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The Surface Ocean CO2 Atlas (SOCAT) is a synthesis of quality-controlled f CO2 (fugacity of carbon dioxide) values for the global surface oceans and coastal seas with regular updates. Version 3 of SOCAT has 14.7 million f CO2 values from 3646 data sets covering the years 1957 to 2014. This latest version has an additional 4.6 million f CO2 values relative to version 2 and extends the record from 2011 to 2014. Version 3 also significantly increases the data availability for 2005 to 2013. SOCAT has an average of approximately 1.2 million surface water f CO2 values per year for the years 2006 to 2012. Quality and documentation of the data has improved. A new feature is the data set quality control (QC) flag of E for data from alternative sensors and platforms. The accuracy of surface water f CO2 has been defined for all data set QC flags. Automated range checking has been carried out for all data sets during their upload into SOCAT. The upgrade of the interactive Data Set Viewer (previously known as the Cruise Data Viewer) allows better interrogation of the SOCAT data collection and rapid creation of high-quality figures for scientific presentations. Automated data upload has been launched for version 4 and will enable more frequent SOCAT releases in the future. High-profile scientific applications of SOCAT include quantification of the ocean sink for atmospheric carbon dioxide and its long-term variation, detection of ocean acidification, as well as evaluation of coupled-climate and ocean-only biogeochemical models. Users of SOCAT data products are urged to acknowledge the contribution of data providers, as stated in the SOCAT Fair Data Use Statement. This ESSD (Earth System Science Data) “living data” publication documents the methods and data sets used for the assembly of this new version of the SOCAT data collection and compares these with those used for earlier versions of the data collection (Pfeil et al., 2013; Sabine et al., 2013; Bakker et al., 2014). 

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J.","contributorId":98872,"corporation":false,"usgs":true,"family":"Sutton","given":"Adrienne","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":652391,"contributorType":{"id":1,"text":"Authors"},"rank":84},{"text":"Tadokoro, Kazuaki","contributorId":176456,"corporation":false,"usgs":false,"family":"Tadokoro","given":"Kazuaki","email":"","affiliations":[],"preferred":false,"id":652392,"contributorType":{"id":1,"text":"Authors"},"rank":85},{"text":"Telszewski, Maciej","contributorId":176457,"corporation":false,"usgs":false,"family":"Telszewski","given":"Maciej","email":"","affiliations":[],"preferred":false,"id":652393,"contributorType":{"id":1,"text":"Authors"},"rank":86},{"text":"Tuma, Matthias","contributorId":176458,"corporation":false,"usgs":false,"family":"Tuma","given":"Matthias","email":"","affiliations":[],"preferred":false,"id":652394,"contributorType":{"id":1,"text":"Authors"},"rank":87},{"text":"van Heuven, Steven M. A. C.","contributorId":176459,"corporation":false,"usgs":false,"family":"van Heuven","given":"Steven","email":"","middleInitial":"M. A. C.","affiliations":[],"preferred":false,"id":652395,"contributorType":{"id":1,"text":"Authors"},"rank":88},{"text":"Vandemark, Douglas","contributorId":171879,"corporation":false,"usgs":false,"family":"Vandemark","given":"Douglas","email":"","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":652396,"contributorType":{"id":1,"text":"Authors"},"rank":89},{"text":"Ward, Brian","contributorId":176460,"corporation":false,"usgs":false,"family":"Ward","given":"Brian","affiliations":[],"preferred":false,"id":652397,"contributorType":{"id":1,"text":"Authors"},"rank":90},{"text":"Watson, Andrew J.","contributorId":176461,"corporation":false,"usgs":false,"family":"Watson","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":652398,"contributorType":{"id":1,"text":"Authors"},"rank":91},{"text":"Xu, Suqing","contributorId":176462,"corporation":false,"usgs":false,"family":"Xu","given":"Suqing","email":"","affiliations":[],"preferred":false,"id":652399,"contributorType":{"id":1,"text":"Authors"},"rank":92}]}} ,{"id":70177921,"text":"70177921 - 2016 - Environmental factors influence lesser scaup migration chronology and population monitoring","interactions":[],"lastModifiedDate":"2018-02-06T12:40:06","indexId":"70177921","displayToPublicDate":"2016-10-27T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Environmental factors influence lesser scaup migration chronology and population monitoring","docAbstract":"

Identifying environmental metrics specific to lesser scaup (Aythya affinis; scaup) spring migration chronology may help inform development of conservation, management and population monitoring. Our objective was to determine how environmental conditions influence spring migration of lesser scaup to assess the effectiveness of the Waterfowl Breeding Population and Habitat Survey in accurately estimating scaup populations. We first compared peak timing of mallard (Anas platyrhynchos) and scaup migration from weekly ground surveys in North Dakota, USA because the Waterfowl Breeding Population and Habitat Survey is designed to capture annual mallard migration. As predicted, we detected that peak timing of scaup and mallard migrations differed in 25 of 36 years investigated (1980–2010). We marked scaup with satellite transmitters (n = 78; 7,403 locations) at Long Point, Lake Erie, Ontario, Canada; Pool 19 of the Mississippi River, Iowa and Illinois, USA; and Presque Isle Bay, Lake Erie, Pennsylvania, USA. We tested the assumption that our marked scaup were representative of the continental population using the traditional survey area by comparing timing of migration of marked birds and scaup counted in the North Dakota Game and Fish Department survey. We detected a strong positive correlation between marked scaup and the survey data, which indicated that marked scaup were representative of the population. We subsequently used our validated sample of marked scaup to investigate the effects of annual variation in temperature, precipitation, and ice cover on spring migration chronology in the traditional and eastern survey areas of the Waterfowl Breeding Population and Habitat Survey, 2005–2010. We evaluated competing environmental models to explain variation in timing and rate of scaup migration at large-scale and local levels. Spring migration of scaup occurred earlier and faster during springs with warmer temperatures and greater precipitation, variables known to influence energy budgets and wetland availability. Our results suggest that surveys designed to index abundance of breeding mallards is imprecise for estimating scaup abundance, and inaccurate at estimating breeding population size by survey stratum.

","language":"English","publisher":"Wildlife Society","publisherLocation":"Washington, D.C.","doi":"10.1002/jwmg.21131","usgsCitation":"Finger, T.A., Afton, A.D., Schummer, M.L., Petrie, S.A., Badzinski, S.S., Johnson, M.A., Szymanski, M.L., Jacobs, K.J., Olsen, G.H., and Mitchell, M., 2016, Environmental factors influence lesser scaup migration chronology and population monitoring: Journal of Wildlife Management, v. 80, no. 8, p. 1437-1449, https://doi.org/10.1002/jwmg.21131.","productDescription":"13 p.","startPage":"1437","endPage":"1449","ipdsId":"IP-068193","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":330488,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","volume":"80","issue":"8","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-22","publicationStatus":"PW","scienceBaseUri":"5813125de4b0b5a0c12ab662","contributors":{"authors":[{"text":"Finger, Taylor A.","contributorId":176345,"corporation":false,"usgs":false,"family":"Finger","given":"Taylor","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":652253,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Afton, Alan D. 0000-0002-0436-8588 aafton@usgs.gov","orcid":"https://orcid.org/0000-0002-0436-8588","contributorId":139582,"corporation":false,"usgs":false,"family":"Afton","given":"Alan","email":"aafton@usgs.gov","middleInitial":"D.","affiliations":[{"id":368,"text":"Louisiana Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":652254,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schummer, Michael L.","contributorId":176347,"corporation":false,"usgs":false,"family":"Schummer","given":"Michael","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":652255,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Petrie, Scott A.","contributorId":141223,"corporation":false,"usgs":false,"family":"Petrie","given":"Scott","email":"","middleInitial":"A.","affiliations":[{"id":13717,"text":"Long Point Waterfowl","active":true,"usgs":false}],"preferred":false,"id":652256,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Badzinski, Shannon S.","contributorId":176348,"corporation":false,"usgs":false,"family":"Badzinski","given":"Shannon","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":652257,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Michael A.","contributorId":174789,"corporation":false,"usgs":false,"family":"Johnson","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":652258,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Szymanski, Michael L.","contributorId":176349,"corporation":false,"usgs":false,"family":"Szymanski","given":"Michael","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":652259,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jacobs, Kevin J.","contributorId":176350,"corporation":false,"usgs":false,"family":"Jacobs","given":"Kevin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":652260,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Olsen, Glenn H. 0000-0002-7188-6203 golsen@usgs.gov","orcid":"https://orcid.org/0000-0002-7188-6203","contributorId":40918,"corporation":false,"usgs":true,"family":"Olsen","given":"Glenn","email":"golsen@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":652252,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mitchell, M.E.","contributorId":176351,"corporation":false,"usgs":false,"family":"Mitchell","given":"M.E.","affiliations":[],"preferred":false,"id":652285,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70177050,"text":"sir20165146 - 2016 - Application of dimensionless sediment rating curves to predict suspended-sediment concentrations, bedload, and annual sediment loads for rivers in Minnesota","interactions":[],"lastModifiedDate":"2020-02-05T13:52:44","indexId":"sir20165146","displayToPublicDate":"2016-10-27T00:00:00","publicationYear":"2016","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":"2016-5146","displayTitle":"Application of Dimensionless Sediment Rating Curves to Predict Suspended-Sediment Concentrations, Bedload, and Annual Sediment Loads for Rivers in Minnesota","title":"Application of dimensionless sediment rating curves to predict suspended-sediment concentrations, bedload, and annual sediment loads for rivers in Minnesota","docAbstract":"

Consistent and reliable sediment data are needed by Federal, State, and local government agencies responsible for monitoring water quality, planning river restoration, quantifying sediment budgets, and evaluating the effectiveness of sediment reduction strategies. Heightened concerns about excessive sediment in rivers and the challenge to reduce costs and eliminate data gaps has guided Federal and State interests in pursuing alternative methods for measuring suspended and bedload sediment. Simple and dependable data collection and estimation techniques are needed to generate hydraulic and water-quality information for areas where data are unavailable or difficult to collect.

The U.S. Geological Survey, in cooperation with the Minnesota Pollution Control Agency and the Minnesota Department of Natural Resources, completed a study to evaluate the use of dimensionless sediment rating curves (DSRCs) to accurately predict suspended-sediment concentrations (SSCs), bedload, and annual sediment loads for selected rivers and streams in Minnesota based on data collected during 2007 through 2013. This study included the application of DSRC models developed for a small group of streams located in the San Juan River Basin near Pagosa Springs in southwestern Colorado to rivers in Minnesota. Regionally based DSRC models for Minnesota also were developed and compared to DSRC models from Pagosa Springs, Colorado, to evaluate which model provided more accurate predictions of SSCs and bedload in Minnesota.

Multiple measures of goodness-of-fit were developed to assess the effectiveness of DSRC models in predicting SSC and bedload for rivers in Minnesota. More than 600 dimensionless ratio values of SSC, bedload, and streamflow were evaluated and delineated according to Pfankuch stream stability categories of “good/fair” and “poor” to develop four Minnesota-based DSRC models. The basis for Pagosa Springs and Minnesota DSRC model effectiveness was founded on measures of goodness-of-fit that included proximity of the model(s) fitted line to the 95-percent confidence intervals of the site-specific model, Nash-Sutcliffe Efficiency values, model biases, and deviation of annual sediment loads from each model to the annual sediment loads calculated from measured data.

Composite plots comparing Pagosa Springs DSRCs, Minnesota DSRCs, site-specific regression models, and measured data indicated that regionally developed DSRCs (Minnesota DSRC models) more closely approximated measured data for nearly every site. Pagosa Springs DSRC models had markedly larger exponents (slopes) when compared to the Minnesota DSRC models and the site-specific regression models, and over-represent SSC and bedload at streamflows exceeding bankfull. The Nash-Sutcliffe Efficiency values for the Minnesota DSRC model for suspended-sediment concentrations closely matched Nash-Sutcliffe Efficiency values of the site-specific regression models for 12 out of 16 sites. Nash-Sutcliffe Efficiency values associated with Minnesota DSRCs were greater than those associated with Pagosa Springs DSRCs for every site except the Whitewater River near Beaver, Minnesota site. Pagosa Springs DSRC models were less accurate than the mean of the measured data at predicting SSC values for one-half of the sites for good/fair stability sites and one-half of the sites for poor stability sites. Relative model biases were calculated and determined to be substantial (greater than 5 percent) for Pagosa Springs and Minnesota models, with Minnesota models having a lower mean model bias. For predicted annual suspended-sediment loads (SSL), the Minnesota DSRC models for good/fair and poor stream stability sites more closely approximated the annual SSLs calculated from the measured data as compared to the Pagosa Springs DSRC model.

Results of data analyses indicate that DSRC models developed using data collected in Minnesota were more effective at compensating for differences in individual stream characteristics across a variety of basin sizes and flow regimes than DSRC models developed using data collected for Pagosa Springs, Colorado. Minnesota DSRC models retained a substantial portion of the unique sediment signatures for most rivers, although deviations were observed for streams with limited sediment supply and for rivers in southeastern Minnesota, which had markedly larger regression exponents. Compared to Pagosa Springs DSRC models, Minnesota DSRC models had regression slopes that more closely matched the slopes of site-specific regression models, had greater Nash-Sutcliffe Efficiency values, had lower model biases, and approximated measured annual sediment loads more closely. The results presented in this report indicate that regionally based DSRCs can be used to estimate reasonably accurate values of SSC and bedload.

Practitioners are cautioned that DSRC reliability is dependent on representative measures of bankfull streamflow, SSC, and bedload. It is, therefore, important that samples of SSC and bedload, which will be used for estimating SSC and bedload at the bankfull streamflow, are collected over a range of conditions that includes the ascending and descending limbs of the event hydrograph. The use of DSRC models may have substantial limitations for certain conditions. For example, DSRC models should not be used to predict SSC and sediment loads for extreme streamflows, such as those that exceed twice the bankfull streamflow value because this constitutes conditions beyond the realm of current (2016) empirical modeling capability. Also, if relations between SSC and streamflow and between bedload and streamflow are not statistically significant, DSRC models should not be used to predict SSC or bedload, as this could result in large errors. For streams that do not violate these conditions, DSRC estimates of SSC and bedload can be used for stream restoration planning and design, and for estimating annual sediment loads for streams where little or no sediment data are available.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165146","collaboration":"Prepared in cooperation with the Minnesota Pollution Control Agency and the Minnesota Department of Natural Resources","usgsCitation":"Ellison, C.A., Groten, J.T., Lorenz, D.L., and Koller, K.S., 2016, Application of dimensionless sediment rating curves to predict suspended-sediment concentrations, bedload, and annual sediment loads for rivers in Minnesota (ver. 1.1, January 2020): U.S. Geological Survey Scientific Investigations Report 2016–5146, 68 p., https://dx.doi.org/10.3133/sir20165146.","productDescription":"Report: ix, 68 p.; 3 Appendix Tables","startPage":"1","endPage":"68","numberOfPages":"82","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-078848","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":330500,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5146/sir20165146_appendix_table1-3.xlsx","text":"Appendix Table 1–3","size":"13.2 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5146 Appendix Table 1–3"},{"id":372046,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2016/5146/versionHist.txt","text":"Version 1.1","size":"1.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"},{"id":372045,"rank":5,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5146/sir20165146_v1.1.pdf","text":"Report—Version 1.1","size":"5.13 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5146"},{"id":330498,"rank":2,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5146/sir20165146_appendix_table1-1.xlsx","text":"Appendix Table 1–1","size":"162 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\"}}]}","contact":"

Director, Minnesota Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, Minnesota 55112

http://mn.water.usgs.gov/

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2016-10-27","revisedDate":"2020-01-31","noUsgsAuthors":false,"publicationDate":"2016-10-27","publicationStatus":"PW","scienceBaseUri":"5813125de4b0b5a0c12ab66b","contributors":{"authors":[{"text":"Ellison, Christopher A. 0000-0002-5886-6654 cellison@usgs.gov","orcid":"https://orcid.org/0000-0002-5886-6654","contributorId":4891,"corporation":false,"usgs":true,"family":"Ellison","given":"Christopher","email":"cellison@usgs.gov","middleInitial":"A.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":651137,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Groten, Joel T. jgroten@usgs.gov","contributorId":171771,"corporation":false,"usgs":true,"family":"Groten","given":"Joel T.","email":"jgroten@usgs.gov","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":false,"id":651138,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lorenz, David L. 0000-0003-3392-4034 lorenz@usgs.gov","orcid":"https://orcid.org/0000-0003-3392-4034","contributorId":1384,"corporation":false,"usgs":true,"family":"Lorenz","given":"David","email":"lorenz@usgs.gov","middleInitial":"L.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":651139,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Koller, Karl S.","contributorId":175443,"corporation":false,"usgs":false,"family":"Koller","given":"Karl","email":"","middleInitial":"S.","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":651140,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70177894,"text":"70177894 - 2016 - Dynamic reusable workflows for ocean science","interactions":[],"lastModifiedDate":"2016-10-26T12:13:47","indexId":"70177894","displayToPublicDate":"2016-10-26T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2380,"text":"Journal of Marine Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Dynamic reusable workflows for ocean science","docAbstract":"Digital catalogs of ocean data have been available for decades, but advances in standardized services and software for catalog search and data access make it now possible to create catalog-driven workflows that automate — end-to-end — data search, analysis and visualization of data from multiple distributed sources. Further, these workflows may be shared, reused and adapted with ease. Here we describe a workflow developed within the US Integrated Ocean Observing System (IOOS) which automates the skill-assessment of water temperature forecasts from multiple ocean forecast models, allowing improved forecast products to be delivered for an open water swim event. A series of Jupyter Notebooks are used to capture and document the end-to-end workflow using a collection of Python tools that facilitate working with standardized catalog and data services. The workflow first searches a catalog of metadata using the Open Geospatial Consortium (OGC) Catalog Service for the Web (CSW), then accesses data service endpoints found in the metadata records using the OGC Sensor Observation Service (SOS) for in situ sensor data and OPeNDAP services for remotely-sensed and model data. Skill metrics are computed and time series comparisons of forecast model and observed data are displayed interactively, leveraging the capabilities of modern web browsers. The resulting workflow not only solves a challenging specific problem, but highlights the benefits of dynamic, reusable workflows in general. These workflows adapt as new data enters the data system, facilitate reproducible science, provide templates from which new scientific workflows can be developed, and encourage data providers to use standardized services. As applied to the ocean swim event, the workflow exposed problems with two of the ocean forecast products which led to improved regional forecasts once errors were corrected. While the example is specific, the approach is general, and we hope to see increased use of dynamic notebooks across the geoscience domains.","language":"English","publisher":"MDPI","doi":"10.3390/jmse4040068","usgsCitation":"Signell, R.P., Fernandez, F., and Wilcox, K., 2016, Dynamic reusable workflows for ocean science: Journal of Marine Science and Engineering, v. 4, no. 4, Article 68; 12 p., https://doi.org/10.3390/jmse4040068.","productDescription":"Article 68; 12 p.","ipdsId":"IP-079560","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470488,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse4040068","text":"Publisher Index Page"},{"id":330385,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"4","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-25","publicationStatus":"PW","scienceBaseUri":"5810c527e4b0f497e7972c1a","contributors":{"authors":[{"text":"Signell, Richard P. 0000-0003-0682-9613 rsignell@usgs.gov","orcid":"https://orcid.org/0000-0003-0682-9613","contributorId":140906,"corporation":false,"usgs":true,"family":"Signell","given":"Richard","email":"rsignell@usgs.gov","middleInitial":"P.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":652061,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fernandez, Filipe","contributorId":176280,"corporation":false,"usgs":false,"family":"Fernandez","given":"Filipe","email":"","affiliations":[],"preferred":false,"id":652062,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilcox, Kyle","contributorId":176282,"corporation":false,"usgs":false,"family":"Wilcox","given":"Kyle","email":"","affiliations":[],"preferred":false,"id":652063,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70179704,"text":"70179704 - 2016 - Food webs of the Delta, Suisun Bay and Suisun Marsh: an update on current understanding and possibilities for management","interactions":[],"lastModifiedDate":"2017-10-30T11:10:33","indexId":"70179704","displayToPublicDate":"2016-10-26T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Food webs of the Delta, Suisun Bay and Suisun Marsh: an update on current understanding and possibilities for management","docAbstract":"This paper reviews and highlights recent research findings on foodweb processes since an earlier review by Kimmerer et al. (2008). We conduct this review within a conceptual framework of the Delta-Suisun food web, which includes both temporal and spatial components. The temporal component is based on knowledge that the landscape has changed markedly from historical conditions. The spatial component of our framework acknowledges that the food web is not spatially static; it varies regionally and across habitat types within regions. The review highlights the idea of a changing baseline with respect to foodweb function. New research also indicates that interactions between habitat-specific food webs vary across the current landscape. For example, based on early work in the South Delta, the food web associated with submerged aquatic vegetation was thought to provide little support to species of concern; however, data from other regions of the estuary suggest that this conceptual model may not apply across the entire region. Habitat restoration has been proposed as a method of re-establishing historic foodweb processes to support species of concern. Benefits are likely for species that directly access such restored habitats, but are less clear for pelagic species. Several topics require attention to further improve the knowledge of food webs needed to support effective management, including: 1) synthesis of factors responsible for low pelagic biomass; 2) monitoring and research on effects of harmful algal blooms; 3) broadening the scope of long-term monitoring; 4) determining benefits of tidal wetland restoration to species of concern, including evaluations of interactions of habitat-specific food webs; and 5) interdisciplinary analysis and synthesis. The only certainty is that food webs will continue to change in response to the changes in the physical environment and new species invasions.","language":"English","publisher":"University of California at Davis John Muir Institute of the Environment and the Delta Stewardship Council","doi":"10.15447/sfews.2016v14iss3art4","usgsCitation":"Brown, L.R., Kimmerer, W.J., Conrad, L., Lesmeister, S., and Mueller-Solger, A., 2016, Food webs of the Delta, Suisun Bay and Suisun Marsh: an update on current understanding and possibilities for management: San Francisco Estuary and Watershed Science, v. 14, no. 3, p. 1-41, https://doi.org/10.15447/sfews.2016v14iss3art4.","productDescription":"41 p.","startPage":"1","endPage":"41","numberOfPages":"43","onlineOnly":"Y","ipdsId":"IP-071951","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true}],"links":[{"id":462051,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2016v14iss3art4","text":"Publisher Index Page"},{"id":333188,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Californina","otherGeospatial":" Sacramento–San Joaquin Delta, Suisun Bay, Suisun Marsh","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.67633056640624,\n 38.542795073979015\n ],\n [\n -121.74087524414064,\n 38.49874308602779\n ],\n [\n -121.8548583984375,\n 38.41378642476067\n ],\n [\n -121.9647216796875,\n 38.299636831993\n ],\n [\n -122.05673217773438,\n 38.21984085877146\n ],\n [\n -122.135009765625,\n 38.1442775452969\n ],\n [\n -122.18170166015625,\n 38.07620357665235\n 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-121.58843994140625,\n 38.46756808589088\n ],\n [\n -121.56234741210938,\n 38.50626606567193\n ],\n [\n -121.53488159179686,\n 38.541720956040386\n ],\n [\n -121.53900146484375,\n 38.58252615935333\n ],\n [\n -121.67633056640624,\n 38.542795073979015\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"3","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-11","publicationStatus":"PW","scienceBaseUri":"5879f5a9e4b0847d353f44ba","contributors":{"authors":[{"text":"Brown, Larry R. 0000-0001-6702-4531 lrbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":1717,"corporation":false,"usgs":true,"family":"Brown","given":"Larry","email":"lrbrown@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":658351,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kimmerer, Wim J.","contributorId":59169,"corporation":false,"usgs":false,"family":"Kimmerer","given":"Wim","email":"","middleInitial":"J.","affiliations":[{"id":6690,"text":"San Francisco State University","active":true,"usgs":false}],"preferred":false,"id":658352,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conrad, Louise 0000-0002-1145-7503","orcid":"https://orcid.org/0000-0002-1145-7503","contributorId":178273,"corporation":false,"usgs":false,"family":"Conrad","given":"Louise","email":"","affiliations":[],"preferred":false,"id":658353,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lesmeister, Sarah","contributorId":178274,"corporation":false,"usgs":false,"family":"Lesmeister","given":"Sarah","email":"","affiliations":[],"preferred":false,"id":658354,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mueller-Solger, Anke 0000-0002-2469-4284 amueller-solger@usgs.gov","orcid":"https://orcid.org/0000-0002-2469-4284","contributorId":178275,"corporation":false,"usgs":true,"family":"Mueller-Solger","given":"Anke","email":"amueller-solger@usgs.gov","affiliations":[],"preferred":true,"id":658355,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70177857,"text":"70177857 - 2016 - Joint analysis of geodetic and earthquake fault-plane solution data to constrain magmatic sources: A case study from Kīlauea Volcano","interactions":[],"lastModifiedDate":"2019-12-14T07:04:38","indexId":"70177857","displayToPublicDate":"2016-10-25T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Joint analysis of geodetic and earthquake fault-plane solution data to constrain magmatic sources: A case study from Kīlauea Volcano","docAbstract":"

A joint analysis of geodetic and seismic datasets from Kīlauea Volcano during a period of magmatic unrest in 2006 demonstrates the effectiveness of this combination for testing and constraining models of magma dynamics for a complex, multi-source system. At the end of 2003, Kīlauea's summit began a four-year-long period of inflation due to a surge in magma supply to the volcano. In 2006, for the first time since 1982, Kīlauea's Southwest Rift Zone (SWRZ) also experienced inflation. To investigate the characteristics of active magma sources and the nature of their interactions with faults in the SWRZ during 2006, we integrate, through Coulomb stress modeling, contemporary geodetic data from InSAR and GPS with a new catalogue of double-couple fault-plane solutions for volcano-tectonic earthquakes. We define two periods of inflation during 2006 based on the rate of deformation measured in daily GPS data, spanning February to 15 March 2006 (Period 1) and 16 March to 30 September 2006 (Period 2). InSAR data for these two periods are inverted to determine the position, change in size, and shape of inflation sources in each period. Our new models are consistent with microseismic activity from each period. They suggest that, during Period 1, deformation in the SWRZ can be explained by pressurization of magma in a spherical reservoir beneath the south caldera, and that, during Period 2, magma was also aseismically intruded farther to the southwest into the SWRZ along a sub-horizontal plane. Our Coulomb stress analysis shows that the microseismicity recorded in the SWRZ is induced by overpressurization of the south caldera reservoir, and not by magma intrusion into the SWRZ. This study highlights the importance of a joint analysis of independent geophysical datasets to fully constrain the nature of magma accumulation.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2016.09.011","usgsCitation":"Wauthier, C., Roman, D.C., and Poland, M.P., 2016, Joint analysis of geodetic and earthquake fault-plane solution data to constrain magmatic sources: A case study from Kīlauea Volcano: Earth and Planetary Science Letters, v. 455, p. 38-48, https://doi.org/10.1016/j.epsl.2016.09.011.","productDescription":"11 p.","startPage":"38","endPage":"48","ipdsId":"IP-077131","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462057,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2016.09.011","text":"Publisher Index Page"},{"id":330354,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.34942626953125,\n 19.199647272639126\n ],\n [\n -154.98687744140625,\n 19.199647272639126\n ],\n [\n -154.98687744140625,\n 19.4665922322076\n ],\n [\n -155.34942626953125,\n 19.4665922322076\n ],\n [\n -155.34942626953125,\n 19.199647272639126\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"455","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58106f97e4b0f497e796110d","contributors":{"authors":[{"text":"Wauthier, Christelle","contributorId":176224,"corporation":false,"usgs":false,"family":"Wauthier","given":"Christelle","email":"","affiliations":[],"preferred":false,"id":651948,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roman, Diana C.","contributorId":176225,"corporation":false,"usgs":false,"family":"Roman","given":"Diana","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":651949,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Poland, Michael P. 0000-0001-5240-6123 mpoland@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":146118,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","email":"mpoland@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":651947,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70177863,"text":"70177863 - 2016 - Extending ordinal regression with a latent zero-augmented beta distribution","interactions":[],"lastModifiedDate":"2016-12-01T13:19:39","indexId":"70177863","displayToPublicDate":"2016-10-25T10:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2151,"text":"Journal of Agricultural, Biological, and Environmental Statistics","active":true,"publicationSubtype":{"id":10}},"title":"Extending ordinal regression with a latent zero-augmented beta distribution","docAbstract":"Ecological abundance data are often recorded on an ordinal scale in which the lowest\ncategory represents species absence. One common example is when plant species cover\nis visually assessedwithin bounded quadrats and then assigned to pre-defined cover class\ncategories.We present an ordinal beta hurdle model that directly models ordinal category\nprobabilitieswith a biologically realistic beta-distributed latent variable.Ahurdle-at-zero\nmodel allows ecologists to explore distribution (absence) and abundance processes in an\nintegrated framework. This provides an alternative to cumulative link models when data\nare inconsistent with the assumption that the odds ofmoving into a higher category are the\nsame for all categories (proportional odds). Graphical tools and a deviance information\ncriterion were developed to assess whether a hurdle-at-zero model should be used for\ninferences rather than standard ordinal methods. Hurdle-at-zero and non-hurdle ordinal\nmodels fit to vegetation cover class data produced substantially different conclusions.The\nordinal beta hurdle model yielded more precise parameter estimates than cumulative logit\nmodels, although out-of-sample predictions were similar. The ordinal beta hurdle model\nprovides inferences directly on the latent biological variable of interest, percent cover,\nand supports exploration of more realistic ecological patterns and processes through the\nhurdle-at-zero or two-part specification.We provide JAGS code as an on-line supplement.\nSupplementary materials accompanying this paper appear on-line.","language":"English","publisher":"Springer","doi":"10.1007/s13253-016-0265-2","usgsCitation":"Irvine, K.M., Rodhouse, T., and Keren, I.N., 2016, Extending ordinal regression with a latent zero-augmented beta distribution: Journal of Agricultural, Biological, and Environmental Statistics, v. 21, no. 4, p. 619-640, https://doi.org/10.1007/s13253-016-0265-2.","productDescription":"22 p.","startPage":"619","endPage":"640","ipdsId":"IP-063501","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":330352,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"21","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-12","publicationStatus":"PW","scienceBaseUri":"58106f97e4b0f497e7961111","contributors":{"authors":[{"text":"Irvine, Kathryn M. 0000-0002-6426-940X kirvine@usgs.gov","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":2218,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","email":"kirvine@usgs.gov","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":651957,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rodhouse, T.J.","contributorId":10978,"corporation":false,"usgs":true,"family":"Rodhouse","given":"T.J.","affiliations":[],"preferred":false,"id":651958,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keren, Ilai. N.","contributorId":176229,"corporation":false,"usgs":false,"family":"Keren","given":"Ilai.","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":651959,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70174874,"text":"sir20165048 - 2016 - Assessment of hydrogeologic terrains, well-construction characteristics, groundwater hydraulics, and water-quality and microbial data for determination of surface-water-influenced groundwater supplies in West Virginia","interactions":[],"lastModifiedDate":"2016-10-24T13:52:21","indexId":"sir20165048","displayToPublicDate":"2016-10-24T10:50:00","publicationYear":"2016","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":"2016-5048","title":"Assessment of hydrogeologic terrains, well-construction characteristics, groundwater hydraulics, and water-quality and microbial data for determination of surface-water-influenced groundwater supplies in West Virginia","docAbstract":"

In January 2014, a storage tank leaked, spilling a large quantity of 4-methylcyclohexane methanol into the Elk River in West Virginia and contaminating the water supply for more than 300,000 people. In response, the West Virginia Legislature passed Senate Bill 373, which requires the West Virginia Department of Health and Human Resources (WVDHHR) to assess the susceptibility and vulnerability of public surface-water-influenced groundwater supply sources (SWIGS) and surface-water intakes statewide. In response to this mandate for reassessing SWIGS statewide, the U.S. Geological Survey (USGS), in cooperation with the WVDHHR, Bureau of Public Health, Office of Environmental Health Services, compiled available data and summarized the results of previous groundwater studies to provide the WVDHHR with data that could be used as part of the process for assessing and determining SWIGS.

\n

Existing geologic, hydrologic, well-construction, water-quality, and other related data and information from previous U.S. Geological Survey (USGS) hydrogeologic studies and the USGS National Water Information System (NWIS) database, in conjunction with data from the West Virginia Bureau for Public Health (WVBPH) Department of Health and Human Resources (WVDHHR) and the West Virginia Department of Environmental Protection database and files, were collected, compiled, and analyzed to help the WVDHHR to better assess public groundwater supply wells that may meet the definition of a surface-water-influenced- groundwater supply (SWIGS).

\n

In this study, measures of intrinsic susceptibility, which are characterized by the physical properties that affect the ease with which water moves through the unsaturated zone and, subsequently, into the saturated zone within an aquifer, showed that karst limestone aquifers are the aquifers most intrinsically susceptible to contamination within the State of West Virginia. Karst limestone aquifers are present within Cambrian- and Ordovician-age formations within West Virginia’s eastern panhandle and in Mississippian-age limestones within the Greenbrier River valley. Solution development within these limestone aquifers allows rapid recharge and flow of groundwater within the aquifer, both of which allow surface contaminants to easily enter the aquifer and travel long distances in a short period of time.

\n

Alluvial aquifers bordering the Ohio River in western West Virginia are also potentially highly susceptible to contamination because these alluvial aquifers can receive significant recharge from the adjacent Ohio River. Any potential contaminants that may be present in the river have the potential to enter the aquifer and contaminate wells completed within the sand and gravel alluvial sediments within which the wells are completed. These same alluvial sediments, however, help to retard the movement of bacteria and other potentially pathogenic organisms, such as Cryptosporidia and Giardia lamblia, into the aquifer. As a result, samples from alluvial aquifers bordering the Ohio River and elsewhere within the State do not commonly test positive for indicator bacteria, such as total coliform, fecal coliform, or Escherichia coli (E. coli). The alluvial sediments do not, however, provide assimilative capacity with respect to water soluble compounds such as nitrate and certain volatile and semi-volatile organic compounds. Therefore, the Ohio River alluvial aquifers are highly susceptible to organic compounds present in the river or on the land surface near a well. These aquifers are also susceptible to nitrate contamination from fertilizers, pesticides, and manure, which are commonly used on the fertile agricultural soils present on terraces along the Ohio River.

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Abandoned-coal-mine aquifers, which are typically used as a source of groundwater in southern West Virginia, are moderately susceptible to contamination. The vast network of voids from mine entries provide vast storage for groundwater in abandoned mine aquifers, and fracturing of overburden strata, which is common in areas of past or current mining, can allow rapid infiltration of contaminants to the aquifer. Where streams cross over below-drainage underground coal mines, there is an increased potential for contamination of coal-mine aquifers. As a result, above-drainage underground coal mines, those mines that are present at an elevation above local tributary drainage, are probably less susceptible to contamination than are below-drainage underground coal mines. Public groundwater supplies in abandoned coal mines need to be evaluated on a case-by-case basis to assess the potential for recharge of contaminated surface water to enter below-drainage underground coal-mine aquifers and to assess potential hydraulic conductivity to nearby surface-water bodies, such as lakes, ponds, rivers, or streams.

\n

Fractured-rock aquifers compose an additional major type of aquifer within the State of West Virginia. Owing to their low permeability and their typically small groundwater capture areas, fractured-rock aquifers within the State of West Virginia typically have low susceptibility to contamination. However, there are exceptions, and wells completed in fractured-rock aquifers that are in close proximity to streams may be adversely affected by induced recharge from the stream. Where such systems are present, frequent bacterial testing of the source water can be used to ascertain the potential for microbial contamination of the aquifer.

\n

Intrinsic susceptibility alone does not fully predict whether or not a well is vulnerable to contamination, only that the hydrogeologic terrain is suitable for rapid transport of pathogenic organisms or chemical compounds to and within the aquifer. However, contaminants may or may not be present in the recharge water to an individual well or well field. Therefore, an assessment of potential contaminant sources, such as nearby gas wells, landfills, underground storage tanks, above ground storage tanks, major transportation corridors, surface or underground coal mines, and flood plains, is needed to assess vulnerability. The assessments need to be conducted on a case-by-case basis or, as has been done in this study, by collecting and compiling the number of potential contaminant sources that may be present in the source-water-protection area for an individual public groundwater supply source.

\n

Groundwater public-supply systems in areas of high intrinsic susceptibility and with a large number of potential contaminant sources within the recharge or source-water-protection area of individual wells or well fields are potentially vulnerable to contamination and probably warrant further evaluation as potential SWIGS. However, measures can be taken to educate the local population and initiate safety protocols and protective strategies to appropriately manage contaminant sources to prevent release of contaminants to the aquifer, therefore, reducing vulnerability of these systems to contamination. However, each public groundwater supply source needs to be assessed on an individual basis. Data presented in this report can be used to categorize and prioritize wells and springs that have a high potential for intrinsic susceptibility or vulnerability to contamination.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165048","collaboration":"Prepared in cooperation with the West Virginia Department of Health and Human Resources, Bureau of Public Health, Office of Environmental Health Services","usgsCitation":"Kozar, M.D., and Paybins, K.S., 2016, Assessment of hydrogeologic terrains, well-construction characteristics, groundwater hydraulics, and water-quality and microbial data for determination of surface-water-influenced groundwater supplies in West Virginia (ver. 1.1, October 2016): U.S. Geological Survey Scientific Investigations Report 2016–5048, 55 p., https://dx.doi.org/10.3133/sir20165048.","productDescription":"Report: vii, 54 p.; 2 Figures; 3 Appendixes","numberOfPages":"67","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-065870","costCenters":[{"id":642,"text":"West Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":325448,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048_figure3A.pdf","text":"Figure 3A -","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"Major Geologic Formations in West Virginia"},{"id":325450,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048_appendix1.xlsx","text":"Appendix 1 - ","size":"168 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"Description of 324 wells in West Virginia sampled as part of the U.S. Geological Survey and West Virginia Department of Environmental Protection statewide Ambient Groundwater Quality Monitoring Network"},{"id":325449,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048_figure3B.pdf","text":"Figure 3B -","size":"745 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"Major geologic formations in the study area of the Blue Ridge Physiographic Province USGS National Water Quality Assessment study in Virginia and North Carolina"},{"id":325446,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5048/coverthb2.jpg"},{"id":325452,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048_appendix3.xlsx","text":"Appendix 3 - ","size":"111 KB","linkHelpText":" Permit data for public groundwater supplies in West Virginia with accompanying counts of number of potential sources of contamination within the respective source-water-protection area for each public groundwater supply source"},{"id":325447,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048.pdf","text":"Report","size":"25.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5048"},{"id":325451,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048_appendix2.xlsx","text":"Appendix 2 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Virginia\",\"nation\":\"USA \"}}]}","edition":"Version 1.0: Originally posted August 30, 2016; Version 1.1: October 24, 2016","contact":"

Director, West Virginia Water Science Center
U.S. Geological Survey
11 Dunbar Street
Charleston, WV 25301
http://wv.usgs.gov

","tableOfContents":"","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2016-08-30","revisedDate":"2016-10-24","noUsgsAuthors":false,"publicationDate":"2016-08-30","publicationStatus":"PW","scienceBaseUri":"57c6a026e4b0f2f0cebdafb8","contributors":{"authors":[{"text":"Kozar, Mark D. 0000-0001-7755-7657 mdkozar@usgs.gov","orcid":"https://orcid.org/0000-0001-7755-7657","contributorId":1963,"corporation":false,"usgs":true,"family":"Kozar","given":"Mark","email":"mdkozar@usgs.gov","middleInitial":"D.","affiliations":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"preferred":true,"id":642941,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paybins, Katherine S. 0000-0002-3967-5043 kpaybins@usgs.gov","orcid":"https://orcid.org/0000-0002-3967-5043","contributorId":2805,"corporation":false,"usgs":true,"family":"Paybins","given":"Katherine","email":"kpaybins@usgs.gov","middleInitial":"S.","affiliations":[{"id":642,"text":"West Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":642942,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70229792,"text":"70229792 - 2016 - Estimating denning date of wolves with daily movement and GPS location fix failure","interactions":[],"lastModifiedDate":"2022-03-17T15:35:53.525913","indexId":"70229792","displayToPublicDate":"2016-10-22T10:24:35","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Estimating denning date of wolves with daily movement and GPS location fix failure","docAbstract":"

We used Global Positioning System (GPS) radiotelemetry data from 7 breeding female wolves (Canis lupus; n = 14 dennings) in 3 regions across Alaska, USA, during 2008–2011 to develop and compare methods for estimating the onset of denning, and thus infer timing of parturition. We developed and tested 2 estimators based on a combination of GPS radiocollar location-fix failure and distance traveled between locations. We developed a quantitative method employing Generalized Additive Models to smooth time series of wolf data to estimate denning onset. In contrast, 3 study authors with first-hand experience with the study wolves implemented a subjective method of estimating denning onset by visual inspection of detection and distance traveled data. We then tested the visual method for repeatability by subjecting it to 10 wolf experts not associated with this study. Side-by-side comparison of estimators indicates that denning onset can be precisely measured using GPS detection success and distance traveled. Furthermore, the visual-inspection method was simple and rapid to implement and yielded more accurate (relative to assumed dates of denning onset) and precise results compared to the quantitative estimator. Although the Generalized Additive Model based approach had the advantage of estimating denning onset objectively following a set of prescribed rules in a statistical inferential framework, we found the method required significant technical capacity to implement and did not represent an improvement over simple visual-inspection-based estimates of denning onset.

","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.703","usgsCitation":"Walsh, P.B., Sethi, S., Lake, B.C., Mangipane, B.A., Nielson, R., and Lowe, S., 2016, Estimating denning date of wolves with daily movement and GPS location fix failure: Wildlife Society Bulletin, v. 40, no. 4, p. 663-668, https://doi.org/10.1002/wsb.703.","productDescription":"6 p.","startPage":"663","endPage":"668","ipdsId":"IP-127033","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":499855,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/80d297d6da034496be9fafe4301c7b9e","text":"External Repository"},{"id":397250,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Lake Clark National Park, Togiak National Wildlife Refuge, Yukon Flats National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -161.12548828125,\n 59.26307320436287\n ],\n [\n -159.0875244140625,\n 59.251841195085\n ],\n [\n -159.136962890625,\n 59.45624336447568\n ],\n [\n -159.246826171875,\n 59.81444699201482\n ],\n [\n -159.356689453125,\n 60.141504734793386\n ],\n [\n -159.8675537109375,\n 60.1141451165239\n ],\n [\n -160.433349609375,\n 60.048389180435564\n ],\n [\n -160.784912109375,\n 59.92474296688904\n ],\n [\n -160.9661865234375,\n 59.795107566022416\n ],\n [\n -161.15844726562497,\n 59.86136748351594\n ],\n [\n -161.553955078125,\n 59.86136748351594\n ],\n [\n -161.5704345703125,\n 59.77852198502987\n ],\n [\n -161.4715576171875,\n 59.775756919903266\n ],\n [\n -161.488037109375,\n 59.60943336371317\n ],\n [\n -161.378173828125,\n 59.60943336371317\n ],\n [\n -161.3616943359375,\n 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59.91648345450877\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"4","noUsgsAuthors":false,"publicationDate":"2016-10-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Walsh, Patrick B.","contributorId":288777,"corporation":false,"usgs":false,"family":"Walsh","given":"Patrick","email":"","middleInitial":"B.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":838275,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sethi, Suresh 0000-0002-0053-1827 ssethi@usgs.gov","orcid":"https://orcid.org/0000-0002-0053-1827","contributorId":191424,"corporation":false,"usgs":true,"family":"Sethi","given":"Suresh","email":"ssethi@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":838274,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lake, Bryce C.","contributorId":288778,"corporation":false,"usgs":false,"family":"Lake","given":"Bryce","email":"","middleInitial":"C.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":838276,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mangipane, Buck A.","contributorId":288781,"corporation":false,"usgs":false,"family":"Mangipane","given":"Buck","email":"","middleInitial":"A.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":838277,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nielson, Ryan","contributorId":288785,"corporation":false,"usgs":false,"family":"Nielson","given":"Ryan","affiliations":[{"id":49982,"text":"WEST, Inc.","active":true,"usgs":false}],"preferred":false,"id":838278,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lowe, Stacey","contributorId":288787,"corporation":false,"usgs":false,"family":"Lowe","given":"Stacey","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":838279,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70177783,"text":"70177783 - 2016 - Statistical correction of lidar-derived digital elevation models with multispectral airborne imagery in tidal marshes","interactions":[],"lastModifiedDate":"2016-10-21T09:45:36","indexId":"70177783","displayToPublicDate":"2016-10-21T10:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Statistical correction of lidar-derived digital elevation models with multispectral airborne imagery in tidal marshes","docAbstract":"

Airborne light detection and ranging (lidar) is a valuable tool for collecting large amounts of elevation data across large areas; however, the limited ability to penetrate dense vegetation with lidar hinders its usefulness for measuring tidal marsh platforms. Methods to correct lidar elevation data are available, but a reliable method that requires limited field work and maintains spatial resolution is lacking. We present a novel method, the Lidar Elevation Adjustment with NDVI (LEAN), to correct lidar digital elevation models (DEMs) with vegetation indices from readily available multispectral airborne imagery (NAIP) and RTK-GPS surveys. Using 17 study sites along the Pacific coast of the U.S., we achieved an average root mean squared error (RMSE) of 0.072 m, with a 40–75% improvement in accuracy from the lidar bare earth DEM. Results from our method compared favorably with results from three other methods (minimum-bin gridding, mean error correction, and vegetation correction factors), and a power analysis applying our extensive RTK-GPS dataset showed that on average 118 points were necessary to calibrate a site-specific correction model for tidal marshes along the Pacific coast. By using available imagery and with minimal field surveys, we showed that lidar-derived DEMs can be adjusted for greater accuracy while maintaining high (1 m) resolution.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2016.09.020","usgsCitation":"Buffington, K., Dugger, B.D., Thorne, K.M., and Takekawa, J.Y., 2016, Statistical correction of lidar-derived digital elevation models with multispectral airborne imagery in tidal marshes: Remote Sensing of Environment, v. 186, p. 616-625, https://doi.org/10.1016/j.rse.2016.09.020.","productDescription":"10 p.","startPage":"616","endPage":"625","ipdsId":"IP-079777","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":470492,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2016.09.020","text":"Publisher Index Page"},{"id":438533,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GJFZHT","text":"USGS data release","linkHelpText":"LEAN-Corrected Collier County DEM for wetlands"},{"id":438532,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NQZXU3","text":"USGS data release","linkHelpText":"LEAN-Corrected Chesapeake Bay Digital Elevation Models, 2019"},{"id":438531,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97R4ES3","text":"USGS data release","linkHelpText":"LEAN-Corrected DEM for Suisun Marsh"},{"id":330288,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"186","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5810c528e4b0f497e7972c1e","chorus":{"doi":"10.1016/j.rse.2016.09.020","url":"http://dx.doi.org/10.1016/j.rse.2016.09.020","publisher":"Elsevier BV","authors":"Buffington Kevin J., Dugger Bruce D., Thorne Karen M., Takekawa John Y.","journalName":"Remote Sensing of Environment","publicationDate":"12/2016"},"contributors":{"authors":[{"text":"Buffington, Kevin J. 0000-0001-9741-1241 kbuffington@usgs.gov","orcid":"https://orcid.org/0000-0001-9741-1241","contributorId":4775,"corporation":false,"usgs":true,"family":"Buffington","given":"Kevin","email":"kbuffington@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":651789,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dugger, Bruce D.","contributorId":176167,"corporation":false,"usgs":false,"family":"Dugger","given":"Bruce","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":651790,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thorne, Karen M. 0000-0002-1381-0657 kthorne@usgs.gov","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":4191,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen","email":"kthorne@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":651788,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Takekawa, John Y. 0000-0003-0217-5907 john_takekawa@usgs.gov","orcid":"https://orcid.org/0000-0003-0217-5907","contributorId":176168,"corporation":false,"usgs":true,"family":"Takekawa","given":"John","email":"john_takekawa@usgs.gov","middleInitial":"Y.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":651791,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70177683,"text":"sir20165035 - 2016 - Simulated effects of groundwater withdrawals from aquifers in Ocean County and vicinity, New Jersey","interactions":[],"lastModifiedDate":"2016-12-16T09:51:41","indexId":"sir20165035","displayToPublicDate":"2016-10-21T02:15:00","publicationYear":"2016","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":"2016-5035","title":"Simulated effects of groundwater withdrawals from aquifers in Ocean County and vicinity, New Jersey","docAbstract":"

Rapid population growth since the 1930s in Ocean County and vicinity, New Jersey, has placed increasing demands upon the area’s freshwater resources. To examine effects of groundwater withdrawals, a three-dimensional groundwater-flow model was developed to simulate the groundwater-flow systems of five area aquifers: the unconfined Kirkwood-Cohansey aquifer system and Vincentown aquifer, and three confined aquifers— the Rio Grande water-bearing zone, the Atlantic City 800-foot sand, and the Piney Point aquifer. The influence of withdrawals is evaluated by using transient groundwater-flow model simulations that incorporate three withdrawal schemes. These are (1) no-withdrawal conditions; (2) 2000–03 withdrawal conditions, using reported monthly withdrawals at all production wells from January 2000 through December 2003; and (3) maximum-allocation withdrawal conditions using the maximum withdrawal allowed by New Jersey Department of Environmental Protection permits at each well. Particle tracking analysis, using results from model simulations, delineated particle flow paths from production wells to the point of recharge, and estimated particle travel times.

Compared with no-withdrawal conditions, 2000–03 withdrawal conditions reduced the amount of groundwater flow out of the Kirkwood-Cohansey aquifer system into streams, increased the net flow of water into other layers, reduced net flow into or out of storage, and reduced flow from the Kirkwood-Cohansey aquifer system to constant head cells.

Freshwater discharging to the Barnegat Bay-Little Egg Harbor estuary from streams and groundwater is essential to maintaining the ecology of the bay. Examination of selected stress periods indicates that simulated base flow in streams flowing into the Barnegat Bay-Little Egg Harbor estuary is reduced by as much as 49 cubic feet per second for 2000 to 2003 withdrawal conditions when compared with no-withdrawal conditions.

In the three confined aquifers, water levels during periods of low recharge and high withdrawals, and high recharge and low withdrawals, were examined to determine seasonal effects on the confined flow systems. The simulated potentiometric surface of the Rio Grande water-bearing zone and the Atlantic City 800-foot sand during selected stress periods indicates substantial declines from no-withdrawal conditions to 2000–03 conditions as a result of groundwater withdrawals. Cones of depression in Toms River Township, Seaside Heights and Seaside Park Boroughs, and Barnegat Light Borough developed in the potentiometric surface of the Piney Point aquifer in response to withdrawals.

Maximum-allocation withdrawals decreased flow out of the Kirkwood-Cohansey aquifer system to constant head cells, increased flow out of the aquifer system to adjacent and lower layers, and reduced groundwater discharge to streams when compared with 2000–03 withdrawal conditions. Increases in withdrawals from the Rio Grande water-bearing zone, the Atlantic City 800-foot sand, and the Piney Point aquifer result in an increase in simulated net groundwater flow into these aquifers. Base-flow reduction from 2000–03 conditions to maximum-allocation conditions of 25 to 29 cubic feet per second in all streams draining to the Barnegat Bay-Little Egg Harbor also is indicated. Potentiometric surfaces of the Rio Grande water-bearing zone, Atlantic City 800-foot sand, and the Piney Point aquifer during two stress periods of simulated maximum-allocation withdrawal conditions indicated the expansion of several cones of depression developed during 2000–03 withdrawals.

Simulation of average 2000–03 withdrawal conditions indicated the extent to which the groundwater-flow system is susceptible to potential saltwater intrusion into near-shore wells. Travel time from recharge to discharge location ranged from 11 to approximately 50,700 years in near-shore Kirkwood-Cohansey aquifer system wells. Those in Seaside Heights Borough, in Island Beach State Park (Berkeley Township), and in Ship Bottom Borough have particle travel times from 140 to 12,000 years and flow paths that originated under Barnegat Bay or the Atlantic Ocean from the simulation of average maximum-allocation withdrawal conditions.

Travel time along flow paths to wells screened in the Rio Grande water-bearing zone and the Atlantic City 800-foot sand from recharge to discharge point ranged from nearly 530 years to greater than 3.73 million years from the simulation of average 2000–03 withdrawal conditions. Particle tracking indicated that most wells screened in these aquifers derived a large part of their recharge from the Oswego River Basin, with a small portion of flow originating either beneath Barnegat Bay or to the east beneath the Atlantic Ocean. Travel time along flow paths that start beneath either Barnegat Bay or the Atlantic Ocean ranged from 2,300 to approximately 134,000 years from the simulation of average maximum-allocation withdrawal conditions.\"

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165035","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Cauller, S.J., Voronin, L.M., and Chepiga, M.M., 2016, Simulated effects of groundwater withdrawals from aquifers in Ocean County and vicinity, New Jersey: U.S. Geological Survey Scientific Investigations Report 2016–5035, 77 p., https://dx.doi.org/10.3133/sir20165035.","productDescription":"x, 77 p.","numberOfPages":"92","onlineOnly":"Y","ipdsId":"IP-020396","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":438535,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7H99392","text":"USGS data release","linkHelpText":"MODFLOW2005 model used to simulate the effects of groundwater withdrawals from aquifers in Ocean County and vicinity, New Jersey"},{"id":330264,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5035/coverthb.jpg"},{"id":330265,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5035/sir20165035.pdf","text":"Report","size":"47.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5035 Report"},{"id":332215,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7H99392"}],"country":"United States","state":"New Jersey","county":"Ocean County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.03549194335938,\n 40.10118506258701\n ],\n [\n -74.04647827148438,\n 40.10538669840983\n ],\n [\n -74.0643310546875,\n 40.092781012494065\n ],\n [\n -74.09042358398438,\n 40.10013461308659\n ],\n [\n -74.08905029296875,\n 40.12219064672336\n ],\n [\n -74.19754028320312,\n 40.20195268954057\n ],\n [\n -74.33212280273438,\n 40.22397567550632\n ],\n [\n -74.55459594726562,\n 40.08122374895389\n ],\n [\n -74.53399658203125,\n 39.68393975392731\n ],\n [\n -74.45709228515625,\n 39.577114881737586\n ],\n [\n -74.4158935546875,\n 39.536880650643056\n ],\n [\n -74.410400390625,\n 39.49874248613119\n ],\n [\n -74.3609619140625,\n 39.487084981687495\n ],\n [\n -74.32525634765625,\n 39.49556336059472\n ],\n [\n -74.2401123046875,\n 39.46058338433589\n ],\n [\n -74.02999877929686,\n 39.74521015328692\n ],\n [\n -73.9764404296875,\n 40.091730433255\n ],\n [\n -74.03549194335938,\n 40.10118506258701\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
http://nj.usgs.gov

","tableOfContents":"


","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2016-10-21","noUsgsAuthors":false,"publicationDate":"2016-10-21","publicationStatus":"PW","scienceBaseUri":"5810c528e4b0f497e7972c20","contributors":{"authors":[{"text":"Cauller, Stephen J. 0000-0002-1823-8813 sjcaulle@usgs.gov","orcid":"https://orcid.org/0000-0002-1823-8813","contributorId":3641,"corporation":false,"usgs":true,"family":"Cauller","given":"Stephen","email":"sjcaulle@usgs.gov","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":false,"id":651597,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Voronin, Lois M. 0000-0002-1064-1675 lvoronin@usgs.gov","orcid":"https://orcid.org/0000-0002-1064-1675","contributorId":1475,"corporation":false,"usgs":true,"family":"Voronin","given":"Lois","email":"lvoronin@usgs.gov","middleInitial":"M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":651596,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chepiga, Mary M. mchepiga@usgs.gov","contributorId":888,"corporation":false,"usgs":true,"family":"Chepiga","given":"Mary M.","email":"mchepiga@usgs.gov","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":false,"id":651598,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70177776,"text":"70177776 - 2016 - Importance of regional variation in conservation planning: A rangewide example of the Greater Sage-Grouse","interactions":[],"lastModifiedDate":"2016-10-20T16:27:31","indexId":"70177776","displayToPublicDate":"2016-10-20T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Importance of regional variation in conservation planning: A rangewide example of the Greater Sage-Grouse","docAbstract":"

We developed rangewide population and habitat models for Greater Sage-Grouse (Centrocercus urophasianus) that account for regional variation in habitat selection and relative densities of birds for use in conservation planning and risk assessments. We developed a probabilistic model of occupied breeding habitat by statistically linking habitat characteristics within 4 miles of an occupied lek using a nonlinear machine learning technique (Random Forests). Habitat characteristics used were quantified in GIS and represent standard abiotic and biotic variables related to sage-grouse biology. Statistical model fit was high (mean correctly classified = 82.0%, range = 75.4–88.0%) as were cross-validation statistics (mean = 80.9%, range = 75.1–85.8%). We also developed a spatially explicit model to quantify the relative density of breeding birds across each Greater Sage-Grouse management zone. The models demonstrate distinct clustering of relative abundance of sage-grouse populations across all management zones. On average, approximately half of the breeding population is predicted to be within 10% of the occupied range. We also found that 80% of sage-grouse populations were contained in 25–34% of the occupied range within each management zone. Our rangewide population and habitat models account for regional variation in habitat selection and the relative densities of birds, and thus, they can serve as a consistent and common currency to assess how sage-grouse habitat and populations overlap with conservation actions or threats over the entire sage-grouse range. We also quantified differences in functional habitat responses and disturbance thresholds across the Western Association of Fish and Wildlife Agencies (WAFWA) management zones using statistical relationships identified during habitat modeling. Even for a species as specialized as Greater Sage-Grouse, our results show that ecological context matters in both the strength of habitat selection (i.e., functional response curves) and response to disturbance.

","language":"English","publisher":"Ecological Society of America","publisherLocation":"Washington, D.C.","doi":"10.1002/ecs2.1462","usgsCitation":"Doherty, K., Evans, J.S., Coates, P.S., Juliusson, L., and Fedy, B., 2016, Importance of regional variation in conservation planning: A rangewide example of the Greater Sage-Grouse: Ecosphere, v. 7, no. 10, e01462: 27 p., https://doi.org/10.1002/ecs2.1462.","productDescription":"e01462: 27 p.","ipdsId":"IP-073764","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":470497,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1462","text":"Publisher Index Page"},{"id":330281,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"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 -123.79394531249999,\n 35.67514743608467\n ],\n [\n -123.79394531249999,\n 47.54687159892238\n ],\n [\n -101.77734374999999,\n 47.54687159892238\n ],\n [\n -101.77734374999999,\n 35.67514743608467\n ],\n [\n -123.79394531249999,\n 35.67514743608467\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"10","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-13","publicationStatus":"PW","scienceBaseUri":"5809d7c2e4b0f497e78fca46","contributors":{"authors":[{"text":"Doherty, Kevin 0000-0003-3635-7346","orcid":"https://orcid.org/0000-0003-3635-7346","contributorId":176149,"corporation":false,"usgs":false,"family":"Doherty","given":"Kevin","email":"","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":true,"id":651763,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evans, Jeffrey S.","contributorId":171756,"corporation":false,"usgs":false,"family":"Evans","given":"Jeffrey","email":"","middleInitial":"S.","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":651764,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":651762,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Juliusson, Lara","contributorId":171757,"corporation":false,"usgs":false,"family":"Juliusson","given":"Lara","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":651765,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fedy, Bradley C.","contributorId":40536,"corporation":false,"usgs":true,"family":"Fedy","given":"Bradley C.","affiliations":[],"preferred":false,"id":651766,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70177753,"text":"70177753 - 2016 - Incorporating imperfect detection into joint models of communites: A response to Warton et al.","interactions":[],"lastModifiedDate":"2016-10-20T11:48:30","indexId":"70177753","displayToPublicDate":"2016-10-20T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3653,"text":"Trends in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating imperfect detection into joint models of communites: A response to Warton et al.","docAbstract":"

Warton et al. [1] advance community ecology by describing a statistical framework that can jointly model abundances (or distributions) across many taxa to quantify how community properties respond to environmental variables. This framework specifies the effects of both measured and unmeasured (latent) variables on the abundance (or occurrence) of each species. Latent variables are random effects that capture the effects of both missing environmental predictors and correlations in parameter values among different species. As presented in Warton et al., however, the joint modeling framework fails to account for the common problem of detection or measurement errors that always accompany field sampling of abundance or occupancy, and are well known to obscure species- and community-level inferences.

","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.tree.2016.07.009","collaboration":"Steven R. Beissinger1, Kelly J. Iknayan1, Elise F. Zipkin2, Robert M. Dorazio3, J. Andrew Royle4, and Marc Kéry5.","usgsCitation":"Beissinger, S.R., Iknayan, K.J., Guillera-Arroita, G., Zipkin, E., Dorazio, R., Royle, A., and Kery, M., 2016, Incorporating imperfect detection into joint models of communites: A response to Warton et al.: Trends in Ecology and Evolution, v. 31, no. 10, p. 736-737, https://doi.org/10.1016/j.tree.2016.07.009.","productDescription":"2 p.","startPage":"736","endPage":"737","ipdsId":"IP-076941","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":470496,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/content/qt161109f9/qt161109f9.pdf","text":"External Repository"},{"id":330241,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"10","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5809d7c2e4b0f497e78fca4b","contributors":{"authors":[{"text":"Beissinger, Steven R.","contributorId":100534,"corporation":false,"usgs":true,"family":"Beissinger","given":"Steven","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":651646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iknayan, Kelly J.","contributorId":77835,"corporation":false,"usgs":true,"family":"Iknayan","given":"Kelly","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":651647,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guillera-Arroita, Gurutzeta","contributorId":149296,"corporation":false,"usgs":false,"family":"Guillera-Arroita","given":"Gurutzeta","email":"","affiliations":[{"id":13336,"text":"University of Melbourne","active":true,"usgs":false}],"preferred":false,"id":651648,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zipkin, Elise ezipkin@usgs.gov","contributorId":470,"corporation":false,"usgs":true,"family":"Zipkin","given":"Elise","email":"ezipkin@usgs.gov","affiliations":[],"preferred":true,"id":651649,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dorazio, Robert 0000-0003-2663-0468 bob_dorazio@usgs.gov","orcid":"https://orcid.org/0000-0003-2663-0468","contributorId":172151,"corporation":false,"usgs":true,"family":"Dorazio","given":"Robert","email":"bob_dorazio@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"preferred":true,"id":651650,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":651645,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kery, Marc","contributorId":168361,"corporation":false,"usgs":false,"family":"Kery","given":"Marc","affiliations":[{"id":12551,"text":"Swiss Ornithological Institute, Sempach, Switzerland","active":true,"usgs":false}],"preferred":false,"id":651651,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70177099,"text":"70177099 - 2016 - Low-δD hydration rinds in Yellowstone perlites record rapid syneruptive hydration during glacial and interglacial conditions","interactions":[],"lastModifiedDate":"2016-10-19T10:27:12","indexId":"70177099","displayToPublicDate":"2016-10-19T11:20:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1336,"text":"Contributions to Mineralogy and Petrology","active":true,"publicationSubtype":{"id":10}},"title":"Low-δD hydration rinds in Yellowstone perlites record rapid syneruptive hydration during glacial and interglacial conditions","docAbstract":"

Hydration of silicic volcanic glass forms perlite, a dusky, porous form of altered glass characterized by abundant “onion-skin” fractures. The timing and temperature of perlite formation are enigmatic and could plausibly occur during eruption, during post-eruptive cooling, or much later at ambient temperatures. To learn more about the origin of natural perlite, and to fingerprint the hydration waters, we investigated perlitic glass from several synglacial and interglacial rhyolitic lavas and tuffs from the Yellowstone volcanic system. Perlitic cores are surrounded by a series of conchoidal cracks that separate 30- to 100-µm-thick slivers, likely formed in response to hydration-induced stress. H2O and D/H profiles confirm that most D/H exchange happens together with rapid H2O addition but some smoother D/H variations may suggest separate minor exchange by deuterium atom interdiffusion following hydration. The hydrated rinds (2–3 wt% H2O) transition rapidly (within 30 µm, or by 1 wt% H2O per 10 µm) to unhydrated glass cores. This is consistent with quenched “hydration fronts” where H2O diffusion coefficients are strongly dependent on H2O concentrations. The chemical, δ18O, and δD systematics of bulk glass records last equilibrium between ~110 and 60 °C without chemical exchange but with some δ18O exchange. Similarly, the δ18O of water extracted from glass by rapid heating suggests that water was added to the glass during cooling at <200 °C. Our observations support fast hydration at temperatures as low as 60 °C; prolonged exposure to high temperature of 175°–225° during water addition is less likely as the glass would lose alkalies and should alter to clays within days. A compilation of low-temperature hydration diffusion coefficients suggests ~2 orders of magnitude higher rates of diffusion at 60–110 °C temperatures, compared with values expected from extrapolation of high-temperature (>400 °C) experimental data. The thick hydration rinds in perlites, measuring hundreds of microns, preserve the original D/H values of hydrating water as a recorder of paleoclimate conditions. Measured δD values in perlitic lavas are −150 to −191 or 20–40 ‰ lower than glass hydrated by modern Yellowstone waters. This suggests that Yellowstone perlites record the low-δD signature of glacial ice. Cooling calculations, combined with the observed high water diffusion coefficients noted for 60–150 °C, suggest that if sufficient hot water or steam is available, any rhyolite flow greater than ~5 m thick can develop the observed ~250-µm hydration rinds within the expected timescale of cooling (weeks–years). As the process of hydration involves shattering of 30- to 100-µm-thick slivers to expose unhydrated rhyolite glass, the time required for hydration may be even shorter. Rapid hydration and formation of relatively thick-walled glass shards allow perlites to provide a snapshot view of the meteoric water (and thus climate) at the time of initial alteration. Perlites retain their initial hydration D/H signal better than thin-walled ash, which in contrast hydrates over many thousands of years with time-averaged precipitation.

","language":"English","publisher":"Springer","doi":"10.1007/s00410-016-1293-1","usgsCitation":"Bindeman, I.N., and Lowenstern, J.B., 2016, Low-δD hydration rinds in Yellowstone perlites record rapid syneruptive hydration during glacial and interglacial conditions: Contributions to Mineralogy and Petrology, v. 171, p. 1-24, https://doi.org/10.1007/s00410-016-1293-1.","productDescription":"Article 89; 24 p.","startPage":"1","endPage":"24","ipdsId":"IP-075820","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":329731,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"171","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-15","publicationStatus":"PW","scienceBaseUri":"58088685e4b0f497e78e24b5","contributors":{"authors":[{"text":"Bindeman, Ilya N.","contributorId":175500,"corporation":false,"usgs":false,"family":"Bindeman","given":"Ilya","email":"","middleInitial":"N.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":651264,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lowenstern, Jacob B. 0000-0003-0464-7779 jlwnstrn@usgs.gov","orcid":"https://orcid.org/0000-0003-0464-7779","contributorId":2755,"corporation":false,"usgs":true,"family":"Lowenstern","given":"Jacob","email":"jlwnstrn@usgs.gov","middleInitial":"B.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":651263,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70177107,"text":"70177107 - 2016 - Probing the volcanic-plutonic connection and the genesis of crystal-rich rhyolite in a deeply dissected supervolcano in the Nevada Great Basin: Source of the late Eocene Caetano Tuff","interactions":[],"lastModifiedDate":"2019-11-14T11:13:42","indexId":"70177107","displayToPublicDate":"2016-10-19T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2420,"text":"Journal of Petrology","active":true,"publicationSubtype":{"id":10}},"title":"Probing the volcanic-plutonic connection and the genesis of crystal-rich rhyolite in a deeply dissected supervolcano in the Nevada Great Basin: Source of the late Eocene Caetano Tuff","docAbstract":"

Late Cenozoic faulting and large-magnitude extension in the Great Basin of the western USA has created locally deep windows into the upper crust, permitting direct study of volcanic and plutonic rocks within individual calderas. The Caetano caldera in north–central Nevada, formed during the mid-Tertiary ignimbrite flare-up, offers one of the best exposed and most complete records of caldera magmatism. Integrating whole-rock geochemistry, mineral chemistry, isotope geochemistry and geochronology with field studies and geologic mapping, we define the petrologic evolution of the magmatic system that sourced the >1100 km3Caetano Tuff. The intra-caldera Caetano Tuff is up to ∼5 km thick, composed of crystal-rich (30–45 vol. %), high-silica rhyolite, overlain by a smaller volume of comparably crystal-rich, low-silica rhyolite. It defies classification as either a monotonous intermediate or crystal-poor zoned rhyolite, as commonly ascribed to ignimbrite eruptions. Crystallization modeling based on the observed mineralogy and major and trace element geochemistry demonstrates that the compositional zonation can be explained by liquid–cumulate evolution in the Caetano Tuff magma chamber, with the more evolved lower Caetano Tuff consisting of extracted liquids that continued to crystallize and mix in the upper part of the chamber following segregation from a cumulate-rich, and more heterogeneous, source mush. The latter is represented in the caldera stratigraphy by the less evolved upper Caetano Tuff. Whole-rock major, trace and rare earth element geochemistry, modal mineralogy and mineral chemistry, O, Sr, Nd and Pb isotope geochemistry, sanidine Ar–Ar geochronology, and zircon U–Pb geochronology and trace element geochemistry provide robust evidence that the voluminous caldera intrusions (Carico Lake pluton and Redrock Canyon porphyry) are genetically equivalent to the least evolved Caetano Tuff and formed from magma that remained in the lower chamber after ignimbrite eruption and caldera collapse. Thus, the Caetano Tuff contradicts models for the mutually exclusive origins of voluminous volcanic and plutonic magmas in the upper crust. Crystal-scale O isotope data indicate that the Caetano Tuff is one of the most 18O-enriched rhyolites in the Great Basin (δ18Omagma = 10·2 ± 0·2‰), supporting anatexis of local metasedimentary basement crust. Metapelite xenoliths in the Carico Lake pluton and ubiquitous xenocrystic zircons in the Caetano Tuff provide constraints for the anatexis process; these data point to shallow (<15 km) dehydration melting of a protolith similar to the Proterozoic McCoy Creek Group siliciclastic sediments in eastern Nevada, projected beneath Caetano in fault-stacked shelf sediments that were thickened during Mesozoic crustal shortening. Mean zircon U–Pb ages for different stratigraphic levels of the intra-caldera Caetano Tuff are 34·2–34·5 Ma, 0·2–0·5 Myr older than the caldera sanidine 40Ar/39Ar age of 34·00 ± 0·03 Ma, documenting protracted duration of assembly and homogenization of isotopically diverse upper crustal melts, followed by crystallization and zonation to generate the Caetano Tuff magma chamber. Sanidine rims in the least evolved Caetano Tuff and in the Carico Lake pluton and Redrock Canyon porphyry have sharply zoned Ba domains that point to crystal growth during magmatic recharge events. The recharge magma is inferred to have been compositionally similar to the Caetano Tuff magma, with increased Ba resulting from remelting of Ba-rich sanidine cumulates. Mush reactivation to generate the Caetano Tuff eruption was sufficiently rapid to preserve compositional gradients in the intracaldera ignimbrite, calling into question models that predict homogeneity as a prerequisite for remobilizing crystal-rich ignimbrite magmas.

","language":"English","publisher":"Oxford University Press","doi":"10.1093/petrology/egw051","usgsCitation":"Watts, K.E., John, D.A., Colgan, J.P., Henry, C., Bindeman, I.N., and Schmitt, A.K., 2016, Probing the volcanic-plutonic connection and the genesis of crystal-rich rhyolite in a deeply dissected supervolcano in the Nevada Great Basin: Source of the late Eocene Caetano Tuff: Journal of Petrology, v. 57, no. 8, p. 1599-1644, https://doi.org/10.1093/petrology/egw051.","productDescription":"46 p.","startPage":"1599","endPage":"1644","ipdsId":"IP-064750","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":470499,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/petrology/egw051","text":"Publisher Index Page"},{"id":329728,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-07","publicationStatus":"PW","scienceBaseUri":"58088686e4b0f497e78e24b9","contributors":{"authors":[{"text":"Watts, Kathryn E. 0000-0002-6110-7499 kwatts@usgs.gov","orcid":"https://orcid.org/0000-0002-6110-7499","contributorId":5081,"corporation":false,"usgs":true,"family":"Watts","given":"Kathryn","email":"kwatts@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":651310,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"John, David A. 0000-0001-7977-9106 djohn@usgs.gov","orcid":"https://orcid.org/0000-0001-7977-9106","contributorId":1748,"corporation":false,"usgs":true,"family":"John","given":"David","email":"djohn@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":651311,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Colgan, Joseph P. 0000-0001-6671-1436 jcolgan@usgs.gov","orcid":"https://orcid.org/0000-0001-6671-1436","contributorId":1649,"corporation":false,"usgs":true,"family":"Colgan","given":"Joseph","email":"jcolgan@usgs.gov","middleInitial":"P.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":651312,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Henry, Christopher D.","contributorId":175501,"corporation":false,"usgs":false,"family":"Henry","given":"Christopher D.","affiliations":[{"id":6689,"text":"Nevada Bureau of Mines and Geology","active":true,"usgs":false}],"preferred":false,"id":651314,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bindeman, Ilya N.","contributorId":175500,"corporation":false,"usgs":false,"family":"Bindeman","given":"Ilya","email":"","middleInitial":"N.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":651313,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schmitt, Axel K.","contributorId":127614,"corporation":false,"usgs":false,"family":"Schmitt","given":"Axel","email":"","middleInitial":"K.","affiliations":[{"id":7081,"text":"University of California - Los Angeles","active":true,"usgs":false}],"preferred":false,"id":651315,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70176894,"text":"sir20165105 - 2016 - Flood-inundation maps for the Peckman River in the Townships of Verona, Cedar Grove, and Little Falls, and the Borough of Woodland Park, New Jersey, 2014","interactions":[],"lastModifiedDate":"2017-07-17T13:36:38","indexId":"sir20165105","displayToPublicDate":"2016-10-19T10:00:00","publicationYear":"2016","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":"2016-5105","title":"Flood-inundation maps for the Peckman River in the Townships of Verona, Cedar Grove, and Little Falls, and the Borough of Woodland Park, New Jersey, 2014","docAbstract":"

Digital flood-inundation maps for an approximate 7.5-mile reach of the Peckman River in New Jersey, which extends from Verona Lake Dam in the Township of Verona downstream through the Township of Cedar Grove and the Township of Little Falls to the confluence with the Passaic River in the Borough of Woodland Park, were created by the U.S. Geological Survey (USGS) in cooperation with the New Jersey Department of Environmental Protection. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/ depict estimates of the probable areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the Peckman River at Ozone Avenue at Verona, New Jersey (station number 01389534). Near-real-time stages at this streamgage may be obtained on the Internet from the USGS National Water Information System at http://waterdata.usgs.gov/.

Flood profiles were simulated for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated using the most current stage-discharge relations at USGS streamgages on the Peckman River at Ozone Avenue at Verona, New Jersey (station number 01389534) and the Peckman River at Little Falls, New Jersey (station number 01389550). The hydraulic model was then used to compute eight water-surface profiles for flood stages at 0.5-foot (ft) intervals ranging from 3.0 ft or near bankfull to 6.5 ft, which is approximately the highest recorded water level during the period of record (1979–2014) at USGS streamgage 01389534, Peckman River at Ozone Avenue at Verona, New Jersey. The simulated water-surface profiles were then combined with a geographic information system digital elevation model derived from light detection and ranging (lidar) data to delineate the area flooded at each water level.

The availability of these maps along with Internet information regarding current stage from the USGS streamgage provides emergency management personnel and residents with information, such as estimates of inundation extents, based on water stage, that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165105","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Niemoczynski, M.J., and Watson, K.M., 2016, Flood-inundation maps for the Peckman River in the Townships of Verona, Cedar Grove, and Little Falls, and the Borough of Woodland Park, New Jersey, 2014: U.S. Geological Survey Scientific Investigations Report 2016-5105, 13 p. https://dx.doi.org/10.3133/sir20165105","productDescription":"vii, 13 p.","onlineOnly":"Y","ipdsId":"IP-053115","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":329482,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5105/sir20165105.pdf","text":"Report","size":"7.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5105"},{"id":329481,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5105/coverthb.jpg"},{"id":343948,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7C53J0H","text":"USGS data release","description":"USGS data release","linkHelpText":"Flood-inundation Mapping Data for the Peckman River in the Townships of Verona, Cedar Grove, and Little Falls, and the Borough of Woodland Park, New Jersey, 2014"}],"country":"United States","state":"New Jersey","city":" Cedar Grove, Little Falls, Verona, Woodland Park","otherGeospatial":"Peckman River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.4,\n 41\n ],\n [\n -74.4,\n 40.8\n ],\n [\n -74.1,\n 40.8\n ],\n [\n -74.1,\n 41\n ],\n [\n -74.4,\n 41\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville NJ, 08648
http://nj.usgs.gov/

","tableOfContents":"","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2016-10-19","noUsgsAuthors":false,"publicationDate":"2016-10-19","publicationStatus":"PW","scienceBaseUri":"58088686e4b0f497e78e24bb","contributors":{"authors":[{"text":"Niemoczynski, Michal J. 0000-0003-0880-7354 mniemocz@usgs.gov","orcid":"https://orcid.org/0000-0003-0880-7354","contributorId":5840,"corporation":false,"usgs":true,"family":"Niemoczynski","given":"Michal","email":"mniemocz@usgs.gov","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":650632,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Watson, Kara M. 0000-0002-2685-0260 kmwatson@usgs.gov","orcid":"https://orcid.org/0000-0002-2685-0260","contributorId":2134,"corporation":false,"usgs":true,"family":"Watson","given":"Kara","email":"kmwatson@usgs.gov","middleInitial":"M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":650631,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70177056,"text":"sir20165139 - 2016 - Water levels and groundwater and surface-water exchanges in lakes of the northeast Twin Cities Metropolitan Area, Minnesota, 2002 through 2015","interactions":[{"subject":{"id":70176961,"text":"sir20165139A - 2016 - Statistical analysis of lake levels and field study of groundwater and surface-water exchanges in the northeast Twin Cities Metropolitan Area, Minnesota, 2002 through 2015: Chapter A of Water levels and groundwater and surface-water exchanges in lakes of the northeast Twin Cities Metropolitan Area, Minnesota, 2002 through 2015","indexId":"sir20165139A","publicationYear":"2016","noYear":false,"chapter":"A","title":"Statistical analysis of lake levels and field study of groundwater and surface-water exchanges in the northeast Twin Cities Metropolitan Area, Minnesota, 2002 through 2015: Chapter A of Water levels and groundwater and surface-water exchanges in lakes of the northeast Twin Cities Metropolitan Area, Minnesota, 2002 through 2015"},"predicate":"IS_PART_OF","object":{"id":70177056,"text":"sir20165139 - 2016 - Water levels and groundwater and surface-water exchanges in lakes of the northeast Twin Cities Metropolitan Area, Minnesota, 2002 through 2015","indexId":"sir20165139","publicationYear":"2016","noYear":false,"title":"Water levels and groundwater and surface-water exchanges in lakes of the northeast Twin Cities Metropolitan Area, Minnesota, 2002 through 2015"},"id":1},{"subject":{"id":70189630,"text":"sir20165139B - 2017 - Simulation and assessment of groundwater flow and groundwater and surface-water exchanges in lakes of the northeast Twin Cities Metropolitan Area, Minnesota, 2003 through 2013: Chapter B of Water levels and groundwater and surface-water exchanges in lakes of the northeast Twin Cities Metropolitan Area, Minnesota, 2002 through 2015","indexId":"sir20165139B","publicationYear":"2017","noYear":false,"chapter":"B","displayTitle":"Simulation and assessment of groundwater flow and groundwater and surface-water exchanges in lakes of the northeast Twin Cities Metropolitan Area, Minnesota, 2003 through 2013: Chapter B of Water levels and groundwater and surface-water exchanges in lakes of the northeast Twin Cities Metropolitan Area, Minnesota, 2002 through 2015","title":"Simulation and assessment of groundwater flow and groundwater and surface-water exchanges in lakes of the northeast Twin Cities Metropolitan Area, Minnesota, 2003 through 2013: Chapter B of Water levels and groundwater and surface-water exchanges in lakes of the northeast Twin Cities Metropolitan Area, Minnesota, 2002 through 2015"},"predicate":"IS_PART_OF","object":{"id":70177056,"text":"sir20165139 - 2016 - Water levels and groundwater and surface-water exchanges in lakes of the northeast Twin Cities Metropolitan Area, Minnesota, 2002 through 2015","indexId":"sir20165139","publicationYear":"2016","noYear":false,"title":"Water levels and groundwater and surface-water exchanges in lakes of the northeast Twin Cities Metropolitan Area, Minnesota, 2002 through 2015"},"id":2}],"lastModifiedDate":"2016-10-19T10:00:47","indexId":"sir20165139","displayToPublicDate":"2016-10-19T00:00:00","publicationYear":"2016","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":"2016-5139","title":"Water levels and groundwater and surface-water exchanges in lakes of the northeast Twin Cities Metropolitan Area, Minnesota, 2002 through 2015","docAbstract":"

Overview

This study assessed lake-water levels and regional and local groundwater and surface-water exchanges near northeast Twin Cities Metropolitan Area lakes applying three approaches: statistical analysis, field study, and groundwater-flow modeling.  Statistical analyses of lake levels were completed to assess the effect of physical setting and climate on lake-level fluctuations of selected lakes. A field study of groundwater and surface-water interactions in selected lakes was completed to (1) estimate potential percentages of surface-water contributions to well water across the northeast Twin Cities Metropolitan Area, (2) estimate general ages for waters extracted from the wells, and (3) assess groundwater inflow to lakes and lake-water outflow to aquifers downgradient from White Bear Lake.  Groundwater flow was simulated using a steady-state, groundwater-flow model to assess regional groundwater and surface-water exchanges and the effects of groundwater withdrawals, climate, and other factors on water levels of northeast Twin Cities Metropolitan Area lakes.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165139","collaboration":"Prepared in cooperation with the Metropolitan Council and Minnesota Department of Health","usgsCitation":"Jones, P.M., Trost, J.J., and Erickson, M.L., eds., 2016, Water levels and groundwater and surface-water exchanges in lakes of the northeast Twin Cities Metropolitan Area, Minnesota, 2002 through 2015: U.S. Geological Survey Scientific Investigations Report 2016–5139, https://dx.doi.org/10.3133/sir20165139.","productDescription":"2 Chapters","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":329671,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5139/coverthb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Twin Cities Metropolitan Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.55957031249999,\n 45.29614310895674\n ],\n [\n -93.56231689453125,\n 45.464946600971466\n ],\n [\n -92.67791748046875,\n 45.464946600971466\n ],\n [\n -92.64907836914062,\n 45.4524242413431\n ],\n [\n -92.65045166015625,\n 45.427371178989084\n ],\n [\n -92.64770507812499,\n 45.40230699238177\n ],\n [\n -92.6806640625,\n 45.37337295384522\n ],\n [\n -92.69851684570312,\n 45.35407536661815\n ],\n [\n -92.70401000976562,\n 45.32704768567264\n ],\n [\n -92.72872924804688,\n 45.30387083299528\n ],\n [\n -92.76031494140625,\n 45.287448160456634\n ],\n [\n -92.7520751953125,\n 45.260388684823695\n ],\n [\n -92.75344848632812,\n 45.21687321093267\n ],\n [\n -92.76443481445311,\n 45.19268354749929\n ],\n [\n -92.75344848632812,\n 45.17041997262664\n ],\n [\n -92.74383544921875,\n 45.11520750294172\n ],\n [\n -92.78778076171874,\n 45.07836948371111\n ],\n [\n -92.79464721679688,\n 45.055091121730385\n ],\n [\n -92.76718139648438,\n 45.035685245316316\n ],\n [\n -92.76443481445311,\n 45.021126517829614\n ],\n [\n -92.7685546875,\n 44.992969144596394\n ],\n [\n -92.76168823242188,\n 44.959939425508466\n ],\n [\n -92.75619506835936,\n 44.92397370210939\n ],\n [\n -92.75894165039062,\n 44.90549606158295\n ],\n [\n -92.77267456054686,\n 44.89771422497743\n ],\n [\n -92.7740478515625,\n 44.88214739188459\n ],\n [\n -92.76168823242188,\n 44.87728189251396\n ],\n [\n -92.76580810546875,\n 44.860736117070026\n ],\n [\n -92.76718139648438,\n 44.83055216443822\n ],\n [\n -92.78366088867188,\n 44.80132682904856\n ],\n [\n -92.79602050781249,\n 44.77696106840196\n ],\n [\n -92.8070068359375,\n 44.74868389996833\n ],\n [\n -92.8619384765625,\n 44.75356026127114\n ],\n [\n -92.90176391601562,\n 44.77403648591521\n ],\n [\n -92.93746948242188,\n 44.7691618526244\n ],\n [\n -92.94433593749999,\n 44.76038647589176\n ],\n [\n -92.96218872070312,\n 44.75697346938202\n ],\n [\n -92.98553466796875,\n 44.762336674810996\n ],\n [\n -93.00544738769531,\n 44.76818687659432\n ],\n [\n -93.02261352539062,\n 44.77793589631623\n ],\n [\n -93.01437377929686,\n 44.82178611905465\n ],\n [\n -93.0157470703125,\n 44.86852295681338\n ],\n [\n -93.02536010742188,\n 44.89868701215517\n ],\n [\n -93.05419921875,\n 44.91327684489316\n ],\n [\n -93.05831909179688,\n 44.941473354802504\n ],\n [\n -93.08441162109374,\n 44.94438944516438\n ],\n [\n -93.11599731445312,\n 44.923001342833096\n ],\n [\n -93.16543579101562,\n 44.89382291168926\n ],\n [\n -93.18878173828125,\n 44.89479576469787\n ],\n [\n -93.2025146484375,\n 44.912304304581525\n ],\n [\n -93.2025146484375,\n 44.94924926661153\n ],\n [\n -93.218994140625,\n 44.96382626228214\n ],\n [\n -93.26019287109375,\n 44.97645666320777\n ],\n [\n -93.27392578125,\n 44.991026746594535\n ],\n [\n -93.28903198242188,\n 45.03665569548622\n ],\n [\n -93.28353881835938,\n 45.08418759300652\n ],\n [\n -93.27941894531249,\n 45.106484856646844\n ],\n [\n -93.31100463867188,\n 45.1394300814679\n ],\n [\n -93.34808349609375,\n 45.15686396890044\n ],\n [\n -93.36593627929688,\n 45.17526062079539\n ],\n [\n -93.394775390625,\n 45.1907479300741\n ],\n [\n -93.4222412109375,\n 45.205263456162385\n ],\n [\n -93.44284057617186,\n 45.215905821884036\n ],\n [\n -93.48129272460936,\n 45.22557897178979\n ],\n [\n -93.50601196289062,\n 45.24105258851866\n ],\n [\n -93.54446411132811,\n 45.25555527789205\n ],\n [\n -93.5540771484375,\n 45.263288531496876\n ],\n [\n -93.55957031249999,\n 45.29614310895674\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Minnesota Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, Minnesota 55112

http://mn.water.usgs.gov/

","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2016-10-19","noUsgsAuthors":false,"publicationDate":"2016-10-19","publicationStatus":"PW","scienceBaseUri":"5805e34be4b0824b2d1c24b6","contributors":{"editors":[{"text":"Jones, Perry M. 0000-0002-6569-5144 pmjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6569-5144","contributorId":2231,"corporation":false,"usgs":true,"family":"Jones","given":"Perry","email":"pmjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":651201,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Trost, Jared J. 0000-0003-0431-2151 jtrost@usgs.gov","orcid":"https://orcid.org/0000-0003-0431-2151","contributorId":3749,"corporation":false,"usgs":true,"family":"Trost","given":"Jared","email":"jtrost@usgs.gov","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":651202,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Erickson, Melinda L. 0000-0002-1117-2866 merickso@usgs.gov","orcid":"https://orcid.org/0000-0002-1117-2866","contributorId":3671,"corporation":false,"usgs":true,"family":"Erickson","given":"Melinda L.","email":"merickso@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":651203,"contributorType":{"id":2,"text":"Editors"},"rank":4}]}} ,{"id":70177094,"text":"70177094 - 2016 - Maximum magnitude (Mmax) in the central and eastern United States for the 2014 U.S. Geological Survey Hazard Model","interactions":[],"lastModifiedDate":"2016-10-19T10:32:28","indexId":"70177094","displayToPublicDate":"2016-10-19T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Maximum magnitude (Mmax) in the central and eastern United States for the 2014 U.S. Geological Survey Hazard Model","docAbstract":"

Probabilistic seismic‐hazard assessment (PSHA) requires an estimate of Mmax, the moment magnitude M of the largest earthquake that could occur within a specified area. Sparse seismicity hinders Mmax estimation in the central and eastern United States (CEUS) and tectonically similar regions worldwide (stable continental regions [SCRs]). A new global catalog of moderate‐to‐large SCR earthquakes is analyzed with minimal assumptions about enigmatic geologic controls on SCR Mmax. An earlier observation that SCR earthquakes of M 7.0 and larger occur in young (250–23 Ma) passive continental margins and associated rifts but not in cratons is not strongly supported by the new catalog. SCR earthquakes of M 7.5 and larger are slightly more numerous and reach slightly higher M in young passive margins and rifts than in cratons. However, overall histograms of M from young margins and rifts and from cratons are statistically indistinguishable. This conclusion is robust under uncertainties inM, the locations of SCR boundaries, and which of two available global SCR catalogs is used. The conclusion stems largely from recent findings that (1) large southeast Asian earthquakes once thought to be SCR were in actively deforming crust and (2) long escarpments in cratonic Australia were formed by prehistoric faulting. The 2014 seismic‐hazard model of the U.S. Geological Survey represents CEUS Mmax as four‐point probability distributions. The distributions have weighted averages of M 7.0 in cratons and M 7.4 in passive margins and rifts. These weighted averages are consistent with Mmax estimates of other SCR PSHAs of the CEUS, southeastern Canada, Australia, and India.

","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120160048","usgsCitation":"Wheeler, R.L., 2016, Maximum magnitude (Mmax) in the central and eastern United States for the 2014 U.S. Geological Survey Hazard Model: Bulletin of the Seismological Society of America, v. 106, no. 5, p. 2154-2167, https://doi.org/10.1785/0120160048.","productDescription":"14 p.","startPage":"2154","endPage":"2167","ipdsId":"IP-076405","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":329732,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"106","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-30","publicationStatus":"PW","scienceBaseUri":"58088686e4b0f497e78e24bd","contributors":{"authors":[{"text":"Wheeler, Russell L. wheeler@usgs.gov","contributorId":858,"corporation":false,"usgs":true,"family":"Wheeler","given":"Russell","email":"wheeler@usgs.gov","middleInitial":"L.","affiliations":[],"preferred":false,"id":651258,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70177063,"text":"70177063 - 2016 - Trophic feasibility of reintroducing anadromous salmonids in three reservoirs on the north fork Lewis River, Washington: Prey supply and consumption demand of resident fishes","interactions":[],"lastModifiedDate":"2016-10-19T11:19:29","indexId":"70177063","displayToPublicDate":"2016-10-19T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Trophic feasibility of reintroducing anadromous salmonids in three reservoirs on the north fork Lewis River, Washington: Prey supply and consumption demand of resident fishes","docAbstract":"

The reintroduction of anadromous salmonids in reservoirs is being proposed with increasing frequency, requiring baseline studies to evaluate feasibility and estimate the capacity of reservoir food webs to support reintroduced populations. Using three reservoirs on the north fork Lewis River as a case study, we demonstrate a method to determine juvenile salmonid smolt rearing capacities for lakes and reservoirs. To determine if the Lewis River reservoirs can support reintroduced populations of juvenile stream-type Chinook Salmon Oncorhynchus tshawytscha, we evaluated the monthly production of daphniaDaphnia spp. (the primary zooplankton consumed by resident salmonids in the system) and used bioenergetics to model the consumption demand of resident fishes in each reservoir. To estimate the surplus of Daphnia prey available for reintroduced salmonids, we assumed a maximum sustainable exploitation rate and accounted for the consumption demand of resident fishes. The number of smolts that could have been supported was estimated by dividing any surplus Daphnia production by the simulated consumption demand of an individual Chinook Salmon fry rearing in the reservoir to successful smolt size. In all three reservoirs, densities of Daphnia were highest in the epilimnion, but warm epilimnetic temperatures and the vertical distribution of planktivores suggested that access to abundant epilimnetic prey was limited. By comparing accessible prey supply and demand on a monthly basis, we were able to identify potential prey supply bottlenecks that could limit smolt production and growth. These results demonstrate that a bioenergetics approach can be a valuable method of examining constraints on lake and reservoir rearing capacity, such as thermal structure and temporal food supply. This method enables numerical estimation of rearing capacity, which is a useful metric for managers evaluating the feasibility of reintroducing Pacific salmon Oncorhynchus spp. in lentic systems.

","language":"English","publisher":"American Fisheries Society","doi":"10.1080/00028487.2016.1219678","usgsCitation":"Sorel, M.H., Hansen, A., Connelly, K.A., and Beauchamp, D.A., 2016, Trophic feasibility of reintroducing anadromous salmonids in three reservoirs on the north fork Lewis River, Washington: Prey supply and consumption demand of resident fishes: Transactions of the American Fisheries Society, v. 145, no. 6, p. 1331-1347, https://doi.org/10.1080/00028487.2016.1219678.","productDescription":"17 p.","startPage":"1331","endPage":"1347","ipdsId":"IP-070088","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":470501,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/journal_contribution/Trophic_Feasibility_of_Reintroducing_Anadromous_Salmonids_in_Three_Reservoirs_on_the_North_Fork_Lewis_River_Washington_Prey_Supply_and_Consumption_Demand_of_Resident_Fishes/4007424","text":"External Repository"},{"id":329735,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Lewis River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.57377624511719,\n 45.94255573048781\n ],\n [\n -122.57377624511719,\n 46.08228057808761\n ],\n [\n -122.00454711914061,\n 46.08228057808761\n ],\n [\n -122.00454711914061,\n 45.94255573048781\n ],\n [\n -122.57377624511719,\n 45.94255573048781\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"145","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-11","publicationStatus":"PW","scienceBaseUri":"58088687e4b0f497e78e24c3","contributors":{"authors":[{"text":"Sorel, Mark H.","contributorId":171739,"corporation":false,"usgs":false,"family":"Sorel","given":"Mark","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":651341,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hansen, Adam G.","contributorId":103947,"corporation":false,"usgs":true,"family":"Hansen","given":"Adam G.","affiliations":[],"preferred":false,"id":651342,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Connelly, Kristin A.","contributorId":174523,"corporation":false,"usgs":false,"family":"Connelly","given":"Kristin","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":651343,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beauchamp, David A. 0000-0002-3592-8381 fadave@usgs.gov","orcid":"https://orcid.org/0000-0002-3592-8381","contributorId":4205,"corporation":false,"usgs":true,"family":"Beauchamp","given":"David","email":"fadave@usgs.gov","middleInitial":"A.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":651191,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70177064,"text":"70177064 - 2016 - Recovery of sockeye salmon in the Elwha River, Washington, after dam removal: Dependence of smolt production on the resumption of anadromy by landlocked kokanee","interactions":[],"lastModifiedDate":"2016-10-19T11:15:59","indexId":"70177064","displayToPublicDate":"2016-10-19T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Recovery of sockeye salmon in the Elwha River, Washington, after dam removal: Dependence of smolt production on the resumption of anadromy by landlocked kokanee","docAbstract":"

Pacific salmon Oncorhynchus spp. are adept at colonizing habitat that has been reopened to anadromous passage. Sockeye Salmon O. nerka are unique in that most populations require lakes to fulfill their life history. Thus, for Sockeye Salmon to colonize a system, projects like dam removals must provide access to lakes. However, if the lakes contain landlocked kokanee (lacustrine Sockeye Salmon), the recovery of Sockeye Salmon could be mediated by interactions between the two life history forms and the processes associated with the resumption of anadromy. Our objective was to evaluate the extent to which estimates of Sockeye Salmon smolt production and recovery are sensitive to the resumption of anadromy by kokanee after dam removal. We informed the analysis based on the abiotic and biotic features of Lake Sutherland, which was recently opened to passage after dam removal on the Elwha River, Washington. We first developed maximum expectations for the smolt-producing capacity of Lake Sutherland by using two predictive models developed from Sockeye Salmon populations in Alaska and British Columbia: one model was based on the mean seasonal biomass of macrozooplankton, and the other was based on the euphotic zone volume of the lake. We then constructed a bioenergetics-based simulation model to evaluate how the capacity of Lake Sutherland to rear yearling smolts could change with varying degrees of anadromy among O. nerka fry. We demonstrated that (1) the smolt-producing capacity of a nursery lake for juvenile Sockeye Salmon changes in nonlinear ways with changes in smolt growth, mortality, and the extent to which kokanee resume anadromy after dam removal; (2) kokanee populations may be robust to changes in abundance after dam removal, particularly if lakes are located higher in the watershed on tributaries separate from where dams were removed; and (3) the productivity of newly establishing Sockeye Salmon can vary considerably depending on whether the population becomes rearing limited or is recruitment limited and depending on how adult escapement is managed.

","language":"English","publisher":"American Fisheries Society","doi":"10.1080/00028487.2016.1223752","usgsCitation":"Hansen, A., Gardner, J.R., Beauchamp, D.A., Paradis, R., and Quinn, T.P., 2016, Recovery of sockeye salmon in the Elwha River, Washington, after dam removal: Dependence of smolt production on the resumption of anadromy by landlocked kokanee: Transactions of the American Fisheries Society, v. 145, no. 6, p. 1303-1317, https://doi.org/10.1080/00028487.2016.1223752.","productDescription":"15 p.","startPage":"1303","endPage":"1317","ipdsId":"IP-072935","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":462059,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/00028487.2016.1223752","text":"Publisher Index Page"},{"id":329734,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Elwha River, Lake Sutherland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.72493743896483,\n 48.07268823432358\n ],\n [\n -123.72493743896483,\n 48.08392779751268\n ],\n [\n -123.68605613708495,\n 48.08392779751268\n ],\n [\n -123.68605613708495,\n 48.07268823432358\n ],\n [\n -123.72493743896483,\n 48.07268823432358\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"145","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-11","publicationStatus":"PW","scienceBaseUri":"58088687e4b0f497e78e24c1","contributors":{"authors":[{"text":"Hansen, Adam G.","contributorId":103947,"corporation":false,"usgs":true,"family":"Hansen","given":"Adam G.","affiliations":[],"preferred":false,"id":651337,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gardner, Jennifer R.","contributorId":175505,"corporation":false,"usgs":false,"family":"Gardner","given":"Jennifer","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":651338,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beauchamp, David A. 0000-0002-3592-8381 fadave@usgs.gov","orcid":"https://orcid.org/0000-0002-3592-8381","contributorId":4205,"corporation":false,"usgs":true,"family":"Beauchamp","given":"David","email":"fadave@usgs.gov","middleInitial":"A.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":651194,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paradis, Rebecca","contributorId":145488,"corporation":false,"usgs":false,"family":"Paradis","given":"Rebecca","affiliations":[{"id":13135,"text":"Lower Elwha Klallam Tribe, Port Angeles, WA","active":true,"usgs":false}],"preferred":false,"id":651339,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Quinn, Thomas P.","contributorId":167272,"corporation":false,"usgs":false,"family":"Quinn","given":"Thomas","email":"","middleInitial":"P.","affiliations":[{"id":24671,"text":"School of Aquatic and Fsiery Sciences, UW, Box 355020, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":651340,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70162047,"text":"70162047 - 2016 - Book review: Mapping gendered routes and spaces in the early modern world","interactions":[],"lastModifiedDate":"2017-04-03T10:41:50","indexId":"70162047","displayToPublicDate":"2016-10-18T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5120,"text":"Renaissance Quarterly","active":true,"publicationSubtype":{"id":10}},"title":"Book review: Mapping gendered routes and spaces in the early modern world","docAbstract":"

This book encapsulates and extends many seminal ideas presented at the eighth “Attending to Early Modern Women” conference held at the University of Wisconsin–Milwaukee in June 2012. Merry Wiesner-Hanks has done a masterful job editing these papers within a central theme of the interaction of spatial domains with gender-based phenomena. The fifteen chapters of this book are organized into four sections: “Framework,” discussing theoretical concepts; “Embodied Environments,” focusing on physicality; “Communities and Networks” of social patterns; and “Exchanges” across geographic space. Together, a global society shaped by gender and sexuality and intersected by race and class emerges.

","language":"English","publisher":"Renaissance Society of America","publisherLocation":"New York, NY","doi":"10.1086/689092","usgsCitation":"Varanka, D.E., 2016, Book review: Mapping gendered routes and spaces in the early modern world: Renaissance Quarterly, v. 69, no. 3, https://doi.org/10.1086/689092.","productDescription":"1 p.","startPage":"1092","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069367","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":339002,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"69","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-20","publicationStatus":"PW","scienceBaseUri":"58e35f7fe4b09da67997eca9","contributors":{"authors":[{"text":"Varanka, Dalia E. 0000-0003-2857-9600 dvaranka@usgs.gov","orcid":"https://orcid.org/0000-0003-2857-9600","contributorId":1296,"corporation":false,"usgs":true,"family":"Varanka","given":"Dalia","email":"dvaranka@usgs.gov","middleInitial":"E.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":588401,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70171470,"text":"sir20165068 - 2016 - Estimating spatially and temporally varying recharge and runoff from precipitation and urban irrigation in the Los Angeles Basin, California","interactions":[],"lastModifiedDate":"2018-07-05T12:42:08","indexId":"sir20165068","displayToPublicDate":"2016-10-17T13:00:00","publicationYear":"2016","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":"2016-5068","title":"Estimating spatially and temporally varying recharge and runoff from precipitation and urban irrigation in the Los Angeles Basin, California","docAbstract":"

A daily precipitation-runoff model, referred to as the Los Angeles Basin watershed model (LABWM), was used to estimate recharge and runoff for a 5,047 square kilometer study area that included the greater Los Angeles area and all surface-water drainages potentially contributing recharge to a 1,450 square kilometer groundwater-study area underlying the greater Los Angeles area, referred to as the Los Angeles groundwater-study area. The recharge estimates for the Los Angeles groundwater-study area included spatially distributed recharge in response to the infiltration of precipitation, runoff, and urban irrigation, as well as mountain-front recharge from surface-water drainages bordering the groundwater-study area. The recharge and runoff estimates incorporated a new method for estimating urban irrigation, consisting of residential and commercial landscape watering, based on land use and the percentage of pervious land area.

The LABWM used a 201.17-meter gridded discretization of the study area to represent spatially distributed climate and watershed characteristics affecting the surface and shallow sub-surface hydrology for the Los Angeles groundwater study area. Climate data from a local network of 201 monitoring sites and published maps of 30-year-average monthly precipitation and maximum and minimum air temperature were used to develop the climate inputs for the LABWM. Published maps of land use, land cover, soils, vegetation, and surficial geology were used to represent the physical characteristics of the LABWM area. The LABWM was calibrated to available streamflow records at six streamflow-gaging stations.

Model results for a 100-year target-simulation period, from water years 1915 through 2014, were used to quantify and evaluate the spatial and temporal variability of water-budget components, including evapotranspiration (ET), recharge, and runoff. The largest outflow of water from the LABWM was ET; the 100-year average ET rate of 362 millimeters per year (mm/yr) accounted for 66 percent of the combined water inflow of 551 mm/yr, including 488 mm/yr from precipitation and 63 mm/yr from urban irrigation. The simulated ET rate varied from a minimum of 0 mm/yr for impervious areas to high values of more than 1,000 mm/yr for many areas, including the south-facing slopes of the San Gabriel Mountains, stream channels underlain by permeable soils and thick root zones, and pervious locations receiving inflows both from urban irrigation and surface water. Runoff was the next largest outflow, averaging 145 mm/yr for the 100-year period, or 26 percent of the combined precipitation and urban-irrigation inflow. Recharge averaged 45 mm/yr, or about 8 percent of the combined inflow from precipitation and urban irrigation.

Simulation results indicated that recharge in response to urban irrigation was an important component of spatially distributed recharge, contributing an average of 56 percent of the total recharge to the eight LABWM subdomains containing the Los Angeles groundwater study area. The 100‑year average recharge rate for the eight subdomains was 41 mm/yr, or 8,473 hectare-meters per year (ha-m/yr), with urban irrigation included in the simulation compared to a recharge rate of 18 mm/yr, or 3,741 ha-m/yr, with urban irrigation excluded. In contrast to recharge, the effect of urban irrigation on runoff was slight; runoff was 72,667 ha-m/yr with urban irrigation included compared to 72,618 ha-m/yr with urban irrigation excluded, an increase of only 48 ha-m/yr (about 0.1 percent).

Simulation results also indicated that potential recharge from hilly drainages outside of, but bordering and tributary to, the lower-lying area of the Los Angeles groundwater study area, in this study referred to as mountain-front recharge, could provide an important contribution to the total recharge for the groundwater basins. The time-averaged recharge rate was similar to the combined direct and mountain-front recharge components estimated in a previous study and used as input for a calibrated groundwater model. The annual (water year) recharge estimates simulated in this study, however, indicated much greater year-to-year variability, which was dependent on year-to-year variability in the magnitude and distribution of daily precipitation, compared to the previous estimates.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165068","collaboration":"Prepared in cooperation with the Water Replenishment District of Southern California","usgsCitation":"Hevesi, J.A., and Johnson, T.D., 2016, Estimating spatially and temporally varying recharge and runoff from precipitation and urban irrigation in the Los Angeles Basin, California: U.S. Geological Survey Scientific Investigations Report 2016–5068, 192 p., https://dx.doi.org/10.3133/sir20165068.","productDescription":"x, 192 p.","numberOfPages":"208","onlineOnly":"Y","ipdsId":"IP-053146","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":328887,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5068/coverthb.jpg"},{"id":328888,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5068/sir20165068_.pdf","text":"Report","size":"32.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5068"}],"country":"United States","state":"California","otherGeospatial":"Los Angeles Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.59078979492186,\n 34.29353023058858\n ],\n [\n -117.97531127929688,\n 33.631772324639655\n ],\n [\n -118.15246582031249,\n 33.75288969455201\n ],\n [\n -118.29666137695312,\n 33.70035029271861\n ],\n [\n -118.41339111328125,\n 33.74032885072381\n ],\n [\n -118.43673706054688,\n 33.775722878425604\n ],\n [\n -118.39828491210936,\n 33.82023008524739\n ],\n [\n -118.44223022460938,\n 33.9285481685662\n ],\n [\n -118.50952148437499,\n 34.016241889667015\n ],\n [\n -118.60565185546874,\n 34.03672867489511\n ],\n [\n -118.67706298828125,\n 34.34230217446123\n ],\n [\n -118.40377807617189,\n 34.426168904360736\n ],\n [\n -117.87368774414064,\n 34.38197934098774\n ],\n [\n -117.59078979492186,\n 34.29353023058858\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819
http://ca.water.usgs.gov

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2016-10-17","noUsgsAuthors":false,"publicationDate":"2016-10-17","publicationStatus":"PW","scienceBaseUri":"5805e349e4b0824b2d1c24b4","contributors":{"authors":[{"text":"Hevesi, Joseph A. 0000-0003-2898-1800 jhevesi@usgs.gov","orcid":"https://orcid.org/0000-0003-2898-1800","contributorId":1507,"corporation":false,"usgs":true,"family":"Hevesi","given":"Joseph","email":"jhevesi@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":631158,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Tyler D. 0000-0002-7334-9188 tyjohns@usgs.gov","orcid":"https://orcid.org/0000-0002-7334-9188","contributorId":1440,"corporation":false,"usgs":true,"family":"Johnson","given":"Tyler","email":"tyjohns@usgs.gov","middleInitial":"D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":631159,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70178936,"text":"70178936 - 2016 - Comparison of mercury mass loading in streams to atmospheric deposition in watersheds of Western North America: Evidence for non-atmospheric mercury sources","interactions":[],"lastModifiedDate":"2018-08-07T12:24:30","indexId":"70178936","displayToPublicDate":"2016-10-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of mercury mass loading in streams to atmospheric deposition in watersheds of Western North America: Evidence for non-atmospheric mercury sources","docAbstract":"

Annual stream loads of mercury (Hg) and inputs of wet and dry atmospheric Hg deposition to the landscape were investigated in watersheds of the Western United States and the Canadian-Alaskan Arctic. Mercury concentration and discharge data from flow gauging stations were used to compute annual mass loads with regression models. Measured wet and modeled dry deposition were compared to annual stream loads to compute ratios of Hg stream load to total Hg atmospheric deposition. Watershed land uses or cover included mining, undeveloped, urbanized, and mixed. Of 27 watersheds that were investigated, 15 had some degree of mining, either of Hg or precious metals (gold or silver), where Hg was used in the amalgamation process. Stream loads in excess of annual Hg atmospheric deposition (ratio > 1) were observed in watersheds containing Hg mines and in relatively small and medium-sized watersheds with gold or silver mines, however, larger watersheds containing gold or silver mines, some of which also contain large dams that trap sediment, were sometimes associated with lower load ratios (< 0.2). In the non-Arctic regions, watersheds with natural vegetation tended to have low ratios of stream load to Hg deposition (< 0.1), whereas urbanized areas had higher ratios (0.34–1.0) because of impervious surfaces. This indicated that, in ecosystems with natural vegetation, Hg is retained in the soil and may be transported subsequently to streams as a result of erosion or in association with dissolved organic carbon. Arctic watersheds (Mackenzie and Yukon Rivers) had a relatively elevated ratio of stream load to atmospheric deposition (0.27 and 0.74), possibly because of melting glaciers or permafrost releasing previously stored Hg to the streams. Overall, our research highlights the important role of watershed characteristics in determining whether a landscape is a net source of Hg or a net sink of atmospheric Hg.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2016.02.112","usgsCitation":"Domagalski, J.L., Majewski, M.S., Alpers, C.N., Eckley, C.S., Eagles-Smith, C.A., Schenk, L.N., and Wherry, S., 2016, Comparison of mercury mass loading in streams to atmospheric deposition in watersheds of Western North America: Evidence for non-atmospheric mercury sources: Science of the Total Environment, v. 568, p. 638-650, https://doi.org/10.1016/j.scitotenv.2016.02.112.","productDescription":"13 p.","startPage":"638","endPage":"650","ipdsId":"IP-069584","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":332020,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"568","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"585116bbe4b08138bf1abd56","contributors":{"authors":[{"text":"Domagalski, Joseph L. 0000-0002-6032-757X joed@usgs.gov","orcid":"https://orcid.org/0000-0002-6032-757X","contributorId":1330,"corporation":false,"usgs":true,"family":"Domagalski","given":"Joseph","email":"joed@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":655590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Majewski, Michael S. majewski@usgs.gov","contributorId":440,"corporation":false,"usgs":true,"family":"Majewski","given":"Michael","email":"majewski@usgs.gov","middleInitial":"S.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":655591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alpers, Charles N. 0000-0001-6945-7365 cnalpers@usgs.gov","orcid":"https://orcid.org/0000-0001-6945-7365","contributorId":411,"corporation":false,"usgs":true,"family":"Alpers","given":"Charles","email":"cnalpers@usgs.gov","middleInitial":"N.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":655592,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eckley, Chris S.","contributorId":167256,"corporation":false,"usgs":false,"family":"Eckley","given":"Chris","email":"","middleInitial":"S.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":655593,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285 ceagles-smith@usgs.gov","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":505,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin","email":"ceagles-smith@usgs.gov","middleInitial":"A.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":655594,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schenk, Liam N. 0000-0002-2491-0813 lschenk@usgs.gov","orcid":"https://orcid.org/0000-0002-2491-0813","contributorId":4273,"corporation":false,"usgs":true,"family":"Schenk","given":"Liam","email":"lschenk@usgs.gov","middleInitial":"N.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":655595,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wherry, Susan 0000-0002-6749-8697 swherry@usgs.gov","orcid":"https://orcid.org/0000-0002-6749-8697","contributorId":140159,"corporation":false,"usgs":true,"family":"Wherry","given":"Susan","email":"swherry@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":655707,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70182772,"text":"70182772 - 2016 - Interaction between climate, volcanism, and isostatic rebound in Southeast Alaska during the last deglaciation","interactions":[],"lastModifiedDate":"2017-03-01T14:56:17","indexId":"70182772","displayToPublicDate":"2016-10-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Interaction between climate, volcanism, and isostatic rebound in Southeast Alaska during the last deglaciation","docAbstract":"

Observations of enhanced volcanic frequency during the last deglaciation have led to the hypothesis that ice unloading in glaciated volcanic terrains can promote volcanism through decompression melting in the shallow mantle or a reduction in crustal magma storage time. However, a direct link between regional climate change, isostatic adjustment, and the initiation of volcanism remains to be demonstrated due to the difficulty of obtaining high-resolution well-dated records that capture short-term climate and volcanic variability traced to a particular source region. Here we present an exceptionally resolved record of 19 tephra layers paired with foraminiferal oxygen isotopes and alkenone paleotemperatures from marine sediment cores along the Southeast Alaska margin spanning the last deglacial transition. Major element compositions of the tephras indicate a predominant source from the nearby Mt. Edgecumbe Volcanic Field (MEVF). We constrain the timing of this regional eruptive sequence to 14.6–13.1 ka. The sudden increase in volcanic activity from the MEVF coincides with the onset of Bølling–Allerød interstadial warmth, the disappearance of ice-rafted detritus, and rapid vertical land motion associated with modeled regional isostatic rebound in response to glacier retreat. These data support the hypothesis that regional deglaciation can rapidly trigger volcanic activity. Rapid sea surface temperature fluctuations and an increase in local salinity (i.e., δ18Osw) variability are associated with the interval of intense volcanic activity, consistent with a two-way interaction between climate and volcanism in which rapid volcanic response to ice unloading may in turn enhance short-term melting of the glaciers, plausibly via albedo effects on glacier ablation zones.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2016.07.033","usgsCitation":"Praetorius, S., Mix, A., Jensen, B., Froese, D., Milne, G.A., Wolhowe, M., Addison, J.A., and Prahl, F., 2016, Interaction between climate, volcanism, and isostatic rebound in Southeast Alaska during the last deglaciation: Earth and Planetary Science Letters, v. 452, p. 79-89, https://doi.org/10.1016/j.epsl.2016.07.033.","productDescription":"11 p.","startPage":"79","endPage":"89","ipdsId":"IP-071002","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":336780,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"452","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58b7eba5e4b01ccd5500baf7","contributors":{"authors":[{"text":"Praetorius, Summer","contributorId":184162,"corporation":false,"usgs":false,"family":"Praetorius","given":"Summer","affiliations":[],"preferred":false,"id":673695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mix, Alan","contributorId":184163,"corporation":false,"usgs":false,"family":"Mix","given":"Alan","affiliations":[],"preferred":false,"id":673696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jensen, Britta","contributorId":184164,"corporation":false,"usgs":false,"family":"Jensen","given":"Britta","affiliations":[],"preferred":false,"id":673697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Froese, Duane","contributorId":184165,"corporation":false,"usgs":false,"family":"Froese","given":"Duane","affiliations":[],"preferred":false,"id":673698,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Milne, Glenn A.","contributorId":178028,"corporation":false,"usgs":false,"family":"Milne","given":"Glenn","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":673699,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wolhowe, Matthew","contributorId":184166,"corporation":false,"usgs":false,"family":"Wolhowe","given":"Matthew","affiliations":[],"preferred":false,"id":673700,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Addison, Jason A. 0000-0003-2416-9743 jaddison@usgs.gov","orcid":"https://orcid.org/0000-0003-2416-9743","contributorId":4192,"corporation":false,"usgs":true,"family":"Addison","given":"Jason","email":"jaddison@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":673694,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Prahl, Fred","contributorId":184167,"corporation":false,"usgs":false,"family":"Prahl","given":"Fred","email":"","affiliations":[],"preferred":false,"id":673701,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70181005,"text":"70181005 - 2016 - Spatial variation in nutrient and water color effects on lake chlorophyll at macroscales","interactions":[],"lastModifiedDate":"2017-02-11T18:48:16","indexId":"70181005","displayToPublicDate":"2016-10-13T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Spatial variation in nutrient and water color effects on lake chlorophyll at macroscales","docAbstract":"

The nutrient-water color paradigm is a framework to characterize lake trophic status by relating lake primary productivity to both nutrients and water color, the colored component of dissolved organic carbon. Total phosphorus (TP), a limiting nutrient, and water color, a strong light attenuator, influence lake chlorophyll a concentrations (CHL). But, these relationships have been shown in previous studies to be highly variable, which may be related to differences in lake and catchment geomorphology, the forms of nutrients and carbon entering the system, and lake community composition. Because many of these factors vary across space it is likely that lake nutrient and water color relationships with CHL exhibit spatial autocorrelation, such that lakes near one another have similar relationships compared to lakes further away. Including this spatial dependency in models may improve CHL predictions and clarify how well the nutrient-water color paradigm applies to lakes distributed across diverse landscape settings. However, few studies have explicitly examined spatial heterogeneity in the effects of TP and water color together on lake CHL. In this study, we examined spatial variation in TP and water color relationships with CHL in over 800 north temperate lakes using spatially-varying coefficient models (SVC), a robust statistical method that applies a Bayesian framework to explore space-varying and scale-dependent relationships. We found that TP and water color relationships were spatially autocorrelated and that allowing for these relationships to vary by individual lakes over space improved the model fit and predictive performance as compared to models that did not vary over space. The magnitudes of TP effects on CHL differed across lakes such that a 1 μg/L increase in TP resulted in increased CHL ranging from 2–24 μg/L across lake locations. Water color was not related to CHL for the majority of lakes, but there were some locations where water color had a positive effect such that a unit increase in water color resulted in a 2 μg/L increase in CHL and other locations where it had a negative effect such that a unit increase in water color resulted in a 2 μg/L decrease in CHL. In addition, the spatial scales that captured variation in TP and water color effects were different for our study lakes. Variation in TP–CHL relationships was observed at intermediate distances (~20 km) compared to variation in water color–CHL relationships that was observed at regional distances (~200 km). These results demonstrate that there are lake-to-lake differences in the effects of TP and water color on lake CHL and that this variation is spatially structured. Quantifying spatial structure in these relationships furthers our understanding of the variability in these relationships at macroscales and would improve model prediction of chlorophyll a to better meet lake management goals.

","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0164592","usgsCitation":"Fergus, C.E., Finley, A.O., Soranno, P.A., and Wagner, T., 2016, Spatial variation in nutrient and water color effects on lake chlorophyll at macroscales: PLoS ONE, e0164592; 20 p., https://doi.org/10.1371/journal.pone.0164592.","productDescription":"e0164592; 20 p.","ipdsId":"IP-072158","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":470504,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0164592","text":"Publisher Index Page"},{"id":335187,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine, Michigan, New York, Wisconsin","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-13","publicationStatus":"PW","scienceBaseUri":"589fff1ae4b099f50d3e044d","contributors":{"authors":[{"text":"Fergus, C. Emi","contributorId":150608,"corporation":false,"usgs":false,"family":"Fergus","given":"C.","email":"","middleInitial":"Emi","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":663408,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Finley, Andrew O.","contributorId":39310,"corporation":false,"usgs":true,"family":"Finley","given":"Andrew","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":663409,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Soranno, Patricia A.","contributorId":172104,"corporation":false,"usgs":false,"family":"Soranno","given":"Patricia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":663410,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":663163,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}