The Curiosity rover's campaign at Pahrump Hills provides the first analyses of lower Mount Sharp strata. Here we report ChemCam elemental composition of a diverse assemblage of post-depositional features embedded in, or cross-cutting, the host rock. ChemCam results demonstrate their compositional diversity, especially compared to the surrounding host rock: (i) Dendritic aggregates and relief enhanced features, characterized by a magnesium enhancement and sulfur detection, and interpreted as Mg-sulfates; (ii) A localized observation that displays iron enrichment associated with sulfur, interpreted as Fe-sulfate; (iii) Dark raised ridges with varying Mg- and Ca-enriched compositions compared to host rock; (iv) Several dark-toned veins with calcium enhancement associated with fluorine detection, interpreted as fluorite veins. (v) Light-toned veins with enhanced calcium associated with sulfur detection, and interpreted as Ca-sulfates. The diversity of the Pahrump Hills diagenetic assemblage suggests a complex post-depositional history for fine-grained sediments for which the origin has been interpreted as fluvial and lacustrine. Assessment of the spatial and relative temporal distribution of these features shows that the Mg-sulfate features are predominant in the lower part of the section, suggesting local modification of the sediments by early diagenetic fluids. In contrast, light-toned Ca-sulfate veins occur in the whole section and cross-cut all other features. A relatively late stage shift in geochemical conditions could explain this observation. The Pahrump Hills diagenetic features have no equivalent compared to targets analyzed in other locations at Gale crater. Only the light-toned Ca-sulfate veins are present elsewhere, along Curiosity's path, suggesting they formed through a common late-stage process that occurred at over a broad area.
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The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response of an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “sub-model” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. The sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.sab.2016.12.002","usgsCitation":"Anderson, R.B., Clegg, S.M., Frydenvang, J., Wiens, R.C., McLennan, S.M., Morris, R., Ehlmann, B.L., and Dyar, M., 2017, Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models: Spectrochimica Acta Part B: Atomic Spectroscopy, v. 129, p. 49-57, https://doi.org/10.1016/j.sab.2016.12.002.","productDescription":"9 p.","startPage":"49","endPage":"57","ipdsId":"IP-070366","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":470074,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1693629","text":"Publisher Index Page"},{"id":335233,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"129","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58a2d3afe4b0c825128699ed","contributors":{"authors":[{"text":"Anderson, Ryan B. 0000-0003-4465-2871 rbanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-4465-2871","contributorId":170054,"corporation":false,"usgs":true,"family":"Anderson","given":"Ryan","email":"rbanderson@usgs.gov","middleInitial":"B.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":668374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clegg, Samuel M.","contributorId":23460,"corporation":false,"usgs":false,"family":"Clegg","given":"Samuel","email":"","middleInitial":"M.","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":668413,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frydenvang, Jens","contributorId":173225,"corporation":false,"usgs":false,"family":"Frydenvang","given":"Jens","email":"","affiliations":[{"id":27196,"text":"LANL","active":true,"usgs":false}],"preferred":false,"id":668414,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wiens, Roger C.","contributorId":140330,"corporation":false,"usgs":false,"family":"Wiens","given":"Roger","email":"","middleInitial":"C.","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":668415,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McLennan, Scott M.","contributorId":95388,"corporation":false,"usgs":true,"family":"McLennan","given":"Scott","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":668416,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Morris, Richard V.","contributorId":167513,"corporation":false,"usgs":false,"family":"Morris","given":"Richard V.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":668417,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ehlmann, Bethany L. 0000-0002-2745-3240","orcid":"https://orcid.org/0000-0002-2745-3240","contributorId":147154,"corporation":false,"usgs":false,"family":"Ehlmann","given":"Bethany","email":"","middleInitial":"L.","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":668418,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dyar, M. 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Each individual in a group likely has different measures of the costs and benefits associated with a group task, such as an aggressive attack on another group, which can alter motivation and behavior. We observed 292 inter-pack aggressive interactions in Yellowstone National Park between 1 April 1995 and 1 April 2011 (>5300 days of observation) in order to determine the role of both sexes, and the influence of pack, age, and other traits on aggression. We recorded the behaviors and characteristics of all individuals present during the interactions (n = 534 individuals) and which individuals participated in each step (i.e. chase, attack, kill, flight) of the interaction. Overall, all wolves were more likely to chase rivals if they outnumbered their opponent, suggesting packs accurately assess their opponent’s size during encounters and individuals adjust their behavior based on relative pack size. Males were more likely than females to chase rival packs and gray-colored wolves were more aggressive than black-colored wolves. Male wolves and gray-colored wolves also recorded higher cortisol levels than females and black-colored wolves, indicating hormonal support for more intense aggressive behavior. Further, we found a positive correlation between male age and probability of chasing, while age-specific participation for females remained constant. Chasing behavior was influenced by the sex of lone intruders, with males more likely to chase male intruders. This difference in behavior suggests male and female wolves may have different strategies and motivations during inter-pack aggressive interactions related to gray wolf mating systems. A division of labor between pack members concerning resource and territory defense suggests selection for specific traits related to aggression is an adaptive response to intense competition between groups of conspecifics.
","language":"English","publisher":"Elsevier B.V.","doi":"10.1016/j.beproc.2017.01.011","usgsCitation":"Cassidy, K.A., Mech, L.D., MacNulty, D., Stahler, D.R., and Smith, D., 2017, Sexually dimorphic aggression indicates male gray wolves specialize in pack defense against conspecific groups: Behavioural Processes, v. 136, p. 64-72, https://doi.org/10.1016/j.beproc.2017.01.011.","productDescription":"9 p.","startPage":"64","endPage":"72","ipdsId":"IP-074240","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":335191,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"136","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"589ffecce4b099f50d3e0428","contributors":{"authors":[{"text":"Cassidy, Kira A.","contributorId":145492,"corporation":false,"usgs":false,"family":"Cassidy","given":"Kira","email":"","middleInitial":"A.","affiliations":[{"id":16134,"text":"Yellowstone Wolf Project, Yellowstone Ctr for Resources","active":true,"usgs":false}],"preferred":false,"id":663050,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mech, L. David 0000-0003-3944-7769 david_mech@usgs.gov","orcid":"https://orcid.org/0000-0003-3944-7769","contributorId":2518,"corporation":false,"usgs":true,"family":"Mech","given":"L.","email":"david_mech@usgs.gov","middleInitial":"David","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":663049,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"MacNulty, Daniel R.","contributorId":179179,"corporation":false,"usgs":false,"family":"MacNulty","given":"Daniel R.","affiliations":[],"preferred":false,"id":663051,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stahler, Daniel R.","contributorId":179180,"corporation":false,"usgs":false,"family":"Stahler","given":"Daniel","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":663052,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Douglas W.","contributorId":179181,"corporation":false,"usgs":false,"family":"Smith","given":"Douglas W.","affiliations":[],"preferred":false,"id":663053,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70181008,"text":"70181008 - 2017 - Complete genome sequences of two acetylene-fermenting Pelobacter acetylenicus strains","interactions":[],"lastModifiedDate":"2017-02-11T17:52:11","indexId":"70181008","displayToPublicDate":"2017-02-11T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5099,"text":"Genome Announcements","active":true,"publicationSubtype":{"id":10}},"title":"Complete genome sequences of two acetylene-fermenting Pelobacter acetylenicus strains","docAbstract":"Acetylene fermentation is a rare metabolism that was serendipitously discovered during C2H2-block assays of N2O reductase. Here, we report the genome sequences of two type strains of acetylene-fermenting Pelobacter acetylenicus, the freshwater bacterium DSM 3246 and the estuarine bacterium DSM 3247.
","language":"English","publisher":"American Society for Microbiology","doi":"10.1128/genomeA.01572-16","usgsCitation":"Sutton, J.M., Baesman, S., Fierst, J.L., Poret-Peterson, A.T., Oremland, R.S., Dunlap, D., and Akob, D.M., 2017, Complete genome sequences of two acetylene-fermenting Pelobacter acetylenicus strains: Genome Announcements, v. 5, no. 6, e01572-16; 2 p., https://doi.org/10.1128/genomeA.01572-16.","productDescription":"e01572-16; 2 p.","ipdsId":"IP-081646","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":470076,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1128/genomea.01572-16","text":"Publisher Index Page"},{"id":335178,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"589ffecce4b099f50d3e0426","contributors":{"authors":[{"text":"Sutton, John M.","contributorId":179294,"corporation":false,"usgs":false,"family":"Sutton","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":663245,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baesman, Shaun 0000-0003-0741-8269 sbaesman@usgs.gov","orcid":"https://orcid.org/0000-0003-0741-8269","contributorId":3478,"corporation":false,"usgs":true,"family":"Baesman","given":"Shaun","email":"sbaesman@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":663244,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fierst, Janna L.","contributorId":179295,"corporation":false,"usgs":false,"family":"Fierst","given":"Janna","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":663246,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Poret-Peterson, Amisha T.","contributorId":179296,"corporation":false,"usgs":false,"family":"Poret-Peterson","given":"Amisha","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":663247,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oremland, Ronald S. 0000-0001-7382-0147 roremlan@usgs.gov","orcid":"https://orcid.org/0000-0001-7382-0147","contributorId":931,"corporation":false,"usgs":true,"family":"Oremland","given":"Ronald","email":"roremlan@usgs.gov","middleInitial":"S.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":663248,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dunlap, Darren S.","contributorId":179297,"corporation":false,"usgs":false,"family":"Dunlap","given":"Darren S.","affiliations":[],"preferred":false,"id":663249,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Akob, Denise M. 0000-0003-1534-3025 dakob@usgs.gov","orcid":"https://orcid.org/0000-0003-1534-3025","contributorId":4980,"corporation":false,"usgs":true,"family":"Akob","given":"Denise","email":"dakob@usgs.gov","middleInitial":"M.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":5058,"text":"Office of the Chief Scientist for Water","active":true,"usgs":true}],"preferred":true,"id":663250,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70181009,"text":"70181009 - 2017 - Complete genome sequence of the acetylene-fermenting Pelobacter sp. strain SFB93","interactions":[],"lastModifiedDate":"2017-02-11T17:48:12","indexId":"70181009","displayToPublicDate":"2017-02-11T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5099,"text":"Genome Announcements","active":true,"publicationSubtype":{"id":10}},"title":"Complete genome sequence of the acetylene-fermenting Pelobacter sp. strain SFB93","docAbstract":"Acetylene fermentation is a rare metabolism that was previously reported as being unique to Pelobacter acetylenicus. Here, we report the genome sequence of Pelobacter sp. strain SFB93, an acetylene-fermenting bacterium isolated from sediments collected in San Francisco Bay, CA.
","language":"English","publisher":"American Society for Microbiology","doi":"10.1128/genomeA.01573-16","usgsCitation":"Sutton, J.M., Baesman, S., Fierst, J.L., Poret-Peterson, A.T., Oremland, R.S., Dunlap, D., and Akob, D.M., 2017, Complete genome sequence of the acetylene-fermenting Pelobacter sp. strain SFB93: Genome Announcements, v. 5, no. 6, e01573-16; 2 p., https://doi.org/10.1128/genomeA.01573-16.","productDescription":"e01573-16; 2 p.","ipdsId":"IP-081654","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":470077,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1128/genomea.01573-16","text":"Publisher Index Page"},{"id":335177,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"589ffec9e4b099f50d3e0424","contributors":{"authors":[{"text":"Sutton, John M.","contributorId":179294,"corporation":false,"usgs":false,"family":"Sutton","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":663252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baesman, Shaun 0000-0003-0741-8269 sbaesman@usgs.gov","orcid":"https://orcid.org/0000-0003-0741-8269","contributorId":3478,"corporation":false,"usgs":true,"family":"Baesman","given":"Shaun","email":"sbaesman@usgs.gov","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":663251,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fierst, Janna L.","contributorId":179295,"corporation":false,"usgs":false,"family":"Fierst","given":"Janna","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":663253,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Poret-Peterson, Amisha T.","contributorId":179296,"corporation":false,"usgs":false,"family":"Poret-Peterson","given":"Amisha","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":663254,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oremland, Ronald S. 0000-0001-7382-0147 roremlan@usgs.gov","orcid":"https://orcid.org/0000-0001-7382-0147","contributorId":931,"corporation":false,"usgs":true,"family":"Oremland","given":"Ronald","email":"roremlan@usgs.gov","middleInitial":"S.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":663255,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dunlap, Darren S.","contributorId":179297,"corporation":false,"usgs":false,"family":"Dunlap","given":"Darren S.","affiliations":[],"preferred":false,"id":663256,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Akob, Denise M. 0000-0003-1534-3025 dakob@usgs.gov","orcid":"https://orcid.org/0000-0003-1534-3025","contributorId":4980,"corporation":false,"usgs":true,"family":"Akob","given":"Denise","email":"dakob@usgs.gov","middleInitial":"M.","affiliations":[{"id":5058,"text":"Office of the Chief Scientist for Water","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":663257,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70178724,"text":"ofr20161199 - 2017 - Geologic assessment of undiscovered oil and gas resources—Lower Cretaceous Albian to Upper Cretaceous Cenomanian carbonate rocks of the Fredericksburg and Washita Groups, United States Gulf of Mexico Coastal Plain and State Waters","interactions":[],"lastModifiedDate":"2017-02-13T10:46:43","indexId":"ofr20161199","displayToPublicDate":"2017-02-10T15:30:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1199","title":" Geologic assessment of undiscovered oil and gas resources—Lower Cretaceous Albian to Upper Cretaceous Cenomanian carbonate rocks of the Fredericksburg and Washita Groups, United States Gulf of Mexico Coastal Plain and State Waters","docAbstract":"In 2010, the U.S. Geological Survey (USGS) assessed Lower Cretaceous Albian to Upper Cretaceous Cenomanian carbonate rocks of the Fredericksburg and Washita Groups and their equivalent units for technically recoverable, undiscovered hydrocarbon resources underlying onshore lands and State Waters of the Gulf Coast region of the United States. This assessment was based on a geologic model that incorporates the Upper Jurassic-Cretaceous-Tertiary Composite Total Petroleum System (TPS) of the Gulf of Mexico basin; the TPS was defined previously by the USGS assessment team in the assessment of undiscovered hydrocarbon resources in Tertiary strata of the Gulf Coast region in 2007. One conventional assessment unit (AU), which extends from south Texas to the Florida panhandle, was defined: the Fredericksburg-Buda Carbonate Platform-Reef Gas and Oil AU. The assessed stratigraphic interval includes the Edwards Limestone of the Fredericksburg Group and the Georgetown and Buda Limestones of the Washita Group. The following factors were evaluated to define the AU and estimate oil and gas resources: potential source rocks, hydrocarbon migration, reservoir porosity and permeability, traps and seals, structural features, paleoenvironments (back-reef lagoon, reef, and fore-reef environments), and the potential for water washing of hydrocarbons near outcrop areas.
In Texas and Louisiana, the downdip boundary of the AU was defined as a line that extends 10 miles downdip of the Lower Cretaceous shelf margin to include potential reef-talus hydrocarbon reservoirs. In Mississippi, Alabama, and the panhandle area of Florida, where the Lower Cretaceous shelf margin extends offshore, the downdip boundary was defined by the offshore boundary of State Waters. Updip boundaries of the AU were drawn based on the updip extent of carbonate rocks within the assessed interval, the presence of basin-margin fault zones, and the presence of producing wells. Other factors evaluated were the middle Cenomanian sea-level fall and erosion that removed large portions of platform and platform-margin carbonate sediments in the Washita Group of central Louisiana. The production history of discovered reservoirs and well data within the AU were examined to estimate the number and size of undiscovered oil and gas reservoirs within the AU. Using the USGS National Oil and Gas Assessment resource assessment methodology, mean volumes of 40 million barrels of oil, 622 billion cubic feet of gas, and 14 million barrels of natural gas liquids are the estimated technically recoverable undiscovered resources for the Fredericksburg-Buda Carbonate Platform-Reef Gas and Oil AU.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161199","usgsCitation":"Swanson, S.M., Enomoto, C.B., Dennen, K.O., Valentine, B.J., and Cahan, S.M., 2017, Geologic assessment of undiscovered oil and gas resources—Lower Cretaceous Albian to Upper Cretaceous Cenomanian carbonate rocks of the Fredericksburg and Washita Groups, United States Gulf of Mexico Coastal Plain and State Waters: U.S. Geological Survey Open-File Report 2016–1199, 69 p., https://doi.org/10.3133/ofr20161199.","productDescription":"Report: vii, 68 p.; Appendix 1: 2 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-064618","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":335075,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1199/ofr20161199.pdf","text":"Report","size":"10.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1199"},{"id":335074,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1199/coverthb.jpg"},{"id":335076,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1199/ofr20161199_appendix1.pdf","text":"Appendix 1 - ","size":"446 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"Input data form for the Fredericksburg-Buda Carbonate Platform-Reef Gas and Oil Assessment Unit (50490127)"}],"country":"United States","state":"Alabama, Arkansas, Florida, Louisiana, Mississippi, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.20898437499999,\n 37.996162679728116\n ],\n [\n -90.3515625,\n 37.75334401310656\n ],\n [\n -90.9228515625,\n 36.491973470593685\n ],\n [\n -92.021484375,\n 35.746512259918504\n ],\n [\n -93.1201171875,\n 35.209721645221386\n ],\n [\n -94.74609375,\n 34.994003757575776\n ],\n [\n -96.6357421875,\n 34.125447565116126\n ],\n [\n -97.55859375,\n 33.90689555128866\n ],\n [\n -98.9208984375,\n 33.687781758439364\n ],\n [\n -100.1513671875,\n 33.211116472416855\n ],\n [\n -101.4697265625,\n 32.24997445586331\n ],\n [\n -102.1728515625,\n 31.27855085894653\n ],\n [\n -101.953125,\n 30.221101852485987\n ],\n [\n -101.42578124999999,\n 29.611670115197377\n ],\n [\n -100.5908203125,\n 28.729130483430154\n ],\n [\n -99.84374999999999,\n 27.605670826465445\n ],\n [\n -99.404296875,\n 26.941659545381516\n ],\n [\n -99.00878906249999,\n 26.31311263768267\n ],\n [\n -98.7451171875,\n 26.194876675795218\n ],\n [\n -97.822265625,\n 25.878994400196202\n ],\n [\n -97.119140625,\n 25.878994400196202\n ],\n [\n -97.03125,\n 26.27371402440643\n ],\n [\n -97.03125,\n 27.0982539061379\n ],\n [\n -96.6796875,\n 27.916766641249065\n ],\n [\n -95.49316406249999,\n 28.459033019728043\n ],\n [\n -94.306640625,\n 29.075375179558346\n ],\n [\n -92.98828125,\n 29.11377539511439\n ],\n [\n -91.7138671875,\n 29.267232865200878\n ],\n [\n -90.87890625,\n 28.92163128242129\n ],\n [\n -89.8681640625,\n 28.806173508854776\n ],\n [\n -89.07714843749999,\n 28.806173508854776\n ],\n [\n -88.8134765625,\n 29.19053283229458\n ],\n [\n -88.9453125,\n 29.80251790576445\n ],\n [\n -88.5498046875,\n 30.14512718337613\n ],\n [\n -87.6708984375,\n 30.031055426540206\n ],\n [\n -86.8798828125,\n 30.031055426540206\n ],\n [\n -85.7373046875,\n 29.76437737516313\n ],\n [\n -85.1220703125,\n 29.38217507514529\n ],\n [\n -84.55078125,\n 29.57345707301757\n ],\n [\n -84.111328125,\n 29.57345707301757\n ],\n [\n -83.6279296875,\n 29.305561325527698\n ],\n [\n -83.27636718749999,\n 29.036960648558267\n ],\n [\n -82.8369140625,\n 29.38217507514529\n ],\n [\n -82.6171875,\n 30.44867367928756\n ],\n [\n -82.705078125,\n 31.690781806136822\n ],\n [\n -83.8037109375,\n 32.287132632616384\n ],\n [\n -85.1220703125,\n 33.211116472416855\n ],\n [\n -86.0009765625,\n 34.415973384481866\n ],\n [\n -87.099609375,\n 35.60371874069731\n ],\n [\n -87.6708984375,\n 37.26530995561875\n ],\n [\n -88.72558593749999,\n 37.78808138412046\n ],\n [\n -89.20898437499999,\n 37.996162679728116\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Energy Resources Science Center
U.S. Geological Survey
Mail Stop 956
12201 Sunrise Valley Drive
Reston, VA 20192
http://energy.usgs.gov/
The characteristics of bed material at selected sites within the Sacramento–San Joaquin Delta, California, during 2010–13 are described in a study conducted by the U.S. Geological Survey in cooperation with the Bureau of Reclamation. During 2010‒13, six complete sets of samples were collected. Samples were initially collected at 30 sites; however, starting in 2012, samples were collected at 7 additional sites. These sites are generally collocated with an active streamgage. At all but one site, a separate bed-material sample was collected at three locations within the channel (left, right, and center). Bed-material samples were collected using either a US BMH–60 or a US BM–54 (for sites with higher stream velocity) cable-suspended, scoop sampler. Samples from each location were oven-dried and sieved. Bed material finer than 2 millimeters was subsampled using a sieving riffler and processed using a Beckman Coulter LS 13–320 laser diffraction particle-size analyzer. To determine the organic content of the bed material, the loss on ignition method was used for one subsample from each location. Particle-size distributions are presented as cumulative percent finer than a given size. Median and 90th-percentile particle size, and the percentage of subsample mass lost using the loss on ignition method for each sample are also presented in this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1026","issn":"2327-638X (online)","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Marineau, M.D., and Wright, S.A., 2017, Bed-material characteristics of the Sacramento–San Joaquin Delta, California, 2010–13: U.S. Geological Survey Data Series 1026, 55 p., \nhttps://doi.org/10.3133/ds1026.","productDescription":"vi, 55 p.","numberOfPages":"66","onlineOnly":"Y","ipdsId":"IP-078870","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":334907,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1026/ds1026.pdf","text":"Report","size":"2.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1026"},{"id":334906,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1026/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento–San Joaquin Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122,\n 38.5\n ],\n [\n -122,\n 37.66667\n ],\n [\n -121.1667,\n 37.66667\n ],\n [\n -121.1667,\n 38.5\n ],\n [\n -122,\n 38.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director
U.S. Geological Survey
California Water Science Center
6000 J Street, Placer Hall
Sacramento, CA 95819
The motivation for earthquake early warning (EEW) is the fact that in many applications a few extra seconds of notice ahead of the about-imminent strong shaking can provide significant benefit. Reducing data latencies, accelerating processing times, and tuning seismic station distributions increase time available for warning. We assess the feasibility of EEW for Hawai‘i and examine how additional stations or upgrades to existing stations can improve warning times. We designed an objective method to identify the most efficient sites for improving an existing seismic network’s coverage, taking both seismic station distribution and seismic hazard into account. The choice of locations for new seismic station sites is informed by improvements in warning time, considering the distribution of seismic hazard and exposure. New sites that improve warning time from earthquakes that are most likely to generate significant ground motions are given preference. This technique may be applied to any seismically active region and target infrastructure in which seismic hazard is spatially defined. We demonstrate this method’s use on the Island of Hawai‘i, with focus on warnings to astronomical observatories on Mauna Kea and island population centers Hilo and Kailua-Kona. We identified 13 candidate sites for new sensors, telemetry upgrades, or new station installations that should provide an additional 1–4 s of warning for the most probable damaging earthquakes in southern Ka‘ū and northern offshore regions in which 2–14 s and <4 s of warning are currently estimated, respectively.
The Water, Energy, and Biogeochemical Model (WEBMOD) uses the framework of the U.S. Geological Survey (USGS) Modular Modeling System to simulate fluxes of water and solutes through watersheds. WEBMOD divides watersheds into model response units (MRU) where fluxes and reactions are simulated for the following eight hillslope reservoir types: canopy; snowpack; ponding on impervious surfaces; O-horizon; two reservoirs in the unsaturated zone, which represent preferential flow and matrix flow; and two reservoirs in the saturated zone, which also represent preferential flow and matrix flow. The reservoir representing ponding on impervious surfaces, currently not functional (2016), will be implemented once the model is applied to urban areas. MRUs discharge to one or more stream reservoirs that flow to the outlet of the watershed. Hydrologic fluxes in the watershed are simulated by modules derived from the USGS Precipitation Runoff Modeling System; the National Weather Service Hydro-17 snow model; and a topography-driven hydrologic model (TOPMODEL). Modifications to the standard TOPMODEL include the addition of heterogeneous vertical infiltration rates; irrigation; lateral and vertical preferential flows through the unsaturated zone; pipe flow draining the saturated zone; gains and losses to regional aquifer systems; and the option to simulate baseflow discharge by using an exponential, parabolic, or linear decrease in transmissivity. PHREEQC, an aqueous geochemical model, is incorporated to simulate chemical reactions as waters evaporate, mix, and react within the various reservoirs of the model. The reactions that can be specified for a reservoir include equilibrium reactions among water; minerals; surfaces; exchangers; and kinetic reactions such as kinetic mineral dissolution or precipitation, biologically mediated reactions, and radioactive decay. WEBMOD also simulates variations in the concentrations of the stable isotopes deuterium and oxygen-18 as a result of varying inputs, mixing, and evaporation. This manual describes the WEBMOD input and output files, along with the algorithms and procedures used to simulate the hydrology and water quality in a watershed. Examples are presented that demonstrate hydrologic processes, weathering reactions, and isotopic evolution in an alpine watershed and the effect of irrigation on water flows and salinity in an intensively farmed agricultural area.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: Surface Water in Book 6: Modeling Techniques","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6B35","issn":"2328-7055","usgsCitation":"Webb, R.M.T., and Parkhurst, D.L., 2017, Water, Energy, and Biogeochemical Model (WEBMOD), user’s manual, version 1: U.S. Geological Survey Techniques and Methods, book 6, chap. B35, 171 p., https://doi.org/10.3133/tm6B35.","productDescription":"xiv, 171 p.","numberOfPages":"190","onlineOnly":"Y","costCenters":[{"id":144,"text":"Branch of Regional Research","active":false,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":438440,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P26W9K","text":"USGS data release","linkHelpText":"Water, Energy, and Biogeochemical Model"},{"id":334918,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/b35/tm6b35.pdf","text":"Report","size":"8.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 6-B35"},{"id":334917,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/b35/coverthb.jpg"},{"id":334980,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://doi.org/10.5066/F7P26W9K","text":"Water, Energy, and Biogeochemical Model (WEBMOD)"}],"publicComments":"This report is Chapter 35 of Section B: Surface Water in Book 6 Modeling Techniques","contact":"Chief, National Research Program, Central Branch
U.S. Geological Survey
Box 25585, Mail Stop 418
Denver, CO 80225-0585
This protocol describes the processing steps for 45 g and 9 g soil samples potentially contaminated with Bacillus anthracis spores. The protocol is designed to separate and concentrate the spores from bulk soil down to a pellet that can be used for further analysis. Soil extraction solution and mechanical shaking are used to disrupt soil particle aggregates and to aid in the separation of spores from soil particles. Soil samples are washed twice with soil extraction solution to maximize recovery. Differential centrifugation is used to separate spores from the majority of the soil material. The 45 g protocol has been demonstrated by two laboratories using both loamy and sandy soil types. There were no significant differences overall between the two laboratories for either soil type, suggesting that the processing protocol would be robust enough to use at multiple laboratories while achieving comparable recoveries. The 45 g protocol has demonstrated a matrix limit of detection at 14 spores/gram of soil for loamy and sandy soils.
","language":"English","publisher":"United States Environmental Protection Agency","publisherLocation":"Cincinnati, OH","usgsCitation":"Silvestri, E.E., and Griffin, D.W., 2017, Processing protocol for soil samples potentially contaminated with Bacillus anthracis spores [HS7.52.02 - 514], vi, 18 p.","productDescription":"vi, 18 p.","ipdsId":"IP-071772","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":342673,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":342662,"type":{"id":15,"text":"Index Page"},"url":"https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=335822"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"594a3429e4b062508e36af53","contributors":{"authors":[{"text":"Silvestri, Erin E.","contributorId":127343,"corporation":false,"usgs":false,"family":"Silvestri","given":"Erin","email":"","middleInitial":"E.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":698739,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Griffin, Dale W. 0000-0003-1719-5812 dgriffin@usgs.gov","orcid":"https://orcid.org/0000-0003-1719-5812","contributorId":2178,"corporation":false,"usgs":true,"family":"Griffin","given":"Dale","email":"dgriffin@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":698738,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70179809,"text":"ofr20171002 - 2017 - Forested floristic quality index: An assessment tool for forested wetland habitats using the quality and quantity of woody vegetation at Coastwide Reference Monitoring System (CRMS) vegetation monitoring stations","interactions":[],"lastModifiedDate":"2017-02-08T11:49:54","indexId":"ofr20171002","displayToPublicDate":"2017-02-08T00:00:00","publicationYear":"2017","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":"2017-1002","title":"Forested floristic quality index: An assessment tool for forested wetland habitats using the quality and quantity of woody vegetation at Coastwide Reference Monitoring System (CRMS) vegetation monitoring stations","docAbstract":"The U.S. Geological Survey, in cooperation with the Coastal Protection and Restoration Authority of Louisiana and the Coastal Wetlands Planning, Protection and Restoration Act, developed the Forested Floristic Quality Index (FFQI) for the Coastwide Reference Monitoring System (CRMS). The FFQI will help evaluate forested wetland sites on a continuum from severely degraded to healthy and will assist in defining areas where forested wetland restoration can be successful by projecting the trajectories of change. At each CRMS forested wetland site there are stations for quantifying the overstory, understory, and herbaceous vegetation layers. Rapidly responding overstory canopy cover and herbaceous layer composition are measured annually, while gradually changing overstory basal area and species composition are collected on a 3-year cycle.
A CRMS analytical team has tailored these data into an index much like the Floristic Quality Index (FQI) currently used for herbaceous marsh and for the herbaceous layer of the swamp vegetation. The core of the FFQI uses basal area by species to assess the quality and quantity of the overstory at each of three stations within each CRMS forested wetland site. Trees that are considered by experts to be higher quality swamp species like Taxodium distichum (bald cypress) and Nyssa aquatica (water tupelo) are scored higher than tree species like Triadica sebifera (Chinese tallow) and Salix nigra (black willow) that are indicators of recent disturbance. This base FFQI is further enhanced by the percent canopy cover in the overstory and the presence of indicator species at the forest floor. This systemic approach attempts to differentiate between locations with similar basal areas that are on different ecosystem trajectories. Because of these varying states of habitat degradation, paired use of the FQI and the FFQI is useful to interpret the vegetative data in transitional locations. There is often an inverse relation between the health of the overstory and health of the herbaceous community beneath it because of resource competition (for example, light) and differing environmental preferences between the two communities. The herbaceous layer vegetation responds rapidly to basic environmental factors such as flooding, salinity, and nutrients and can offer insight into the sustainability of swamps on a temporal scale shorter than tha of the slowly growing woody vegetation.
The FFQI will be available via the CRMS spatial viewer (http://lacoast.gov/crms2/home.aspx), and a new score will be calculated annually for each CRMS forested wetland site as data are collected to establish trends, to compare among sites, and to evaluate specific restoration projects when applicable. The FFQI will identify forested wetland areas in need of restoration and conservation and will help define targets and trajectories for restoration planning.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171002","collaboration":"Prepared in cooperation with the Coastal Protection and Restoration Authority of Louisiana and the Coastal Wetlands Planning, Protection and Restoration Act","usgsCitation":"Wood, W.B., Shaffer, G.P., Visser, J.M., Krauss, K.W., Piazza, S.C., Sharp, L.A., and Cretini, K.F., 2017, Forested Floristic Quality Index—An assessment tool for forested wetland habitats using the quality and quantity of woody vegetation at Coastwide Reference Monitoring System (CRMS) vegetation monitoring stations: U.S. Geological Survey Open-File Report 2017–1002, 15 p., https://doi.org/10.3133/ofr20171002.","productDescription":"iv, 15 p.","numberOfPages":"24","onlineOnly":"Y","ipdsId":"IP-059586","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":334901,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1002/ofr20171002.pdf","text":"Report","size":"2.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1002"},{"id":334900,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1002/coverthb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.691650390625,\n 28.603814407841327\n ],\n [\n -92.691650390625,\n 30.779598396611537\n ],\n [\n -88.52783203125,\n 30.779598396611537\n ],\n [\n -88.52783203125,\n 28.603814407841327\n ],\n [\n -92.691650390625,\n 28.603814407841327\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506
https://www.usgs.gov/centers/wetland-and-aquatic-research-center-warc
","tableOfContents":"Demographic characteristics of bats are often insufficiently described for modeling populations. In data poor situations, experts are often relied upon for characterizing ecological systems. In concert with the development of a matrix model describing Indiana bat (Myotis sodalis) demography, we elicited estimates for parameterizing this model from 12 experts. We conducted this elicitation in two stages, requesting expert values for 12 demographic rates. These rates were adult and juvenile seasonal (winter, summer, fall) survival rates, pup survival in fall, and propensity and success at breeding. Experts were most in agreement about adult fall survival (3% Coefficient of Variation) and least in agreement about propensity of juveniles to breed (37% CV). The experts showed greater concordance for adult ( mean CV, adult = 6.2%) than for juvenile parameters ( mean CV, juvenile = 16.4%), and slightly more agreement for survival (mean CV, survival = 9.8%) compared to reproductive rates ( mean CV, reproduction = 15.1%). However, survival and reproduction were negatively and positively biased, respectively, relative to a stationary dynamic. Despite the species exhibiting near stationary dynamics for two decades prior to the onset of a potential extinction-causing agent, white-nose syndrome, expert estimates indicated a population decline of -11% per year (95% CI = -2%, -20%); quasi-extinction was predicted within a century ( mean = 61 years to QE, range = 32, 97) by 10 of the 12 experts. Were we to use these expert estimates in our modeling efforts, we would have errantly trained our models to a rapidly declining demography asymptomatic of recent demographic behavior. While experts are sometimes the only source of information, a clear understanding of the temporal and spatial context of the information being elicited is necessary to guard against wayward predictions.
","language":"English","publisher":"PeerJ","doi":"10.7287/peerj.preprints.2790v1","usgsCitation":"Thogmartin, W.E., Sanders-Reed, C., Szymanski, J., Pruitt, L., and Runge, M.C., 2017, Experts correctly describe demography associated with historical decline of the endangered Indiana bat, but not recent period of stationarity: PeerJ, v. 5, e2790v1, https://doi.org/10.7287/peerj.preprints.2790v1.","productDescription":"e2790v1","ipdsId":"IP-029961","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":461755,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.7287/peerj.preprints.2790v1","text":"External Repository"},{"id":338812,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58de194de4b02ff32c699c93","contributors":{"authors":[{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":687426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sanders-Reed, Carol A.","contributorId":86441,"corporation":false,"usgs":true,"family":"Sanders-Reed","given":"Carol A.","affiliations":[],"preferred":false,"id":687708,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Szymanski, Jennifer","contributorId":15123,"corporation":false,"usgs":false,"family":"Szymanski","given":"Jennifer","affiliations":[{"id":6969,"text":"U.S. Fish and Wildlife Service, Division of Endangered Species","active":true,"usgs":false}],"preferred":false,"id":687709,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pruitt, Lori","contributorId":17468,"corporation":false,"usgs":true,"family":"Pruitt","given":"Lori","email":"","affiliations":[],"preferred":false,"id":687710,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":687711,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70179037,"text":"sir20165172 - 2017 - Electrical resistivity investigation of fluvial geomorphology to evaluate potential seepage conduits to agricultural lands along the San Joaquin River, Merced County, California, 2012–13","interactions":[],"lastModifiedDate":"2017-02-09T10:45:20","indexId":"sir20165172","displayToPublicDate":"2017-02-08T00:00:00","publicationYear":"2017","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":"2016-5172","title":"Electrical resistivity investigation of fluvial geomorphology to evaluate potential seepage conduits to agricultural lands along the San Joaquin River, Merced County, California, 2012–13","docAbstract":"Increased flows in the San Joaquin River, part of the San Joaquin River Restoration Program, are designed to help restore fish populations. However, increased seepage losses could result from these higher restoration flows, which could exacerbate existing drainage problems in neighboring agricultural lands and potentially damage crops. Channel deposits of abandoned river meanders that are hydraulically connected to the river could act as seepage conduits, allowing rapid and widespread water-table rise during restoration flows. There is a need to identify the geometry and properties of these channel deposits to assess their role in potential increased seepage effects and to evaluate management alternatives for reducing seepage. Electrical and electromagnetic surface geophysical methods have provided a reliable proxy for lithology in studies of fluvial and hyporheic systems where a sufficient electrical contrast exists between deposits of differing grain size. In this study, direct-current (DC) resistivity was used to measure subsurface resistivity to identify channel deposits and to map their subsurface geometry. The efficacy of this method was assessed by using DC resistivity surveys collected along a reach of the San Joaquin River in Merced County, California, during the summers of 2012 and 2013, in conjunction with borings and associated measurements from a hydraulic profiling tool. Modeled DC resistivity data corresponded with data from cores, hand-auger samples, a hydraulic profiling tool, and aerial photographs, confirming that DC resistivity is effective for differentiating between silt and sand deposits in this setting. Modeled DC resistivity data provided detailed two-dimensional cross-sectional resistivity profiles to a depth of about 20 meters. The distribution of high-resistivity units in these profiles was used as a proxy for identifying areas of high hydraulic conductivity. These data were used subsequently to guide the location and depth of wells installed onsite for monitoring flow in the channel deposits. Estimates of the cross-sectional area of channel deposits from DC resistivity pseudosections can provide critical input for groundwater-flow models designed to simulate river seepage and evaluate seepage-management alternatives.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165172","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Groover, K.D., Burgess, M.K., Howle, J.F., Philips, S.P., 2017, Electrical resistivity investigation of fluvial geomorphology to evaluate potential seepage conduits to agricultural lands along the San Joaquin River, Merced County, California, 2012–13: U.S. Geological Survey Scientific Investigations Report 2016–5172, 39 p., https://doi.org/10.3133/sir20165172.","productDescription":"vii, 39 p.","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-055526","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":334523,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5172/sir20165172.pdf","text":"Report","size":"5.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5172"},{"id":334522,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5172/coverthb3.jpg"}],"country":"United States","state":"California","county":"Merced County","otherGeospatial":"San Joaquin River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.1,\n 36.5\n ],\n [\n -121.1,\n 37.5\n ],\n [\n -120.5,\n 37.5\n ],\n [\n -120.5,\n 36.5\n ],\n [\n -121.1,\n 36.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
https://ca.water.usgs.gov
The Wind River Basin in Wyoming is one of many structural and sedimentary basins that formed in the Rocky Mountain foreland during the Laramide orogeny. The basin is nearly 200 miles long, 70 miles wide, and encompasses about 7,400 square miles in central Wyoming. The basin is bounded by the Washakie Range, Owl Creek uplift, and southern Bighorn Mountains on the north, the Casper arch on the east, the Granite Mountains on the south, and Wind River Range on the west.
Many important conventional oil and gas fields producing from reservoirs ranging in age from Mississippian through Tertiary have been discovered in this basin. In addition, an extensive unconventional overpressured basin-centered gas accumulation has been identified in Cretaceous and Tertiary strata in the deeper parts of the basin. It has long been suggested that various Upper Cretaceous marine shales, including the Cody Shale, are the principal hydrocarbon source rocks for many of these accumulations. With recent advances and success in horizontal drilling and multistage fracture stimulation, there has been an increase in exploration and completion of wells in these marine shales in other Rocky Mountain Laramide basins that were traditionally thought of only as hydrocarbon source rocks.
The two stratigraphic cross sections presented in this report were constructed as part of a project carried out by the U.S. Geological Survey to characterize and evaluate the undiscovered continuous (unconventional) oil and gas resources of the Niobrara interval of the Upper Cretaceous Cody Shale in the Wind River Basin in central Wyoming. The primary purpose of the cross sections is to show the stratigraphic relationship of the Niobrara equivalent strata and associated rocks in the lower part of the Cody Shale in the Wind River Basin. These two cross sections were constructed using borehole geophysical logs from 37 wells drilled for oil and gas exploration and production, and one surface section along East Sheep Creek near Shotgun Butte in the northwestern part of the basin. Both lines originate at the East Sheep Creek surface section and end near Clarkson Hill in the extreme southeastern part of the basin. The stratigraphic interval extends from the upper part of the Frontier Formation to the middle part of the Cody Shale. The datum is the base of the “chalk kick” marker bed, a distinctive resistivity peak or zone in the lower part of the Cody Shale. A gamma ray and (or) spontaneous potential (SP) log was used in combination with a resistivity log to identify and correlate units. Marine molluscan index fossils collected from nearby outcrop sections were projected into the subsurface to help determine the relative ages of the strata and aid in correlation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3370","usgsCitation":"Finn, T.M., 2017, Stratigraphic cross sections of the Niobrara interval of the Cody Shale and associated rocks in the Wind River Basin, central Wyoming: U.S. Geological Survey Scientific Investigations Map 3370, 19 p., 1 sheet, https://doi.org/10.3133/sim3370.","productDescription":"Report: iv, 19 p.; Sheet: 56.00 x 29.00 inches","onlineOnly":"Y","ipdsId":"IP-073462","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":334322,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3370/coverthb.jpg"},{"id":334325,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3370/sim3370.pdf","text":"Report","size":"5.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3370 Pamphlet"},{"id":334327,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3370/sim3370_sheet.pdf","text":"Map","size":"1.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3370 Map"}],"country":"United States","state":"Wyoming","otherGeospatial":"Wind River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110,\n 42.5\n ],\n [\n -110,\n 43.75\n ],\n [\n -106.5,\n 43.75\n ],\n [\n -106.5,\n 42.5\n ],\n [\n -110,\n 42.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, USGS Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, Mail Stop 939
Denver Federal Center
Denver, CO 80225
In 1916, Swedish geologist Ernst Jakob Lennart von Post delivered a provocative lecture in Oslo, Norway, advocating the use of pollen grains in bog sediments as indicators of past vegetation and climate. The lecture spawned many applications and represents a landmark in multidisciplinary science.
","largerWorkTitle":"Nature Plants","language":"English","doi":"10.1038/nplants.2017.1","usgsCitation":"Edwards, K.J., Fyfe, R., and Jackson, S., 2017, The first 100 years of pollen analysis: Nature Plants, v. 3, 17001, https://doi.org/10.1038/nplants.2017.1.","productDescription":"17001","ipdsId":"IP-082947","costCenters":[{"id":41166,"text":"Southwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":470080,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/2164/9078","text":"External Repository"},{"id":381137,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","noUsgsAuthors":false,"publicationDate":"2017-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Edwards, Kevin J.","contributorId":245536,"corporation":false,"usgs":false,"family":"Edwards","given":"Kevin","email":"","middleInitial":"J.","affiliations":[{"id":39960,"text":"University of Aberdeen, UK","active":true,"usgs":false}],"preferred":false,"id":806384,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fyfe, Ralph","contributorId":245537,"corporation":false,"usgs":false,"family":"Fyfe","given":"Ralph","email":"","affiliations":[{"id":39461,"text":"University of Plymouth, UK","active":true,"usgs":false}],"preferred":false,"id":806385,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jackson, Stephen T. 0000-0002-1487-4652","orcid":"https://orcid.org/0000-0002-1487-4652","contributorId":245538,"corporation":false,"usgs":true,"family":"Jackson","given":"Stephen T.","affiliations":[{"id":41166,"text":"Southwest Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":806386,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70189688,"text":"70189688 - 2017 - Comparing stream-specific to generalized temperature models to guide salmonid management in a changing climate","interactions":[],"lastModifiedDate":"2018-04-24T13:34:59","indexId":"70189688","displayToPublicDate":"2017-02-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3278,"text":"Reviews in Fish Biology and Fisheries","active":true,"publicationSubtype":{"id":10}},"title":"Comparing stream-specific to generalized temperature models to guide salmonid management in a changing climate","docAbstract":"Global climate change is predicted to increase air and stream temperatures and alter thermal habitat suitability for growth and survival of coldwater fishes, including brook charr (Salvelinus fontinalis), brown trout (Salmo trutta), and rainbow trout (Oncorhynchus mykiss). In a changing climate, accurate stream temperature modeling is increasingly important for sustainable salmonid management throughout the world. However, finite resource availability (e.g. funding, personnel) drives a tradeoff between thermal model accuracy and efficiency (i.e. cost-effective applicability at management-relevant spatial extents). Using different projected climate change scenarios, we compared the accuracy and efficiency of stream-specific and generalized (i.e. region-specific) temperature models for coldwater salmonids within and outside the State of Michigan, USA, a region with long-term stream temperature data and productive coldwater fisheries. Projected stream temperature warming between 2016 and 2056 ranged from 0.1 to 3.8 °C in groundwater-dominated streams and 0.2–6.8 °C in surface-runoff dominated systems in the State of Michigan. Despite their generally lower accuracy in predicting exact stream temperatures, generalized models accurately projected salmonid thermal habitat suitability in 82% of groundwater-dominated streams, including those with brook charr (80% accuracy), brown trout (89% accuracy), and rainbow trout (75% accuracy). In contrast, generalized models predicted thermal habitat suitability in runoff-dominated streams with much lower accuracy (54%). These results suggest that, amidst climate change and constraints in resource availability, generalized models are appropriate to forecast thermal conditions in groundwater-dominated streams within and outside Michigan and inform regional-level salmonid management strategies that are practical for coldwater fisheries managers, policy makers, and the public. We recommend fisheries professionals reserve resource-intensive stream-specific models for runoff-dominated systems containing high-priority fisheries resources (e.g. trophy individuals, endangered species) that will be directly impacted by projected stream warming.","language":"English","publisher":"SpringerLink","doi":"10.1007/s11160-017-9467-0","usgsCitation":"Andrew K. Carlson, Taylor, W.W., Hartikainen, K.M., Dana M. Infante, Beard, and Lynch, A., 2017, Comparing stream-specific to generalized temperature models to guide salmonid management in a changing climate: Reviews in Fish Biology and Fisheries, v. 2, no. 27, p. 443-462, https://doi.org/10.1007/s11160-017-9467-0.","productDescription":"19 p. 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Box 25046, Mail Stop 980
Denver, CO 80225
The U.S. Geological Survey Branch of Quality Systems operates the Precipitation Chemistry Quality Assurance Project (PCQA) for the National Atmospheric Deposition Program/National Trends Network (NADP/NTN) and National Atmospheric Deposition Program/Mercury Deposition Network (NADP/MDN). Since 1978, various programs have been implemented by the PCQA to estimate data variability and bias contributed by changing protocols, equipment, and sample submission schemes within NADP networks. These programs independently measure the field and laboratory components which contribute to the overall variability of NADP wet-deposition chemistry and precipitation depth measurements. The PCQA evaluates the quality of analyte-specific chemical analyses from the two, currently (2016) contracted NADP laboratories, Central Analytical Laboratory and Mercury Analytical Laboratory, by comparing laboratory performance among participating national and international laboratories. Sample contamination and stability are evaluated for NTN and MDN by using externally field-processed blank samples provided by the Branch of Quality Systems. A colocated sampler program evaluates the overall variability of NTN measurements and bias between dissimilar precipitation gages and sample collectors.
This report documents historical PCQA operations and general procedures for each of the external quality-assurance programs from 2007 to 2016.
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USGS Branch of Quality Systems
Box 25046, Mail Stop 401
Denver, CO 80225
Heavy rainfall occurred across Louisiana and southwestern Mississippi in August 2016 as a result of a slow-moving area of low pressure and a high amount of atmospheric moisture. The storm caused major flooding in the southern portions of Louisiana including areas surrounding Baton Rouge and Lafayette. Flooding occurred along the rivers such as the Amite, Comite, Tangipahoa, Tickfaw, Vermilion, and Mermentau Rivers. Over 31 inches of rain was reported in the city of Watson, 20 miles northeast of Baton Rouge, La., over the duration of the event. Streamflow-gaging stations operated by the U.S. Geological Survey (USGS) recorded peak streamflows of record at 10 locations, and 7 other locations experienced peak streamflows ranking in the top five for the duration of the period of record. In August 2016, USGS hydrographers made 50 discharge measurements at 21 locations on streams in Louisiana. Many of those discharge measurements were made for the purpose of verifying the accuracy of stage-streamflow relations at gaging stations operated by the USGS. Following the storm event, USGS hydrographers recovered and documented 590 high-water marks, noting location and height of the water above land surface. Many of these high-water marks were used to create 12 flood-inundation maps for selected communities of Louisiana that experienced flooding in August 2016. Digital datasets of the inundation area, modeling boundary, water depth rasters, and final map products are available online.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175005","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Watson, K.M., Storm, J.B., Breaker, B.K., and Rose, C.E., 2017, Characterization of peak streamflows and flood inundation of selected areas in Louisiana from the August 2016 flood: U.S. Geological Survey Scientific Investigations Report 2017–5005, 26 p., https://doi.org/10.3133/sir20175005.","productDescription":"Report: v, 26 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-081535","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":334119,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5005/sir20175005.pdf","text":"Report","size":"7.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5005"},{"id":334118,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5005/coverthb2.jpg"},{"id":334120,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79K48C1","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Flood Inundation Extent and Depth in Selected Areas of Louisiana in August 2016"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.3123779296875,\n 28.859107573773\n ],\n [\n -93.3123779296875,\n 31.475524020001806\n ],\n [\n -89.879150390625,\n 31.475524020001806\n ],\n [\n -89.879150390625,\n 28.859107573773\n ],\n [\n -93.3123779296875,\n 28.859107573773\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
401 Hardin Road
Little Rock, AR 72211
For this study, a methodology was developed for assessing impacts of wind energy generation on populations of birds and bats at regional to national scales. The approach combines existing methods in applied ecology for prioritizing species in terms of their potential risk from wind energy facilities and estimating impacts of fatalities on population status and trend caused by collisions with wind energy infrastructure. Methods include a qualitative prioritization approach, demographic models, and potential biological removal. The approach can be used to prioritize species in need of more thorough study as well as to identify species with minimal risk. However, the components of this methodology require simplifying assumptions and the data required may be unavailable or of poor quality for some species. These issues should be carefully considered before using the methodology. The approach will increase in value as more data become available and will broaden the understanding of anthropogenic sources of mortality on bird and bat populations.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Wind energy and wildlife interactions","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer International Publishing","doi":"10.1007/978-3-319-51272-3_4","usgsCitation":"Diffendorfer, J., Beston, J.A., Merrill, M., Stanton, J.C., Corum, M., Loss, S., Thogmartin, W.E., Johnson, D.H., Erickson, R.A., and Heist, K.W., 2017, A method to assess the population-level consequences of wind energy facilities on bird and bat species, chap. of Wind energy and wildlife interactions, p. 65-76, https://doi.org/10.1007/978-3-319-51272-3_4.","productDescription":"12 p.","startPage":"65","endPage":"76","ipdsId":"IP-073352","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":339790,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-03","publicationStatus":"PW","scienceBaseUri":"58f5d43de4b0f2e20545e405","contributors":{"authors":[{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":649358,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beston, Julie A. jbeston@usgs.gov","contributorId":5673,"corporation":false,"usgs":true,"family":"Beston","given":"Julie","email":"jbeston@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science 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wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":649364,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, Douglas H. 0000-0002-7778-6641 douglas_h_johnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7778-6641","contributorId":1387,"corporation":false,"usgs":true,"family":"Johnson","given":"Douglas","email":"douglas_h_johnson@usgs.gov","middleInitial":"H.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":649365,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":649366,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Heist, Kevin W.","contributorId":83040,"corporation":false,"usgs":false,"family":"Heist","given":"Kevin","email":"","middleInitial":"W.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":649367,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70176334,"text":"ds1019 - 2017 - Groundwater-quality data for the Madera/Chowchilla–Kings shallow aquifer study unit, 2013–14: Results from the California GAMA Program","interactions":[],"lastModifiedDate":"2017-02-06T09:46:13","indexId":"ds1019","displayToPublicDate":"2017-02-03T00:00:00","publicationYear":"2017","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":"1019","title":"Groundwater-quality data for the Madera/Chowchilla–Kings shallow aquifer study unit, 2013–14: Results from the California GAMA Program","docAbstract":"Groundwater quality in the 2,390-square-mile Madera/Chowchilla–Kings Shallow Aquifer study unit was investigated by the U.S. Geological Survey from August 2013 to April 2014 as part of the California State Water Resources Control Board Groundwater Ambient Monitoring and Assessment Program’s Priority Basin Project. The study was designed to provide a statistically unbiased, spatially distributed assessment of untreated groundwater quality in the shallow aquifer systems of the Madera, Chowchilla, and Kings subbasins of the San Joaquin Valley groundwater basin. The shallow aquifer system corresponds to the part of the aquifer system generally used by domestic wells and is shallower than the part of the aquifer system generally used by public-supply wells. This report presents the data collected for the study and a brief preliminary description of the results.
Groundwater samples were collected from 77 wells and were analyzed for organic constituents, inorganic constituents, selected isotopic and age-dating tracers, and microbial indicators. Most of the wells sampled for this study were private domestic wells. Unlike groundwater from public-supply wells, the groundwater from private domestic wells is not regulated for quality in California and is rarely analyzed for water-quality constituents. To provide context for the sampling results, however, concentrations of constituents measured in the untreated groundwater were compared with regulatory and non-regulatory benchmarks established for drinking-water quality by the U.S. Environmental Protection Agency, the State of California, and the U.S. Geological Survey.
Of the 319 organic constituents assessed in this study (90 volatile organic compounds and 229 pesticides and pesticide degradates), 17 volatile organic compounds and 23 pesticides and pesticide degradates were detected in groundwater samples; concentrations of all but 2 were less than the respective benchmarks. The fumigants 1,2-dibromo-3-chloropropane (DBCP) and 1,2-dibromoethane (EDB) were detected at concentrations above their respective regulatory benchmarks in samples from a total of four wells.
Most detections of inorganic constituents were at concentrations or activities less than the respective benchmark levels. Five inorganic constituents were detected in groundwater samples from one or more wells at concentrations or activities greater than their respective regulatory, health-based benchmarks: arsenic, uranium, nitrate, adjusted gross alpha particle activity, and gross beta particle activity. Four inorganic constituents were detected in samples from one or more wells at concentrations or activities greater than their respective non-regulatory, health-based benchmarks: manganese, molybdenum, vanadium, and radon-222. Three inorganic constituents were detected in groundwater samples from one or more wells at concentrations greater than their respective non-regulatory, aesthetic-based benchmarks: iron, sulfate, and total dissolved solids.
Microbial indicators (Escherichia coli, total coliform, and enterococci) were analyzed for presence or absence. The presence of Escherichia coli (E. coli) was not detected; the presence of total coliform was detected in samples from 10 of the 72 grid wells for which it was analyzed, and the presence of enterococci was detected in samples from 5 of the 73 grid wells analyzed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1019","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Shelton, J.L., and Fram, M.S., 2017, Groundwater-quality data for the Madera/Chowchilla–Kings shallow aquifer study unit, 2013–14: Results from the California GAMA Program: U.S. Geological Survey Data Series 1019, 115 p., https://doi.org/10.3133/ds1019.","productDescription":"Report: viii, 115 p.","numberOfPages":"128","onlineOnly":"N","ipdsId":"IP-056132","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":334554,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1019/ds1019.pdf","text":"Report","size":"3.67 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1019"},{"id":334553,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1019/coverthb2.jpg"}],"country":"United States","state":"California","otherGeospatial":"Madera/Chowchilla-Kings Shallow Aquifer study unit","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.666667,\n 37.416667\n ],\n [\n -120.666667,\n 36\n ],\n [\n -119.166667,\n 36\n ],\n [\n -119.166667,\n 37.416667\n ],\n [\n -120.666667,\n 37.416667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819