the mine and the LCA.</p><p>Inverse geochemical models developed using PHREEQC indicate that halite brine in the flooded mine is derived from a mixture of freshwater from the LCA (81 percent), saline water from bedrock fracture zones (16 percent), and an hypothesized bromide-rich brine (3 percent) assumed to originate from salt-bearing rocks above the flooded mine. Geochemical modeling results also indicate that halite brine entering the rubble chimneys is diluted by both bedrock water and aquifer water, and that water from the mine has not reached the bedrock surface. Forward geochemical models indicate that additional land subsidence could occur if pumping from the brine-mitigation project were to introduce either freshwater or bedrock water that is undersaturated with respect to anhydrite into the lower part of the rubble chimneys. In this unlikely scenario, the maximum subsidence rates are predicted to range from 0.6 to 1.1 centimeters per year—subsidence rates would be lower (0.1 to 0.6 centimeters per year) if ion-exchange reactions affect the water chemistry.</p><p>Variable-density, transient groundwater-flow models were constructed using SEAWAT to simulate the movement of saline water, aquifer water, bedrock water, and brine within the rubble chimneys and surrounding deformation zone during the 10.7-year period following flooding of the salt mine. Two three-dimensional models reproduced the profile of halite saturation with depth measured in September 2006 reasonably well, and neither model indicated that saline water had migrated beyond the collapse area. The models differed in the number of fracture zones represented: one zone in model A (O/B-FZ) and three zones in model B (O/B-FZ, B/C-FZ, and Syr-FZ). It is unknown whether model A or model B better represents current conditions because the lateral extents of the B/C-FZ and Syr-FZ have not been delineated beyond the collapse area.</p><p>In model A, the salinity of water in the upper part of the rubble chimneys is derived mainly from the inflow of bedrock water from the O/B-FZ, as indicated by geochemical models. Bedrock water that was pushed upward by brine during the 10.7-year simulation period formed a diffuse front above a nearly horizontal brine level in both chimneys. In model B, some of the salinity in the upper part of the rubble chimneys is derived from mine water. The rate of bedrock-water inflow from the O/B-FZ was lower in model B than in model A, and mixing with waters from the Syr-FZ and B/C-FZ transported mine water higher in the water column than in model A. Simulated brine levels in both chimneys sloped northward, reflecting lateral diversion of brine into the B/C-FZ, and less aquifer water was displaced from the collapse area than in model A.</p><p>Models A and B were used to simulate changes in water levels and salinity produced by pumping for the brine-mitigation project from September 2006 through February 2008. Both simulations indicated that current pumping rates are sufficient to offset upward migration of brine and saline water through the collapse area and, therefore, to further prevent contamination of the LCA. A greater decrease in salinity was simulated in model B, however, because the porosity of the rubble chimneys was lower (6 percent compared to 10 percent in model A), and some brine and saline waters were diverted through the B/C-FZ. Model B better simulates the influent saturation to the desalination plant, the amount of halite produced, and the observed declines in saturations than model A, which is more consistent with results of geochemical modeling. Sensitivity analyses indicate that the actual brine-displacement rate could be lower than estimated because simulated declines in saturations underpredict the observed decline from September 2006 through February 2008.</p><p>Although halite saturations within the upper part of the collapse area are predicted to decrease with continued pumping, brine displacement from the flooded mine is expected to continue for hundreds of years. Simulations of a shutdown of the brine-mitigation project indicate southward migration of saline water through the LCA, extending 700 meters to the model boundary within 10 years. Continued migration of saline water would eventually form a pool in the LCA in a bedrock depression 8 kilometers south of the collapse area near Sonyea, but the large relative density of the saline water would likely prevent it from reaching overlying aquifers. Simulations also indicate that brine will migrate through bedrock fracture zones—some brine could possibly emerge updip to the north where the subcrop area of the Bertie Limestone intersects the bedrock surface near Avon, but the projected time of travel is unknown.</p>","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/pp1767","collaboration":"Prepared in cooperation with the New York State Attorney General's Office","usgsCitation":"Yager, R.M., Misut, P.E., Langevin, C.D., and Parkhurst, D.L., 2009, Brine migration from a flooded salt mine in the Genesee Valley, Livingston County, New York: Geochemical modeling and simulation of variable-density flow: U.S. Geological Survey Professional Paper 1767, Report: vii, 52 p.; Animations, https://doi.org/10.3133/pp1767.","productDescription":"Report: vii, 52 p.; Animations","additionalOnlineFiles":"Y","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":423583,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_86941.htm","linkFileType":{"id":5,"text":"html"}},{"id":118583,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/pp_1767.jpg"},{"id":12915,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/pp/pp1767/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","county":"Livingston County","otherGeospatial":"Genesee Valley","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -77.925,\n              42.9333\n            ],\n            [\n              -77.925,\n              42.5439\n            ],\n            [\n              -77.6556,\n              42.5439\n            ],\n            [\n              -77.6556,\n              42.9333\n            ],\n            [\n              -77.925,\n              42.9333\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a0ae4b07f02db5fb391","contributors":{"authors":[{"text":"Yager, Richard M. 0000-0001-7725-1148 ryager@usgs.gov","orcid":"https://orcid.org/0000-0001-7725-1148","contributorId":950,"corporation":false,"usgs":true,"family":"Yager","given":"Richard","email":"ryager@usgs.gov","middleInitial":"M.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":303039,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Misut, Paul E. 0000-0002-6502-5255 pemisut@usgs.gov","orcid":"https://orcid.org/0000-0002-6502-5255","contributorId":1073,"corporation":false,"usgs":true,"family":"Misut","given":"Paul","email":"pemisut@usgs.gov","middleInitial":"E.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":303041,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langevin, Christian D. 0000-0001-5610-9759 langevin@usgs.gov","orcid":"https://orcid.org/0000-0001-5610-9759","contributorId":1030,"corporation":false,"usgs":true,"family":"Langevin","given":"Christian","email":"langevin@usgs.gov","middleInitial":"D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":303040,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parkhurst, David L. 0000-0003-3348-1544 dlpark@usgs.gov","orcid":"https://orcid.org/0000-0003-3348-1544","contributorId":1088,"corporation":false,"usgs":true,"family":"Parkhurst","given":"David","email":"dlpark@usgs.gov","middleInitial":"L.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":303042,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":97742,"text":"sir20095106 - 2009 - Effect of detention basin release rates on flood flows: Application of a model to the Blackberry Creek Watershed in Kane County, Illinois","interactions":[],"lastModifiedDate":"2024-06-14T21:11:29.875122","indexId":"sir20095106","displayToPublicDate":"2009-08-11T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2009-5106","title":"Effect of detention basin release rates on flood flows: Application of a model to the Blackberry Creek Watershed in Kane County, Illinois","docAbstract":"<p>The effects of stormwater detention basins with specified release rates are examined on the watershed scale with a Hydrological Simulation Program - FORTRAN (HSPF) continuous-simulation model. Modeling procedures for specifying release rates from detention basins with orifice and weir discharge configurations are discussed in this report. To facilitate future detention modeling as a tool for watershed management, a chart relating watershed impervious area to detention volume is presented. The report also presents a case study of the Blackberry Creek watershed in Kane County, Ill., a rapidly urbanizing area seeking to avoid future flood damages from increased urbanization, to illustrate the effects of various detention basin release rates on flood peaks and volumes and flood frequencies. The case study compares flows simulated with a 1996 land-use HSPF model to those simulated with four different 2020 projected land-use HSPF model scenarios - no detention, and detention basins with release rates of 0.08, 0.10, and 0.12 cubic feet per second per acre (ft<sup>3</sup>/s-acre), respectively. Results of the simulations for 15 locations, which included the downstream ends of all tributaries and various locations along the main stem, showed that a release rate of 0.10 ft<sup>3</sup>/s-acre, in general, can maintain postdevelopment 100-year peak-flood discharge at a similar magnitude to that of 1996 land-use conditions. Although the release rate is designed to reduce the 100-year peak flow, reduction of the 2-year peak flow is also achieved for a smaller proportion of the peak. Results also showed that the 0.10 ft<sup>3</sup>/s-acre release rate was less effective in watersheds with relatively high percentages of preexisting (1996) development than in watersheds with less preexisting development.</p>","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/sir20095106","collaboration":"Prepared in cooperation with the Kane County Department of Environmental and Building Management and the Illinois Department of Natural Resources-Office of Water Resources","usgsCitation":"Soong, D., Murphy, E., and Straub, T., 2009, Effect of detention basin release rates on flood flows: Application of a model to the Blackberry Creek Watershed in Kane County, Illinois: U.S. Geological Survey Scientific Investigations Report 2009-5106, vi, 33 p., https://doi.org/10.3133/sir20095106.","productDescription":"vi, 33 p.","onlineOnly":"Y","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":12907,"rank":3,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2009/5106/","linkFileType":{"id":5,"text":"html"}},{"id":344330,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2009/5106/pdf/sir2009-5106.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":430244,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_86940.htm","linkFileType":{"id":5,"text":"html"}},{"id":118643,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2009_5106.jpg"}],"country":"United States","state":"Illinois","county":"Kane County","otherGeospatial":"Blackberry Creek Watershed","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -88.53333333333333,41.7 ], [ -88.53333333333333,41.93333333333333 ], [ -88.31666666666666,41.93333333333333 ], [ -88.31666666666666,41.7 ], [ -88.53333333333333,41.7 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a4be4b07f02db62584e","contributors":{"authors":[{"text":"Soong, David T.","contributorId":87487,"corporation":false,"usgs":true,"family":"Soong","given":"David T.","affiliations":[],"preferred":false,"id":303019,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Murphy, Elizabeth A.","contributorId":69660,"corporation":false,"usgs":true,"family":"Murphy","given":"Elizabeth A.","affiliations":[],"preferred":false,"id":303018,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Straub, Timothy D. 0000-0002-5896-0851 tdstraub@usgs.gov","orcid":"https://orcid.org/0000-0002-5896-0851","contributorId":2273,"corporation":false,"usgs":true,"family":"Straub","given":"Timothy D.","email":"tdstraub@usgs.gov","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":false,"id":303017,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":97736,"text":"sim3085 - 2009 - Terrestrial Ecosystems - Land Surface Forms of the Conterminous United States","interactions":[],"lastModifiedDate":"2012-02-10T00:11:46","indexId":"sim3085","displayToPublicDate":"2009-08-11T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3085","title":"Terrestrial Ecosystems - Land Surface Forms of the Conterminous United States","docAbstract":"As part of an effort to map terrestrial ecosystems, the U.S. Geological Survey has generated land surface form classes to be used in creating maps depicting standardized, terrestrial ecosystem models for the conterminous United States, using an ecosystems classification developed by NatureServe . A biophysical stratification approach, developed for South America and now being implemented globally, was used to model the ecosystem distributions. Since land surface forms strongly influence the differentiation and distribution of terrestrial ecosystems, they are one of the key input layers in this biophysical stratification.\r\n\r\nAfter extensive investigation into various land surface form mapping methodologies, the decision was made to use the methodology developed by the Missouri Resource Assessment Partnership (MoRAP). MoRAP made modifications to Hammond's land surface form classification, which allowed the use of 30-meter source data and a 1-km2 window for analyzing the data cell and its surrounding cells (neighborhood analysis). While Hammond's methodology was based on three topographic variables, slope, local relief, and profile type, MoRAP's methodology uses only slope and local relief. Using the MoRAP method, slope is classified as gently sloping when more than 50 percent of the area in a 1-km2 neighborhood has slope less than 8 percent, otherwise the area is considered moderately sloping. Local relief, which is the difference between the maximum and minimum elevation in a neighborhood, is classified into five groups: 0-15 m, 16-30 m, 31-90 m, 91-150 m, and >150 m. The land surface form classes are derived by combining slope and local relief to create eight landform classes: flat plains (gently sloping and local relief =< 15 m), smooth plains (gently sloping and 15 m < local relief =< 30 m), irregular plains (gently sloping and 30 m < local relief =< 90 m), escarpments (gently sloping and local relief > 90 m), low hills (not gently sloping and local relief =< 30 m), hills (not gently sloping and 30 m < local relief =< 90 m), breaks/foothills (not gently sloping and 90 m < local relief =< 150 m), and low mountains (not gently sloping and local relief > 150 m). However, in the USGS application of the MoRAP methodology, an additional local relief group was used (> 400 m) to capture additional local topographic variation. As a result, low mountains were redefined as not gently sloping and 151 m < local relief < 400 m, and a new land surface form class, high mountains/deep canyons, was identified as not gently sloping and local relief > 400 m. The final application of the MoRAP methodology was implemented using the USGS 30-meter National Elevation Dataset and an existing USGS slope dataset that had been derived by calculating the slope from the NED in Universal Transverse Mercator (UTM) coordinates in each UTM zone, and then combining all of the zones into a national dataset. \r\n\r\nThis map shows a smoothed image of the nine land surface form classes based on MoRAP's methodology. Additional information about this map and any data developed for the ecosystems modeling of the conterminous United States is available online at http://rmgsc.cr.usgs.gov/ecosystems/.","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/sim3085","collaboration":"Prepared in collaboration with NatureServe","usgsCitation":"Cress, J., Sayre, R.G., Comer, P., and Warner, H., 2009, Terrestrial Ecosystems - Land Surface Forms of the Conterminous United States (Version 1.0): U.S. Geological Survey Scientific Investigations Map 3085, Sheet: 45 x 35 inches, https://doi.org/10.3133/sim3085.","productDescription":"Sheet: 45 x 35 inches","costCenters":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"links":[{"id":125537,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim_3085.jpg"},{"id":12901,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/3085/","linkFileType":{"id":5,"text":"html"}}],"scale":"5000000","projection":"Albers Equal Area Conic","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -125,23 ], [ -125,50 ], [ -65,50 ], [ -65,23 ], [ -125,23 ] ] ] } } ] }","edition":"Version 1.0","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4ac9e4b07f02db67c43c","contributors":{"authors":[{"text":"Cress, Jill J.","contributorId":76832,"corporation":false,"usgs":true,"family":"Cress","given":"Jill J.","affiliations":[],"preferred":false,"id":303005,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sayre, Roger G. rsayre@usgs.gov","contributorId":2882,"corporation":false,"usgs":true,"family":"Sayre","given":"Roger","email":"rsayre@usgs.gov","middleInitial":"G.","affiliations":[],"preferred":false,"id":303004,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Comer, Patrick","contributorId":85683,"corporation":false,"usgs":true,"family":"Comer","given":"Patrick","affiliations":[],"preferred":false,"id":303006,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warner, Harumi hwarner@usgs.gov","contributorId":2881,"corporation":false,"usgs":true,"family":"Warner","given":"Harumi","email":"hwarner@usgs.gov","affiliations":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true}],"preferred":true,"id":303003,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":97735,"text":"sim3084 - 2009 - Terrestrial ecosystems - Isobioclimates of the conterminous United States","interactions":[],"lastModifiedDate":"2016-07-06T14:47:28","indexId":"sim3084","displayToPublicDate":"2009-08-11T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3084","title":"Terrestrial ecosystems - Isobioclimates of the conterminous United States","docAbstract":"<p>As part of an effort to map terrestrial ecosystems, the U.S. Geological Survey has generated isobioclimate classes to be used in creating maps depicting standardized, terrestrial ecosystem models for the conterminous United States, using an ecosystems classification developed by NatureServe . A biophysical stratification approach, developed for South America (Sayre and others, 2008) and now being implemented globally, was used to model the ecosystem distributions. Bioclimate regimes strongly influence the differentiation and distribution of terrestrial ecosystems, and are therefore one of the key input layers in this biophysical stratification.</p>\n<p>The Rivas-Mart&iacute;nez methodology is based on the concept of establishing a quantifiable classification system which would closely relate the distribution of vegetation to climatic parameters and indices. This method first establishes bioclimatic indices calculated from various ranges of temperature and precipitation data, compares these indices to defined thresholds, and finally applies sets of decision rules to identify the climate classes. The climate classification is hierarchical with four levels: macrobioclimates, bioclimates, thermotypes, and ombrotypes. Thermotypes, which represent thermoclimatic belts, are identified using the positive annual temperature (Tp) thresholds or the compensated thermicity index (Itc) thresholds. Ombrotypes, which represent ombroclimatic belts, are based on the ombrothermic index (Io) which is calculated as a function of both the total positive precipitation and temperature . For this national implementation the source data used for establishing the bioclimatic indices was Daymet. Daymet temperature and precipitation data were developed from 18 years (1980&ndash;1997) of climatological records and is available at a spatial resolution of 1 kilometer . This implementation of the Rivas-Mart&iacute;nez methodology resulted in the generation of four climate layers for the conterminous United States: macroclimates, bioclimates, thermotypes, and ombrotypes.</p>\n<p>However, the biophysical stratification approach used for the ecosystems modeling effort required a single climate layer that accurately reflected regional variation in wet/dry gradients and hot/cold gradients, with a manageable number of classes. Therefore, the data layers for thermotypes and ombrotypes were combined, yielding 127 unique thermotype-ombrotype combinations.The isobioclimates image shows ombrotypic regions (dry/wet gradients) for each thermotypic (warm/cold) region.&nbsp;<strong>Additional information about this map and any of the data developed for the ecosystems modeling of the conterminous United States is available online at&nbsp;<a href=\"http://rmgsc.cr.usgs.gov/ecosystems/\">http://rmgsc.cr.usgs.gov/ecosystems/</a>.</strong></p>","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/sim3084","collaboration":"Prepared in collaboration with NatureServe","usgsCitation":"Cress, J., Sayre, R.G., Comer, P., and Warner, H., 2009, Terrestrial ecosystems - Isobioclimates of the conterminous United States (Version 1.0): U.S. Geological Survey Scientific Investigations Map 3084, Sheet: 45 x 35 inches, https://doi.org/10.3133/sim3084.","productDescription":"Sheet: 45 x 35 inches","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"links":[{"id":126594,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim_3084.jpg"},{"id":12900,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/3084/","linkFileType":{"id":5,"text":"html"}}],"scale":"5000000","projection":"Albers Equal Area Conic","country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -97.18505859374999,\n              25.97779895546436\n            ],\n            [\n              -97.42675781249999,\n              25.878994400196202\n            ],\n            [\n              -98.2177734375,\n              26.115985925333536\n            ],\n            [\n              -99.0966796875,\n              26.47057302237511\n            ],\n            [\n              -99.580078125,\n              27.625140335093324\n            ],\n            [\n              -100.283203125,\n              28.265682390146477\n            ],\n            [\n              -100.83251953125,\n              29.305561325527698\n            ],\n            [\n              -101.27197265625,\n              29.592565403314087\n            ],\n            [\n              -101.49169921875,\n              29.80251790576445\n            ],\n            [\n              -102.67822265625,\n              29.76437737516313\n            ],\n            [\n              -103.11767578124999,\n              29.036960648558267\n            ],\n            [\n              -104.47998046875,\n              29.611670115197377\n            ],\n            [\n              -104.7216796875,\n              29.973970240516614\n            ],\n            [\n              -104.69970703125,\n              30.20211367909724\n            ],\n            [\n              -104.9853515625,\n              30.675715404167743\n            ],\n            [\n              -106.12792968749999,\n              31.44741029142872\n            ],\n            [\n              -106.45751953125,\n              31.80289258670676\n            ],\n            [\n              -108.21533203125,\n              31.784216884487385\n            ],\n            [\n              -108.204345703125,\n              31.344254455668054\n            ],\n            [\n              -110.98388671874999,\n              31.3348710339506\n            ],\n            [\n              -114.82910156249999,\n              32.52828936482526\n            ],\n            [\n              -114.7412109375,\n              32.731840896865684\n            ],\n            [\n              -117.13623046874999,\n              32.54681317351514\n            ],\n            [\n              -117.3779296875,\n              33.211116472416855\n            ],\n            [\n              -118.23486328125,\n              33.779147331286474\n            ],\n            [\n              -118.564453125,\n              34.043556504127444\n            ],\n            [\n              -118.94897460937499,\n              34.052659421375964\n            ],\n            [\n              -119.2236328125,\n              34.161818161230386\n            ],\n            [\n              -119.66308593749999,\n              34.470335121217495\n            ],\n            [\n              -120.44311523437499,\n              34.45221847282654\n            ],\n            [\n              -120.640869140625,\n              34.56990638085636\n            ],\n            [\n              -120.673828125,\n              35.137879119634185\n            ],\n            [\n              -121.9482421875,\n              36.4566360115962\n            ],\n            [\n              -121.83837890625,\n              36.721273880045004\n            ],\n            [\n              -121.88232421875,\n              36.949891786813296\n            ],\n            [\n              -122.05810546875,\n              36.96744946416931\n            ],\n            [\n              -122.40966796874999,\n              37.23032838760387\n            ],\n            [\n              -122.56347656249999,\n              37.71859032558816\n            ],\n            [\n              -122.67333984374999,\n              37.90953361677018\n            ],\n            [\n              -123.00292968749999,\n              37.996162679728116\n            ],\n            [\n              -123.02490234375,\n              38.238180119798635\n            ],\n            [\n              -123.74999999999999,\n              38.92522904714054\n            ],\n            [\n              -123.85986328124999,\n              39.757879992021756\n            ],\n            [\n              -124.365234375,\n              40.27952566881291\n            ],\n            [\n              -124.4091796875,\n              40.463666324587685\n            ],\n            [\n              -124.18945312500001,\n              41.07935114946899\n            ],\n            [\n              -124.0576171875,\n              41.60722821271717\n            ],\n            [\n              -124.29931640625,\n              42.00032514831621\n            ],\n            [\n              -124.49707031249999,\n              42.827638636242284\n            ],\n            [\n              -124.16748046874999,\n              43.83452678223684\n            ],\n            [\n              -124.03564453125,\n              45.058001435398296\n            ],\n            [\n              -124.03564453125,\n              46.30140615437332\n            ],\n            [\n              -124.25537109375,\n              47.41322033016902\n            ],\n            [\n              -124.62890625,\n              47.91634204016118\n            ],\n            [\n              -124.69482421875,\n              48.4146186174932\n            ],\n            [\n              -123.96972656249999,\n              48.188063481211415\n            ],\n            [\n              -123.134765625,\n              48.17341248658084\n            ],\n            [\n              -122.78320312499999,\n              48.246625590713826\n            ],\n            [\n              -122.81616210937499,\n              48.42920055556841\n            ],\n            [\n              -123.18969726562499,\n              48.50204750525715\n            ],\n            [\n              -123.24462890625,\n              48.69096039092549\n            ],\n            [\n              -122.728271484375,\n              48.77067246880509\n            ],\n            [\n              -122.82714843749999,\n              49.001843917978526\n            ],\n            [\n              -95.174560546875,\n              49.01625665778159\n            ],\n            [\n              -95.152587890625,\n              49.38237278700955\n            ],\n            [\n              -94.81201171875,\n              49.31796095602274\n            ],\n            [\n              -94.669189453125,\n              48.777912755501845\n            ],\n            [\n              -93.834228515625,\n              48.63290858589532\n            ],\n            [\n              -93.8232421875,\n              48.516604348867475\n            ],\n            [\n              -93.44970703125,\n              48.56752037390827\n            ],\n            [\n              -93.33984375,\n              48.66194284607008\n            ],\n            [\n              -92.59277343749999,\n              48.531157010976706\n            ],\n            [\n              -92.08740234375,\n              48.37084770238363\n            ],\n            [\n              -91.4501953125,\n              48.06339653776211\n            ],\n            [\n              -91.07666015625,\n              48.19538740833338\n            ],\n            [\n              -90.8349609375,\n              48.23930899024905\n            ],\n            [\n              -90.791015625,\n              48.10743118848039\n            ],\n            [\n              -89.62646484375,\n              48.019324184801185\n            ],\n            [\n              -89.3463134765625,\n              47.98256841921402\n            ],\n            [\n              -88.3795166015625,\n              48.31973404047173\n            ],\n            [\n              -84.847412109375,\n              46.897739085507\n            ],\n            [\n              -84.55078125,\n              46.464349400461124\n            ],\n            [\n              -84.4134521484375,\n              46.49839225859763\n            ],\n            [\n              -84.19921875,\n              46.53619267489863\n            ],\n            [\n              -84.1058349609375,\n              46.51351558059737\n            ],\n            [\n              -84.122314453125,\n              46.320378031062354\n            ],\n            [\n              -84.00146484374999,\n              46.15700496290803\n            ],\n            [\n              -83.95751953125,\n              46.06560846138691\n            ],\n            [\n              -83.81469726562499,\n              46.11132565729796\n            ],\n            [\n              -83.6334228515625,\n              46.11894150610708\n            ],\n            [\n              -83.42468261718749,\n              46.0007775685566\n            ],\n            [\n              -83.583984375,\n              45.82497145796607\n            ],\n            [\n              -82.518310546875,\n              45.32897866218559\n            ],\n            [\n              -82.12280273437499,\n              43.57243174740972\n            ],\n            [\n              -82.40295410156249,\n              43.000629854450025\n            ],\n            [\n              -82.474365234375,\n              42.79136972365016\n            ],\n            [\n              -82.5732421875,\n              42.5611728553181\n            ],\n            [\n              -82.8533935546875,\n              42.370720143531955\n            ],\n            [\n              -83.07861328125,\n              42.32200108060303\n            ],\n            [\n              -83.12255859375,\n              42.14304156290942\n            ],\n            [\n              -83.14453125,\n              42.04521345501039\n            ],\n            [\n              -83.07861328125,\n              41.86956082699455\n            ],\n            [\n              -82.6776123046875,\n              41.68111756290652\n            ],\n            [\n              -82.3974609375,\n              41.68111756290652\n            ],\n            [\n              -81.243896484375,\n              42.21224516288584\n            ],\n            [\n              -80.09033203125,\n              42.39506551565123\n            ],\n            [\n              -78.9532470703125,\n              42.827638636242284\n            ],\n            [\n              -78.91754150390625,\n              42.95039177450287\n            ],\n            [\n              -79.06585693359375,\n              43.092960677116295\n            ],\n            [\n              -79.06036376953125,\n              43.26120612479979\n            ],\n            [\n              -79.19769287109375,\n              43.45291889355465\n            ],\n            [\n              -78.6895751953125,\n              43.632099415557754\n            ],\n            [\n              -76.7999267578125,\n              43.64005063334694\n            ],\n            [\n              -76.4483642578125,\n              44.11125397357153\n            ],\n            [\n              -75.7781982421875,\n              44.51609322284931\n            ],\n            [\n              -75.30029296875,\n              44.84029065139799\n            ],\n            [\n              -74.827880859375,\n              45.02695045318546\n            ],\n            [\n              -71.510009765625,\n              45.02695045318546\n            ],\n            [\n              -71.3232421875,\n              45.29034662473615\n            ],\n            [\n              -70.653076171875,\n              45.42158812329091\n            ],\n            [\n              -70.68603515625,\n              45.537136680398596\n            ],\n            [\n              -70.301513671875,\n              45.920587344733654\n            ],\n            [\n              -70.2685546875,\n              46.240651955001695\n            ],\n            [\n              -70.059814453125,\n              46.40756396630067\n            ],\n            [\n              -69.993896484375,\n              46.694667307773116\n            ],\n            [\n              -69.246826171875,\n              47.46523622438362\n            ],\n            [\n              -69.01611328125,\n              47.4355191531953\n            ],\n            [\n              -69.0380859375,\n              47.26432008025478\n            ],\n            [\n              -68.93920898437499,\n              47.19717795172789\n            ],\n            [\n              -68.35693359375,\n              47.36115300722623\n            ],\n            [\n              -67.763671875,\n              47.06263847995432\n            ],\n            [\n              -67.78564453125,\n              45.706179285330855\n            ],\n            [\n              -67.423095703125,\n              45.5679096098613\n            ],\n            [\n              -67.423095703125,\n              45.22074260255366\n            ],\n            [\n              -67.17041015625,\n              45.174292524076726\n            ],\n            [\n              -66.961669921875,\n              44.85586880735725\n            ],\n            [\n              -67.24731445312499,\n              44.63739123445585\n            ],\n            [\n              -68.236083984375,\n              44.308126684886126\n            ],\n            [\n              -68.258056640625,\n              44.20583500104184\n            ],\n            [\n              -69.06005859375,\n              44.04811573082351\n            ],\n            [\n              -69.80712890625,\n              43.73935207915473\n            ],\n            [\n              -70.048828125,\n              43.78695837311561\n            ],\n            [\n              -70.2081298828125,\n              43.73538317799622\n            ],\n            [\n              -70.191650390625,\n              43.58039085560786\n            ],\n            [\n              -70.587158203125,\n              43.25320494908846\n            ],\n            [\n              -70.81787109374999,\n              42.89206418807337\n            ],\n            [\n              -70.76568603515625,\n              42.70060440808085\n            ],\n            [\n              -70.68603515625,\n              42.66426107379467\n            ],\n            [\n              -70.63934326171875,\n              42.69051116998241\n            ],\n            [\n              -70.59814453125,\n              42.65416193033991\n            ],\n            [\n              -70.6585693359375,\n              42.589488572714245\n            ],\n            [\n              -70.8782958984375,\n              42.54498667313236\n            ],\n            [\n              -70.83984375,\n              42.508552415528634\n            ],\n            [\n              -70.9771728515625,\n              42.44372793752476\n            ],\n            [\n              -70.97442626953125,\n              42.391008609205045\n            ],\n            [\n              -70.8343505859375,\n              42.26917949243506\n            ],\n            [\n              -70.75469970703125,\n              42.24681856113825\n            ],\n            [\n              -70.64208984375,\n              42.07783959017503\n            ],\n            [\n              -70.5487060546875,\n              41.92680320648791\n            ],\n            [\n              -70.53497314453125,\n              41.820455096140314\n            ],\n            [\n              -70.42785644531249,\n              41.74672584176937\n            ],\n            [\n              -70.21636962890625,\n              41.73852846935917\n            ],\n            [\n              -70.0323486328125,\n              41.781552998900345\n            ],\n            [\n              -70.0103759765625,\n              41.85319643776675\n            ],\n            [\n              -70.0762939453125,\n              41.90432124806034\n            ],\n            [\n              -70.09277343749999,\n              42.02889410108475\n            ],\n            [\n              -70.16143798828125,\n              42.05948945192712\n            ],\n            [\n              -70.1751708984375,\n              42.01869237684385\n            ],\n            [\n              -70.24932861328125,\n              42.06356771883277\n            ],\n            [\n              -70.22186279296875,\n              42.07987816698549\n            ],\n            [\n              -70.13946533203124,\n              42.07580094787543\n            ],\n            [\n              -70.02960205078125,\n              42.02889410108475\n            ],\n            [\n              -69.96917724609375,\n              41.916585116228354\n            ],\n            [\n              -69.93072509765625,\n              41.7856490686444\n            ],\n            [\n              -69.93072509765625,\n              41.6770148220322\n            ],\n            [\n              -69.971923828125,\n              41.61338889474735\n            ],\n            [\n              -69.949951171875,\n              41.253032440653186\n            ],\n            [\n              -70.11199951171875,\n              41.23238023874142\n            ],\n            [\n              -70.279541015625,\n              41.304634388885916\n            ],\n            [\n              -70.44158935546875,\n              41.347948493443546\n            ],\n            [\n              -70.675048828125,\n              41.3500103516271\n            ],\n            [\n              -70.7684326171875,\n              41.31494988250963\n            ],\n            [\n              -70.7958984375,\n              41.29844430929419\n            ],\n            [\n              -71.03759765625,\n              41.49623534616764\n            ],\n            [\n              -71.10076904296875,\n              41.50034959128928\n            ],\n            [\n              -71.180419921875,\n              41.46125371076149\n            ],\n            [\n              -71.3616943359375,\n              41.45507852101139\n            ],\n            [\n              -71.44683837890625,\n              41.43654942411456\n            ],\n            [\n              -71.4935302734375,\n              41.36031866306708\n            ],\n            [\n              -71.531982421875,\n              41.16004614168688\n            ],\n            [\n              -71.97967529296874,\n              41.01928287604565\n            ],\n            [\n              -72.86956787109375,\n              40.724364221722716\n            ],\n            [\n              -73.33099365234375,\n              40.61812224225511\n            ],\n            [\n              -73.75946044921875,\n              40.58267063809529\n            ],\n            [\n              -73.92425537109375,\n              40.543026009954986\n            ],\n            [\n              -73.9874267578125,\n              40.46993497635153\n            ],\n            [\n              -73.96820068359375,\n              40.319325896602095\n            ],\n            [\n              -74.07806396484375,\n              39.928694653732364\n            ],\n            [\n              -74.1357421875,\n              39.631076770083666\n            ],\n            [\n              -74.3994140625,\n              39.364032338047984\n            ],\n            [\n              -74.6356201171875,\n              39.21948715423953\n            ],\n            [\n              -74.8004150390625,\n              38.96795115401593\n            ],\n            [\n              -74.96520996093749,\n              38.929502416386605\n            ],\n            [\n              -75.07507324218749,\n              38.775499003812946\n            ],\n            [\n              -75.0421142578125,\n              38.47939467327645\n            ],\n            [\n              -75.16845703124999,\n              38.004819966413194\n            ],\n            [\n              -75.574951171875,\n              37.65773212628274\n            ],\n            [\n              -75.882568359375,\n              37.16907157713011\n            ],\n            [\n              -75.9814453125,\n              36.88840804313823\n            ],\n            [\n              -75.706787109375,\n              36.16448788632064\n            ],\n            [\n              -75.443115234375,\n              35.7019167328534\n            ],\n            [\n              -75.52001953125,\n              35.22767235493586\n            ],\n            [\n              -76.00341796875,\n              35.10193405724606\n            ],\n            [\n              -76.519775390625,\n              34.6241677899049\n            ],\n            [\n              -76.783447265625,\n              34.66935854524543\n            ],\n            [\n              -77.2119140625,\n              34.58799745550482\n            ],\n            [\n              -77.596435546875,\n              34.37064492478658\n            ],\n            [\n              -77.87109375,\n              34.043556504127444\n            ],\n            [\n              -77.93701171875,\n              33.797408767572485\n            ],\n            [\n              -78.167724609375,\n              33.87041555094183\n            ],\n            [\n              -78.662109375,\n              33.8430453147447\n            ],\n            [\n              -79.013671875,\n              33.568861182555565\n            ],\n            [\n              -79.156494140625,\n              33.32134852669881\n            ],\n            [\n              -79.1455078125,\n              33.201924189778936\n            ],\n            [\n              -79.69482421875,\n              32.7872745269555\n            ],\n            [\n              -80.43090820312499,\n              32.47269502206151\n            ],\n            [\n              -80.44189453125,\n              32.32427558887655\n            ],\n            [\n              -80.83740234375,\n              32.0732655510424\n            ],\n            [\n              -81.265869140625,\n              31.42866311735861\n            ],\n            [\n              -81.419677734375,\n              30.760718908944472\n            ],\n            [\n              -81.34277343749999,\n              29.99300228455108\n            ],\n            [\n              -80.936279296875,\n              29.1233732108192\n            ],\n            [\n              -80.52978515625,\n              28.488005204159457\n            ],\n            [\n              -80.606689453125,\n              28.372068829631633\n            ],\n            [\n              -80.474853515625,\n              27.848790459862073\n            ],\n            [\n              -80.068359375,\n              26.95145308349826\n            ],\n            [\n              -80.079345703125,\n              26.2145910237943\n            ],\n            [\n              -80.167236328125,\n              25.37380917154398\n            ],\n            [\n              -80.79345703125,\n              24.87646991083154\n            ],\n            [\n              -80.9033203125,\n              24.856534339310674\n            ],\n            [\n              -81.177978515625,\n              25.21488107113259\n            ],\n            [\n              -81.2548828125,\n              25.54244147012483\n            ],\n            [\n              -81.551513671875,\n              25.878994400196202\n            ],\n            [\n              -81.67236328125,\n              25.86910939099931\n            ],\n            [\n              -81.89208984375,\n              26.441065564038418\n            ],\n            [\n              -82.11181640625,\n              26.43122806450644\n            ],\n            [\n              -82.298583984375,\n              26.833874515058554\n            ],\n            [\n              -82.63916015625,\n              27.371767300523047\n            ],\n            [\n              -82.869873046875,\n              27.89734922968426\n            ],\n            [\n              -82.72705078125,\n              28.391400375817753\n            ],\n            [\n              -82.7490234375,\n              29.046565622728846\n            ],\n            [\n              -83.023681640625,\n              29.180941290001776\n            ],\n            [\n              -83.8916015625,\n              30.0405664305846\n            ],\n            [\n              -84.3310546875,\n              30.097613277217132\n            ],\n            [\n              -84.320068359375,\n              29.907329376851553\n            ],\n            [\n              -85.05615234375,\n              29.592565403314087\n            ],\n            [\n              -85.36376953125,\n              29.66896252599253\n            ],\n            [\n              -86.02294921875,\n              30.29701788337205\n            ],\n            [\n              -87.03369140625,\n              30.372875188118016\n            ],\n            [\n              -88.17626953125,\n              30.221101852485987\n            ],\n            [\n              -89.27490234375,\n              30.240086360983426\n            ],\n            [\n              -89.31884765624999,\n              29.76437737516313\n            ],\n            [\n              -89.47265625,\n              29.554345125748267\n            ],\n            [\n              -88.9013671875,\n              29.267232865200878\n            ],\n            [\n              -89.27490234375,\n              28.8831596093235\n            ],\n            [\n              -89.69238281249999,\n              29.209713225868185\n            ],\n            [\n              -90.2197265625,\n              29.075375179558346\n            ],\n            [\n              -91.01074218749999,\n              29.0945770775118\n            ],\n            [\n              -91.4501953125,\n              29.34387539941801\n            ],\n            [\n              -92.46093749999999,\n              29.53522956294847\n            ],\n            [\n              -93.44970703125,\n              29.726222319395504\n            ],\n            [\n              -94.52636718749999,\n              29.477861195816843\n            ],\n            [\n              -95.2294921875,\n              29.05616970274342\n            ],\n            [\n              -96.064453125,\n              28.5941685062326\n            ],\n            [\n              -96.767578125,\n              28.16887518006332\n            ],\n            [\n              -97.27294921875,\n              27.68352808378776\n            ],\n            [\n              -97.3388671875,\n              26.980828590472107\n            ],\n            [\n              -97.18505859374999,\n              25.97779895546436\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","edition":"Version 1.0","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4ad9e4b07f02db685295","contributors":{"authors":[{"text":"Cress, Jill J.","contributorId":76832,"corporation":false,"usgs":true,"family":"Cress","given":"Jill J.","affiliations":[],"preferred":false,"id":303001,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sayre, Roger G. rsayre@usgs.gov","contributorId":2882,"corporation":false,"usgs":true,"family":"Sayre","given":"Roger","email":"rsayre@usgs.gov","middleInitial":"G.","affiliations":[],"preferred":false,"id":303000,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Comer, Patrick","contributorId":85683,"corporation":false,"usgs":true,"family":"Comer","given":"Patrick","affiliations":[],"preferred":false,"id":303002,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warner, Harumi hwarner@usgs.gov","contributorId":2881,"corporation":false,"usgs":true,"family":"Warner","given":"Harumi","email":"hwarner@usgs.gov","affiliations":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true}],"preferred":true,"id":302999,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":97738,"text":"ofr20091135 - 2009 - Magnetotelluric and audiomagnetotelluric groundwater survey along the Humu'ula portion of Saddle Road near and around the Pohakuloa Training Area, Hawaii","interactions":[],"lastModifiedDate":"2016-08-29T18:51:45","indexId":"ofr20091135","displayToPublicDate":"2009-08-11T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2009-1135","title":"Magnetotelluric and audiomagnetotelluric groundwater survey along the Humu'ula portion of Saddle Road near and around the Pohakuloa Training Area, Hawaii","docAbstract":"<p>The Pohakuloa Training Area (PTA), operated by the U.S. Army on the Big Island of Hawaii, is in need of a reliable potable water supply to sustain ongoing operations by staff and trainees. In an effort to acquire baseline hydrologic data with which to develop a plan for providing that water, a series of magnetotelluric (MT) geophysical surveys was performed that spanned the Mauna Loa/Mauna Kea Saddle region of Hawaii Island. These surveys provided electrical resistivity profiles and resistivity maps at several elevations along the axis of the field measurements that can be interpreted to yield information on the depth to the water table. In 2004 a preliminary sequence of 23 audiomagnetotelluric (AMT) soundings was collected along Saddle Road extending from the Waikii Ranch area, west of the PTA, to Department of Hawaiian Home Lands Humu'ula properties east of the Mauna Kea access road. The results of those soundings showed that highly resistive rocks, consistent with dry basalts, were present to depths of at least one kilometer, the maximum depth to which the AMT technique can reliably reach in Hawaii's rocks. A second survey was conducted in 2008 using MT instruments capable of recovering resistivity data to depths of several kilometers below sea level where saturated formations are known to exist. A total of 30 MT soundings was performed along a roughly east to west transect that extended from the (recently acquired) Keamuku PTA lands on the west to as far as the County of Hawaii's upper Kaumana water supply well to the east. Inversion and processing of the field data yielded an electrical cross-section following the Saddle that roughly parallels the geologic contact between the Mauna Kea and Mauna Loa lavas. Several additional electrical sections were constructed normal to the main transect to investigate the three-dimensional nature of the contact. These resistivity data and models suggest that the elevation of saturated rock in places are 400 to 600 meters above mean sea level beneath the surveyed region. Highest elevations for water-saturated zones based upon preferred electrical models are located between training area 3 and training area 6 southwest of training area 4.</p>","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20091135","usgsCitation":"Pierce, H., and Thomas, D., 2009, Magnetotelluric and audiomagnetotelluric groundwater survey along the Humu'ula portion of Saddle Road near and around the Pohakuloa Training Area, Hawaii: U.S. Geological Survey Open-File Report 2009-1135, iv, 160 p., https://doi.org/10.3133/ofr20091135.","productDescription":"iv, 160 p.","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[],"links":[{"id":118509,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2009_1135.jpg"},{"id":12903,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2009/1135/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Hawai'i","otherGeospatial":"Pohakuloa Training Area","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -155.6707763671875,\n              19.63870735832961\n            ],\n            [\n              -155.6707763671875,\n              19.811930193969296\n            ],\n            [\n              -155.14755249023438,\n              19.811930193969296\n            ],\n            [\n              -155.14755249023438,\n              19.63870735832961\n            ],\n            [\n              -155.6707763671875,\n              19.63870735832961\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a80e4b07f02db6493f0","contributors":{"authors":[{"text":"Pierce, Herbert A.","contributorId":83093,"corporation":false,"usgs":true,"family":"Pierce","given":"Herbert A.","affiliations":[],"preferred":false,"id":303011,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thomas, Donald M.","contributorId":89569,"corporation":false,"usgs":true,"family":"Thomas","given":"Donald M.","affiliations":[],"preferred":false,"id":303012,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":97744,"text":"ofr20091136 - 2009 - Estimating Casualties for Large Earthquakes Worldwide Using an Empirical Approach","interactions":[],"lastModifiedDate":"2012-02-02T00:14:31","indexId":"ofr20091136","displayToPublicDate":"2009-08-11T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2009-1136","title":"Estimating Casualties for Large Earthquakes Worldwide Using an Empirical Approach","docAbstract":"We developed an empirical country- and region-specific earthquake vulnerability model to be used as a candidate for post-earthquake fatality estimation by the U.S. Geological Survey's Prompt Assessment of Global Earthquakes for Response (PAGER) system. The earthquake fatality rate is based on past fatal earthquakes (earthquakes causing one or more deaths) in individual countries where at least four fatal earthquakes occurred during the catalog period (since 1973).\r\n\r\nBecause only a few dozen countries have experienced four or more fatal earthquakes since 1973, we propose a new global regionalization scheme based on idealization of countries that are expected to have similar susceptibility to future earthquake losses given the existing building stock, its vulnerability, and other socioeconomic characteristics.\r\n\r\nThe fatality estimates obtained using an empirical country- or region-specific model will be used along with other selected engineering risk-based loss models for generation of automated earthquake alerts. These alerts could potentially benefit the rapid-earthquake-response agencies and governments for better response to reduce earthquake fatalities. Fatality estimates are also useful to stimulate earthquake preparedness planning and disaster mitigation. \r\n\r\nThe proposed model has several advantages as compared with other candidate methods, and the country- or region-specific fatality rates can be readily updated when new data become available.","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20091136","usgsCitation":"Jaiswal, K., Wald, D.J., and Hearne, M., 2009, Estimating Casualties for Large Earthquakes Worldwide Using an Empirical Approach: U.S. Geological Survey Open-File Report 2009-1136, Report: vi, 78 p.; PAGER Implementation of Empirical Model (xls), https://doi.org/10.3133/ofr20091136.","productDescription":"Report: vi, 78 p.; PAGER Implementation of Empirical Model (xls)","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":301,"text":"Geologic Hazards Team","active":false,"usgs":true}],"links":[{"id":118510,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2009_1136.jpg"},{"id":12909,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2009/1136/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a0ce4b07f02db5fca0c","contributors":{"authors":[{"text":"Jaiswal, Kishor kjaiswal@usgs.gov","contributorId":861,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":false,"id":303024,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":303023,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hearne, Mike 0000-0002-8225-2396 mhearne@usgs.gov","orcid":"https://orcid.org/0000-0002-8225-2396","contributorId":4659,"corporation":false,"usgs":true,"family":"Hearne","given":"Mike","email":"mhearne@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":303025,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":97737,"text":"sim3086 - 2009 - Terrestrial Ecosystems - Topographic Moisture Potential of the Conterminous United States","interactions":[],"lastModifiedDate":"2012-02-10T00:11:54","indexId":"sim3086","displayToPublicDate":"2009-08-11T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3086","title":"Terrestrial Ecosystems - Topographic Moisture Potential of the Conterminous United States","docAbstract":"As part of an effort to map terrestrial ecosystems, the U.S. Geological Survey has generated topographic moisture potential classes to be used in creating maps depicting standardized, terrestrial ecosystem models for the conterminous United States, using an ecosystems classification developed by NatureServe. A biophysical stratification approach, developed for South America and now being implemented globally, was used to model the ecosystem distributions. Substrate moisture regimes strongly influence the differentiation and distribution of terrestrial ecosystems, and therefore topographic moisture potential is one of the key input layers in this biophysical stratification.\r\n\r\nThe method used to produce these topographic moisture potential classes was based on the derivation of ground moisture potential using a combination of computed topographic characteristics (CTI, slope, and aspect) and mapped National Wetland Inventory (NWI) boundaries. This method does not use climate or soil attributes to calculate relative topographic moisture potential since these characteristics are incorporated into the ecosystem model though other input layers. All of the topographic data used for this assessment were derived from the USGS 30-meter National Elevation Dataset (NED ) including the National Compound Topographic Index (CTI). The CTI index is a topographically derived measure of slope for a raster cell and the contributing area from upstream raster cells, and thus expresses potential for water flow to a point. In other words CTI data are 'a quantification of the position of a site in the local landscape', where the lowest values indicate ridges and the highest values indicate stream channels, lakes and ponds. These CTI values were compared to independent estimates of water accumulation by obtaining geospatial data from a number of sample locations representing two types of NWI boundaries: freshwater emergent wetlands and freshwater forested/shrub wetlands. Where these shorelines (the interface between the NWI wetlands and adjacent land) occurred, the CTI values were extracted and a histogram of their statistical distributions was calculated. Based on an evaluation of these histograms, CTI thresholds were developed to separate periodically saturated or flooded land, mesic uplands (moderately moist), and uplands. After the range of CTI values for these three different substrate moisture regimes was determined, the CTI values were grouped into three initial topographic moisture potential classes. As a final step in the generation of this national data layer, the uplands classification was subdivided into either very dry uplands or dry uplands. Very dry uplands were defined as uplands with relatively steep, south-facing slopes, and identification of this class was based on the slope and aspect datasets derived from the NED. The remaining uplands that did not meet these additional criteria were simply re-classified as dry uplands. The final National Topographic Moisture Potential dataset for the conterminous United States contains four classes: periodically saturated or flooded land (CTI = 18.5), mesic uplands (12 =< CTI < 18.5), dry uplands (CTI < 12), and very dry uplands (CTI < 12, Slope > 24 degrees and 91 degrees =< Aspect =< 314 degrees).\r\n\r\nThis map shows a smoothed and generalized image of the four topographic moisture potential classes. Additional information about this map and any of the data developed for the ecosystems modeling of the conterminous United States is available online at http://rmgsc.cr.usgs.gov/ecosystems/.","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/sim3086","collaboration":"Prepared in collaboration with NatureServe","usgsCitation":"Cress, J., Sayre, R.G., Comer, P., and Warner, H., 2009, Terrestrial Ecosystems - Topographic Moisture Potential of the Conterminous United States (Version 1.0): U.S. Geological Survey Scientific Investigations Map 3086, Sheet: 45 x 35 inches, https://doi.org/10.3133/sim3086.","productDescription":"Sheet: 45 x 35 inches","costCenters":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"links":[{"id":125538,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim_3086.jpg"},{"id":12902,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/3086/","linkFileType":{"id":5,"text":"html"}}],"scale":"5000000","projection":"Albers Equal Area Conic","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -125,23 ], [ -125,50 ], [ -65,50 ], [ -65,23 ], [ -125,23 ] ] ] } } ] }","edition":"Version 1.0","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4ad9e4b07f02db68529c","contributors":{"authors":[{"text":"Cress, Jill J.","contributorId":76832,"corporation":false,"usgs":true,"family":"Cress","given":"Jill J.","affiliations":[],"preferred":false,"id":303009,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sayre, Roger G. rsayre@usgs.gov","contributorId":2882,"corporation":false,"usgs":true,"family":"Sayre","given":"Roger","email":"rsayre@usgs.gov","middleInitial":"G.","affiliations":[],"preferred":false,"id":303008,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Comer, Patrick","contributorId":85683,"corporation":false,"usgs":true,"family":"Comer","given":"Patrick","affiliations":[],"preferred":false,"id":303010,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warner, Harumi hwarner@usgs.gov","contributorId":2881,"corporation":false,"usgs":true,"family":"Warner","given":"Harumi","email":"hwarner@usgs.gov","affiliations":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true}],"preferred":true,"id":303007,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":97734,"text":"sir20095141 - 2009 - Anisotropic Velocities of Gas Hydrate-Bearing Sediments in Fractured Reservoirs","interactions":[],"lastModifiedDate":"2012-02-10T00:11:48","indexId":"sir20095141","displayToPublicDate":"2009-08-07T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2009-5141","title":"Anisotropic Velocities of Gas Hydrate-Bearing Sediments in Fractured Reservoirs","docAbstract":"During the Indian National Gas Hydrate Program Expedition 01 (NGHP-01), one of the richest marine gas hydrate accumulations was discovered at drill site NGHP-01-10 in the Krishna-Godavari Basin, offshore of southeast India. The occurrence of concentrated gas hydrate at this site is primarily controlled by the presence of fractures. Gas hydrate saturations estimated from P- and S-wave velocities, assuming that gas hydrate-bearing sediments (GHBS) are isotropic, are much higher than those estimated from the pressure cores. To reconcile this difference, an anisotropic GHBS model is developed and applied to estimate gas hydrate saturations. Gas hydrate saturations estimated from the P-wave velocities, assuming high-angle fractures, agree well with saturations estimated from the cores. An anisotropic GHBS model assuming two-component laminated media - one component is fracture filled with 100-percent gas hydrate, and the other component is the isotropic water-saturated sediment - adequately predicts anisotropic velocities at the research site.","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/sir20095141","usgsCitation":"Lee, M.W., 2009, Anisotropic Velocities of Gas Hydrate-Bearing Sediments in Fractured Reservoirs: U.S. Geological Survey Scientific Investigations Report 2009-5141, iv, 13 p., https://doi.org/10.3133/sir20095141.","productDescription":"iv, 13 p.","onlineOnly":"Y","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":118668,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2009_5141.jpg"},{"id":12899,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2009/5141/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 65,5 ], [ 65,20 ], [ 100,20 ], [ 100,5 ], [ 65,5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4ac8e4b07f02db67c219","contributors":{"authors":[{"text":"Lee, Myung W. mlee@usgs.gov","contributorId":779,"corporation":false,"usgs":true,"family":"Lee","given":"Myung","email":"mlee@usgs.gov","middleInitial":"W.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":302998,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":97727,"text":"sir20095052 - 2009 - Evaluation of Real-Time Quantitative Polymerase Chain Reaction (qPCR) to Determine Escherichia coli Concentrations at Two Lake Erie Beaches","interactions":[],"lastModifiedDate":"2012-03-08T17:16:25","indexId":"sir20095052","displayToPublicDate":"2009-08-05T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2009-5052","title":"Evaluation of Real-Time Quantitative Polymerase Chain Reaction (qPCR) to Determine Escherichia coli Concentrations at Two Lake Erie Beaches","docAbstract":"During the recreational seasons of 2006 and 2007, the quantitative polymerase chain reaction (qPCR) method was used to determine Escherichia coli (E. coli) concentrations in samples from two Lake Erie beaches. Results from the qPCR method were compared to those obtained by traditional culturing on modified mTEC agar. Regression analysis showed strong, statistically significant correlations between results from the two methods for both years. Correlation coefficients at Edgewater and Villa Angela Beaches were 0.626 and 0.789 for 2006 and 0.667 and 0.829 for 2007, respectively. Linear regression analyses were done to determine how well E. coli concentrations could have been predicted from qPCR results. These hypothetical predictions were compared to the current practice of determining recreational water quality from E. coli concentrations determined for samples collected on the previous day. The qPCR method resulted in a greater percentage of correct predictions of water-quality exceedances than the current method for both beaches and both years. However, because regression equations differed somewhat between both sites and both years, the study did not result in any single relation reliable enough to use for actual real-time prediction of water-quality exceedances for either beach; therefore, a posterior analysis of data was done. Additional years of data may be needed to develop such a relation. Results from this study support the continued development and testing of a qPCR method for providing rapid and accurate estimates of E. coli concentrations for monitoring recreational water quality.","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/sir20095052","collaboration":"Prepared in cooperation with the Northeast Ohio Regional Sewer District","usgsCitation":"Kephart, C.M., and Bushon, R.N., 2009, Evaluation of Real-Time Quantitative Polymerase Chain Reaction (qPCR) to Determine Escherichia coli Concentrations at Two Lake Erie Beaches: U.S. Geological Survey Scientific Investigations Report 2009-5052, iv, 14 p., https://doi.org/10.3133/sir20095052.","productDescription":"iv, 14 p.","temporalStart":"2006-01-01","temporalEnd":"2007-12-31","costCenters":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"links":[{"id":125589,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2009_5052.jpg"},{"id":12892,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2009/5052/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -81.83333333333333,41.416666666666664 ], [ -81.83333333333333,41.666666666666664 ], [ -81.5,41.666666666666664 ], [ -81.5,41.416666666666664 ], [ -81.83333333333333,41.416666666666664 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a07e4b07f02db5f9aff","contributors":{"authors":[{"text":"Kephart, Christopher M. 0000-0002-3369-5596 ckephart@usgs.gov","orcid":"https://orcid.org/0000-0002-3369-5596","contributorId":1932,"corporation":false,"usgs":true,"family":"Kephart","given":"Christopher","email":"ckephart@usgs.gov","middleInitial":"M.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":302985,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bushon, Rebecca N. rnbushon@usgs.gov","contributorId":2304,"corporation":false,"usgs":true,"family":"Bushon","given":"Rebecca","email":"rnbushon@usgs.gov","middleInitial":"N.","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"preferred":true,"id":302986,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":97722,"text":"ofr20091122 - 2009 - Update of Watershed Regressions for Pesticides (WARP) for Predicting Atrazine Concentration in Streams","interactions":[],"lastModifiedDate":"2012-02-02T00:15:03","indexId":"ofr20091122","displayToPublicDate":"2009-07-31T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2009-1122","title":"Update of Watershed Regressions for Pesticides (WARP) for Predicting Atrazine Concentration in Streams","docAbstract":"Regression models for predicting atrazine concentrations in streams were updated by incorporating refined annual atrazine-use estimates and by adding an explanatory variable representing annual precipitation characteristics. The updated Watershed Regressions for Pesticides (WARP) models enable improved predictions of specific pesticide-concentration statistics for unmonitored streams. \r\n\r\nfor unmonitored streams. Separate WARP regression models were derived for selected percentiles (5th, 10th, 15th, 25th, 50th, 75th, 85th, 90th and 95th), annual mean, annual maximum, and annual maximum moving-average (21-, 60-, and 90-day durations) concentration statistics. Development of the regression models involved the same model-development data, model-validation data, and regression methods as those used in the original development of WARP. The original WARP models were based on atrazine-use estimates from either 1992 or 1997. This update of the WARP models incorporates annual atrazine-use estimates. In addition, annual precipitation data were evaluated as potential explanatory variables.\r\n\r\nas potential explanatory variables. The updated WARP models include the same five explanatory variables and transformations that were used in the original WARP models, including the new annual atrazine-use data. The models also include a sixth explanatory variable, total precipitation during May and June of the year of sampling. The updated WARP models account for as much as 82 percent of the variability in the concentration statistics among the 112 sites used for model development, whereas previous WARP models accounted for no more than 77 percent. Concentration statistics predicted by the 95th percentile, annual mean, annual maximum and annual maximum moving-average concentration models were within a factor of 10 of the observed concentration statistics for most of the model development and validation sites.\r\n\r\nOverall, performance of the models for the development and validation sites supports the application of the WARP models for predicting atrazine-concentration statistics in streams and provides a framework to interpret the predictions in terms of uncertainty. For streams where direct measurements of atrazine are lacking, the updated WARP model predictions can be used to characterize the probable values of atrazine-concentration statistics for comparison to specific water-quality benchmarks.","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20091122","usgsCitation":"Stone, W.W., and Gilliom, R.J., 2009, Update of Watershed Regressions for Pesticides (WARP) for Predicting Atrazine Concentration in Streams: U.S. Geological Survey Open-File Report 2009-1122, viii, 22 p., https://doi.org/10.3133/ofr20091122.","productDescription":"viii, 22 p.","costCenters":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"links":[{"id":125464,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2009_1122.jpg"},{"id":12888,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2009/1122/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e48cee4b07f02db5459ad","contributors":{"authors":[{"text":"Stone, Wesley W. 0000-0003-0239-2063 wwstone@usgs.gov","orcid":"https://orcid.org/0000-0003-0239-2063","contributorId":1496,"corporation":false,"usgs":true,"family":"Stone","given":"Wesley","email":"wwstone@usgs.gov","middleInitial":"W.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":302973,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gilliom, Robert J. rgilliom@usgs.gov","contributorId":488,"corporation":false,"usgs":true,"family":"Gilliom","given":"Robert","email":"rgilliom@usgs.gov","middleInitial":"J.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":302972,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70199513,"text":"70199513 - 2009 - Peat accretion histories during the past 6,000 years in the marshes of the Sacramento-San Joaquin Delta, CA, USA","interactions":[],"lastModifiedDate":"2018-09-19T16:42:25","indexId":"70199513","displayToPublicDate":"2009-07-30T16:41:58","publicationYear":"2009","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Peat accretion histories during the past 6,000 years in the marshes of the Sacramento-San Joaquin Delta, CA, USA","docAbstract":"<p><span>The purpose of this study was to determine how vertical accretion rates in marshes vary through the millennia. Peat cores were collected in remnant and drained marshes in the Sacramento–San Joaquin Delta of California. Cubic smooth spline regression models were used to construct age–depth models and accretion histories for three remnant marshes. Estimated vertical accretion rates at these sites range from 0.03 to 0.49&nbsp;cm&nbsp;year</span><sup>−1</sup><span>. The mean contribution of organic matter to soil volume at the remnant marsh sites is generally stable (4.73% to 6.94%), whereas the mean contribution of inorganic matter to soil volume has greater temporal variability (1.40% to 7.92%). The hydrogeomorphic position of each marsh largely determines the inorganic content of peat. Currently, the remnant marshes are keeping pace with sea level rise, but this balance may shift for at least one of the sites under future sea level rise scenarios.</span></p>","doi":"10.1007/s12237-009-9202-8","usgsCitation":"Drexler, J.Z., de Fontaine, C.S., and Brown, T., 2009, Peat accretion histories during the past 6,000 years in the marshes of the Sacramento-San Joaquin Delta, CA, USA: Estuaries and Coasts, v. 32, no. 5, p. 871-892, https://doi.org/10.1007/s12237-009-9202-8.","productDescription":"22 p.","startPage":"871","endPage":"892","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":357522,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta","volume":"32","issue":"5","noUsgsAuthors":false,"publicationDate":"2009-07-30","publicationStatus":"PW","scienceBaseUri":"5c10cbd4e4b034bf6a7f7efb","contributors":{"authors":[{"text":"Drexler, Judith Z. 0000-0002-0127-3866 jdrexler@usgs.gov","orcid":"https://orcid.org/0000-0002-0127-3866","contributorId":167492,"corporation":false,"usgs":true,"family":"Drexler","given":"Judith","email":"jdrexler@usgs.gov","middleInitial":"Z.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":745649,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"de Fontaine, Christian S.","contributorId":140339,"corporation":false,"usgs":false,"family":"de Fontaine","given":"Christian","email":"","middleInitial":"S.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":745650,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Thomas A.","contributorId":52817,"corporation":false,"usgs":true,"family":"Brown","given":"Thomas A.","affiliations":[],"preferred":false,"id":745651,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":97718,"text":"fs20093052 - 2009 - Everglades Depth Estimation Network (EDEN) Applications: Tools to View, Extract, Plot, and Manipulate EDEN Data","interactions":[],"lastModifiedDate":"2012-02-02T00:14:28","indexId":"fs20093052","displayToPublicDate":"2009-07-29T00:00:00","publicationYear":"2009","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":"2009-3052","title":"Everglades Depth Estimation Network (EDEN) Applications: Tools to View, Extract, Plot, and Manipulate EDEN Data","docAbstract":"The Everglades Depth Estimation Network (EDEN) is an integrated system of real-time water-level monitoring, ground-elevation data, and water-surface elevation modeling to provide scientists and water managers with current on-line water-depth information for the entire freshwater part of the greater Everglades. To assist users in applying the EDEN data to their particular needs, a series of five EDEN tools, or applications (EDENapps), were developed. Using EDEN's tools, scientists can view the EDEN datasets of daily water-level and ground elevations, compute and view daily water depth and hydroperiod surfaces, extract data for user-specified locations, plot transects of water level, and animate water-level transects over time. Also, users can retrieve data from the EDEN datasets for analysis and display in other analysis software programs. As scientists and managers attempt to restore the natural volume, timing, and distribution of sheetflow in the wetlands, such information is invaluable. Information analyzed and presented with these tools is used to advise policy makers, planners, and decision makers of the potential effects of water management and restoration scenarios on the natural resources of the Everglades.","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/fs20093052","usgsCitation":"Telis, P.A., and Henkel, H., 2009, Everglades Depth Estimation Network (EDEN) Applications: Tools to View, Extract, Plot, and Manipulate EDEN Data: U.S. Geological Survey Fact Sheet 2009-3052, 4 p., https://doi.org/10.3133/fs20093052.","productDescription":"4 p.","costCenters":[{"id":275,"text":"Florida Integrated Science Center","active":false,"usgs":true}],"links":[{"id":125408,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_2009_3052.jpg"},{"id":12884,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2009/3052/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e49e5e4b07f02db5e6e34","contributors":{"authors":[{"text":"Telis, Pamela A. patelis@usgs.gov","contributorId":64741,"corporation":false,"usgs":true,"family":"Telis","given":"Pamela","email":"patelis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":false,"id":302965,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Henkel, Heather","contributorId":101759,"corporation":false,"usgs":true,"family":"Henkel","given":"Heather","affiliations":[],"preferred":false,"id":302966,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":97714,"text":"sir20095094 - 2009 - Simulation of the Regional Ground-Water-Flow System and Ground-Water/Surface-Water Interaction in the Rock River Basin, Wisconsin","interactions":[],"lastModifiedDate":"2012-03-08T17:16:25","indexId":"sir20095094","displayToPublicDate":"2009-07-28T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2009-5094","title":"Simulation of the Regional Ground-Water-Flow System and Ground-Water/Surface-Water Interaction in the Rock River Basin, Wisconsin","docAbstract":"A regional, two-dimensional, areal ground-water-flow model was developed to simulate the ground-water-flow system and ground-water/surface-water interaction in the Rock River Basin. The model was developed by the U.S. Geological Survey (USGS), in cooperation with the Rock River Coalition. The objectives of the regional model were to improve understanding of the ground-water-flow system and to develop a tool suitable for evaluating the effects of potential regional water-management programs. The computer code GFLOW was used because of the ease with which the model can simulate ground-water/surface-water interactions, provide a framework for simulating regional ground-water-flow systems, and be refined in a stepwise fashion to incorporate new data and simulate ground-water-flow patterns at multiple scales.\r\n\r\nThe ground-water-flow model described in this report simulates the major hydrogeologic features of the modeled area, including bedrock and surficial aquifers, ground-water/surface-water interactions, and ground-water withdrawals from high-capacity wells. The steady-state model treats the ground-water-flow system as a single layer with hydraulic conductivity and base elevation zones that reflect the distribution of lithologic groups above the Precambrian bedrock and a regionally significant confining unit, the Maquoketa Formation. In the eastern part of the Basin where the shale-rich Maquoketa Formation is present, deep ground-water flow in the sandstone aquifer below the Maquoketa Formation was not simulated directly, but flow into this aquifer was incorporated into the GFLOW model from previous work in southeastern Wisconsin. Recharge was constrained primarily by stream base-flow estimates and was applied uniformly within zones guided by regional infiltration estimates for soils. The model includes average ground-water withdrawals from 1997 to 2006 for municipal wells and from 1997 to 2005 for high-capacity irrigation, industrial, and commercial wells. In addition, the model routes tributary base flow through the river network to the Rock River. The parameter-estimation code PEST was linked to the GFLOW model to select the combination of parameter values best able to match more than 8,000 water-level measurements and base-flow estimates at 9 streamgages.\r\n\r\nResults from the calibrated GFLOW model show simulated (1) ground-water-flow directions, (2) ground-water/surface-water interactions, as depicted in a map of gaining and losing river and lake sections, (3) ground-water contributing areas for selected tributary rivers, and (4) areas of relatively local ground water captured by rivers. Ground-water flow patterns are controlled primarily by river geometries, with most river sections gaining water from the ground-water-flow system; losing sections are most common on the downgradient shore of lakes and reservoirs or near major pumping centers. Ground-water contributing areas to tributary rivers generally coincide with surface watersheds; however the locations of ground-water divides are controlled by the water table, whereas surface-water divides are controlled by surface topography. Finally, areas of relatively local ground water captured by rivers generally extend upgradient from rivers but are modified by the regional flow pattern, such that these areas tend to shift toward regional ground-water divides for relatively small rivers.\r\n\r\nIt is important to recognize the limitations of this regional-scale model. Heterogeneities in subsurface properties and in recharge rates are considered only at a very broad scale (miles to tens of miles). No account is taken of vertical variations in properties or pumping rates, and no provision is made to account for stacked ground-water-flow systems that have different flow patterns at different depths. Small-scale flow systems (hundreds to thousands of feet) associated with minor water bodies are not considered; as a result, the model is not currently designed for simulating site-specifi","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/sir20095094","collaboration":"Prepared in cooperation with the Rock River Coalition","usgsCitation":"Juckem, P.F., 2009, Simulation of the Regional Ground-Water-Flow System and Ground-Water/Surface-Water Interaction in the Rock River Basin, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2009-5094, Report: vi, 39 p.; 5 Appendixes (xls & csv), https://doi.org/10.3133/sir20095094.","productDescription":"Report: vi, 39 p.; 5 Appendixes (xls & csv)","additionalOnlineFiles":"Y","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":125596,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2009_5094.jpg"},{"id":12880,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2009/5094/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -89.75,42.25 ], [ -89.75,44 ], [ -88,44 ], [ -88,42.25 ], [ -89.75,42.25 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e49f7e4b07f02db5f2197","contributors":{"authors":[{"text":"Juckem, Paul F. 0000-0002-3613-1761 pfjuckem@usgs.gov","orcid":"https://orcid.org/0000-0002-3613-1761","contributorId":1905,"corporation":false,"usgs":true,"family":"Juckem","given":"Paul","email":"pfjuckem@usgs.gov","middleInitial":"F.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":302956,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":97711,"text":"sir20095130 - 2009 - Modeling Flood Plain Hydrology and Forest Productivity of Congaree Swamp, South Carolina","interactions":[],"lastModifiedDate":"2017-01-17T10:18:17","indexId":"sir20095130","displayToPublicDate":"2009-07-25T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2009-5130","title":"Modeling Flood Plain Hydrology and Forest Productivity of Congaree Swamp, South Carolina","docAbstract":"An ecological field and modeling study was conducted to examine the flood relations of backswamp forests and park trails of the flood plain portion of Congaree National Park, S.C. Continuous water level gages were distributed across the length and width of the flood plain portion - referred to as 'Congaree Swamp' - to facilitate understanding of the lag and peak flood coupling with stage of the Congaree River. A severe and prolonged drought at study start in 2001 extended into late 2002 before backswamp zones circulated floodwaters. Water levels were monitored at 10 gaging stations over a 4-year period from 2002 to 2006. Historical water level stage and discharge data from the Congaree River were digitized from published sources and U.S. Geological Survey (USGS) archives to obtain long-term daily averages for an upstream gage at Columbia, S.C., dating back to 1892. Elevation of ground surface was surveyed for all park trails, water level gages, and additional circuits of roads and boundaries. Rectified elevation data were interpolated into a digital elevation model of the park trail system. Regression models were applied to establish time lags and stage relations between gages at Columbia, S.C., and gages in the upper, middle, and lower reaches of the river and backswamp within the park. Flood relations among backswamp gages exhibited different retention and recession behavior between flood plain reaches with greater hydroperiod in the lower reach than those in the upper and middle reaches of the Congaree Swamp. A flood plain inundation model was developed from gage relations to predict critical river stages and potential inundation of hiking trails on a real-time basis and to forecast the 24-hour flood \r\n\r\nIn addition, tree-ring analysis was used to evaluate the effects of flood events and flooding history on forest resources at Congaree National Park. Tree cores were collected from populations of loblolly pine (Pinus taeda), baldcypress (Taxodium distichum), water tupelo (Nyssa aquatica), green ash (Fraxinus pennslyvanica), laurel oak (Quercus laurifolia), swamp chestnut oak (Quercus michauxii), and sycamore (Plantanus occidentalis) within Congaree Swamp in highand low-elevation sites characteristic of shorter and longer flood duration and related to upriver flood controls and dam operation. Ring counts and dating indicated that all loblolly pine trees and nearly all baldcypress collections in this study are postsettlement recruits and old-growth cohorts, dating from 100 to 300 years in age. Most hardwood species and trees cored for age analysis were less than 100 years old, demonstrating robust growth and high site quality. Growth chronologies of loblolly pine and baldcypress exhibited positive and negative inflections over the last century that corresponded with climate history and residual effects of Hurricane Hugo in 1989. Stemwood production on average was less for trees and species on sites with longer flood retention and hydroperiod affected more by groundwater seepage and site elevation than river floods. Water level data provided evidence that stream regulation and operations of the Saluda Dam (post-1934) have actually increased the average daily water stage in the Congaree River. There was no difference in tree growth response by species or hydrogeomorphic setting to predam and postdam flood conditions and river stage. Climate-growth analysis showed that long-term growth variation is controlled more by spring/ summer temperatures in loblolly pine and by spring/summer precipitation in baldcypress than flooding history.","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/sir20095130","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Doyle, T.W., 2009, Modeling Flood Plain Hydrology and Forest Productivity of Congaree Swamp, South Carolina: U.S. Geological Survey Scientific Investigations Report 2009-5130, vi, 46 p., https://doi.org/10.3133/sir20095130.","productDescription":"vi, 46 p.","onlineOnly":"Y","temporalStart":"2002-01-01","temporalEnd":"2006-12-31","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":195020,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":12865,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2009/5130/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"South Carolina","otherGeospatial":"Congaree Swamp","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -81.2109375,\n              33.51391942394942\n            ],\n            [\n              -81.2109375,\n              34.064036693555465\n            ],\n            [\n              -80.277099609375,\n              34.064036693555465\n            ],\n            [\n              -80.277099609375,\n              33.51391942394942\n            ],\n            [\n              -81.2109375,\n              33.51391942394942\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b05e4b07f02db699a3b","contributors":{"authors":[{"text":"Doyle, Thomas W. 0000-0001-5754-0671 doylet@usgs.gov","orcid":"https://orcid.org/0000-0001-5754-0671","contributorId":703,"corporation":false,"usgs":true,"family":"Doyle","given":"Thomas","email":"doylet@usgs.gov","middleInitial":"W.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":302952,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":97710,"text":"tm11C3 - 2009 - Estimating Prediction Uncertainty from Geographical Information System Raster Processing: A User's Manual for the Raster Error Propagation Tool (REPTool)","interactions":[],"lastModifiedDate":"2012-03-02T17:16:07","indexId":"tm11C3","displayToPublicDate":"2009-07-25T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"11-C3","title":"Estimating Prediction Uncertainty from Geographical Information System Raster Processing: A User's Manual for the Raster Error Propagation Tool (REPTool)","docAbstract":"The U.S. Geological Survey Raster Error Propagation Tool (REPTool) is a custom tool for use with the Environmental System Research Institute (ESRI) ArcGIS Desktop application to estimate error propagation and prediction uncertainty in raster processing operations and geospatial modeling. REPTool is designed to introduce concepts of error and uncertainty in geospatial data and modeling and provide users of ArcGIS Desktop a geoprocessing tool and methodology to consider how error affects geospatial model output. Similar to other geoprocessing tools available in ArcGIS Desktop, REPTool can be run from a dialog window, from the ArcMap command line, or from a Python script.\r\n\r\nREPTool consists of public-domain, Python-based packages that implement Latin Hypercube Sampling within a probabilistic framework to track error propagation in geospatial models and quantitatively estimate the uncertainty of the model output. Users may specify error for each input raster or model coefficient represented in the geospatial model. The error for the input rasters may be specified as either spatially invariant or spatially variable across the spatial domain. Users may specify model output as a distribution of uncertainty for each raster cell. REPTool uses the Relative Variance Contribution method to quantify the relative error contribution from the two primary components in the geospatial model - errors in the model input data and coefficients of the model variables.\r\n\r\nREPTool is appropriate for many types of geospatial processing operations, modeling applications, and related research questions, including applications that consider spatially invariant or spatially variable error in geospatial data.","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/tm11C3","isbn":"9781411324305","usgsCitation":"Gurdak, J., Qi, S.L., and Geisler, M.L., 2009, Estimating Prediction Uncertainty from Geographical Information System Raster Processing: A User's Manual for the Raster Error Propagation Tool (REPTool): U.S. Geological Survey Techniques and Methods 11-C3, viii, 71 p., https://doi.org/10.3133/tm11C3.","productDescription":"viii, 71 p.","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":118579,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/tm_11_c3.gif"},{"id":12864,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/tm/11c3/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a0ce4b07f02db5fc9a8","contributors":{"authors":[{"text":"Gurdak, Jason J.","contributorId":65125,"corporation":false,"usgs":true,"family":"Gurdak","given":"Jason J.","affiliations":[],"preferred":false,"id":302951,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Qi, Sharon L. 0000-0001-7278-4498 slqi@usgs.gov","orcid":"https://orcid.org/0000-0001-7278-4498","contributorId":1130,"corporation":false,"usgs":true,"family":"Qi","given":"Sharon","email":"slqi@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":302949,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Geisler, Michael L.","contributorId":15727,"corporation":false,"usgs":true,"family":"Geisler","given":"Michael","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":302950,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":97699,"text":"ofr20091137 - 2009 - Quaternary Geologic Framework of the St. Clair River between Michigan and Ontario, Canada","interactions":[],"lastModifiedDate":"2012-02-10T00:11:51","indexId":"ofr20091137","displayToPublicDate":"2009-07-21T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2009-1137","title":"Quaternary Geologic Framework of the St. Clair River between Michigan and Ontario, Canada","docAbstract":"Concern about the effect of geomorphic changes in the St. Clair River on water levels in the Upper Great Lakes resulted in the need for information on the geologic framework of the river. A geophysical survey of the Upper St. Clair River between Port Huron, MI, and Sarnia, Ontario, Canada, was conducted to determine the Quaternary geologic framework of the region. Previously available and new sediment samples and photographic and video data support the interpretation of the seismic stratigraphy and surficial geology. Three seismic stratigraphic units and two unconformities were identified. Glacial drift, consisting of interbedded till and glaciolacustrine deposits, overlies shale. Glaciofluvial and modern fluvial processes have eroded the glacial drift. Glaciofluvial, glaciolacustrine, fluvial, and lacustrine deposits overlie this unconformity. Seismic facies were interpreted to identify areas where these geologic facies exist; however, in the absence of distinct boundaries between facies, these deposits were mapped as one undifferentiated unit. This unit is thickest in the northernmost 3 kilometers of the river, where it consists of relatively coarse-grained fluvial, reworked glaciofluvial, and possibly glaciofluvial deposits. To the south, this coarse-grained unit thins or is absent. The undifferentiated unit comprises most of the surficial deposits in the northernmost river area. Some areas of glacial drift, predominantly till, are exposed at the lake and riverbed. The shale is not exposed anywhere in the region. Geophysical surveys at sites downriver, together with the results of previous studies, indicate that the geologic framework is similar to that in the northernmost river area except for the absence or reduced thickness of the coarse-grained fluvial deposits. Instead, glacial drift is exposed at the riverbed or is covered by a veneer of sediment. This information on the substrate is important for ongoing sediment transport studies.","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20091137","collaboration":"Prepared in cooperation with the USACE as a component of the IUGLS","usgsCitation":"Foster, D.S., and Denny, J.F., 2009, Quaternary Geologic Framework of the St. Clair River between Michigan and Ontario, Canada: U.S. Geological Survey Open-File Report 2009-1137, Available Online Only, https://doi.org/10.3133/ofr20091137.","productDescription":"Available Online Only","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2008-05-29","temporalEnd":"2008-06-04","costCenters":[{"id":680,"text":"Woods Hole Science Center","active":false,"usgs":true}],"links":[{"id":118511,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2009_1137.jpg"},{"id":12854,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2009/1137/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -82.83333333333333,42.5 ], [ -82.83333333333333,43.166666666666664 ], [ -82.25,43.166666666666664 ], [ -82.25,42.5 ], [ -82.83333333333333,42.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4adbe4b07f02db685c5d","contributors":{"authors":[{"text":"Foster, David S. 0000-0003-1205-0884 dfoster@usgs.gov","orcid":"https://orcid.org/0000-0003-1205-0884","contributorId":1320,"corporation":false,"usgs":true,"family":"Foster","given":"David","email":"dfoster@usgs.gov","middleInitial":"S.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":302927,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Denny, Jane F. 0000-0002-3472-618X jdenny@usgs.gov","orcid":"https://orcid.org/0000-0002-3472-618X","contributorId":418,"corporation":false,"usgs":true,"family":"Denny","given":"Jane","email":"jdenny@usgs.gov","middleInitial":"F.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":302926,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
]}