Conventional characterization and monitoring of hydrocarbon (HC) pollution is often expensive and time-consuming. Magnetic susceptibility (MS) has been proposed as an inexpensive, long-term monitoring proxy of the degradation of HC. We acquired repeated down hole MS logging data in boreholes at a HC-contaminated field research site in Bemidji, MN, USA. The MS data were analyzed in conjunction with redox conditions and iron availability within the source zone to better assess whether MS can serve as a proxy for monitoring HC contamination in unconsolidated sediments. The MS response at the site diminished during the sampling period, which was found to coincide with depletion of solid phase iron in the source zone. Previous geochemical observations and modeling at the site suggest that the most likely cause of the decrease in MS is the transformation of magnetite to siderite, coupled with the exhaustion of ferrihydrite. Although the temporal MS response at this site gives valuable field-scale evidence for changing conditions of iron cycling and stability of iron minerals it does not provide a simple proxy for long-term monitoring of biodegradation of hydrocarbons in the smear zone.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.7b02155","usgsCitation":"Lund, A.L., Slater, L.D., Atekwana, E.A., Ntarlagiannis, D., Cozzarelli, I.M., and Bekins, B.A., 2017, Evidence of coupled carbon and iron cycling at a hydrocarbon-contaminated site from time lapse magnetic susceptibility: Environmental Science & Technology, v. 51, no. 19, p. 11244-11249, https://doi.org/10.1021/acs.est.7b02155.","productDescription":"6 p.","startPage":"11244","endPage":"11249","ipdsId":"IP-089117","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":438229,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7J67FTF","text":"USGS data release","linkHelpText":"Partial release of iron, alkalinity, and oxygen data from Bemidji crude oil site, Minnesota 1993-2016"},{"id":348272,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","city":"Bemidji","volume":"51","issue":"19","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-21","publicationStatus":"PW","scienceBaseUri":"5a07e88ae4b09af898c8cb83","contributors":{"authors":[{"text":"Lund, Anders L.","contributorId":197902,"corporation":false,"usgs":false,"family":"Lund","given":"Anders","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":714553,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Slater, Lee D.","contributorId":197903,"corporation":false,"usgs":false,"family":"Slater","given":"Lee","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":714554,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Atekwana, Estella A.","contributorId":197904,"corporation":false,"usgs":false,"family":"Atekwana","given":"Estella","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":714555,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ntarlagiannis, Dimitrios","contributorId":150729,"corporation":false,"usgs":false,"family":"Ntarlagiannis","given":"Dimitrios","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":714556,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cozzarelli, Isabelle M. 0000-0002-5123-1007 icozzare@usgs.gov","orcid":"https://orcid.org/0000-0002-5123-1007","contributorId":1693,"corporation":false,"usgs":true,"family":"Cozzarelli","given":"Isabelle","email":"icozzare@usgs.gov","middleInitial":"M.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":714557,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bekins, Barbara A. 0000-0002-1411-6018 babekins@usgs.gov","orcid":"https://orcid.org/0000-0002-1411-6018","contributorId":1348,"corporation":false,"usgs":true,"family":"Bekins","given":"Barbara","email":"babekins@usgs.gov","middleInitial":"A.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":714552,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70192781,"text":"70192781 - 2017 - A new parameterization for integrated population models to document amphibian reintroductions","interactions":[],"lastModifiedDate":"2017-11-07T13:49:47","indexId":"70192781","displayToPublicDate":"2017-09-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"A new parameterization for integrated population models to document amphibian reintroductions","docAbstract":"Managers are increasingly implementing reintroduction programs as part of a global effort to alleviate amphibian declines. Given uncertainty in factors affecting populations and a need to make recurring decisions to achieve objectives, adaptive management is a useful component of these efforts. A major impediment to the estimation of demographic rates often used to parameterize and refine decision-support models is that life-stage-specific monitoring data are frequently sparse for amphibians. We developed a new parameterization for integrated population models to match the ecology of amphibians and capitalize on relatively inexpensive monitoring data to document amphibian reintroductions. We evaluate the capability of this model by fitting it to Oregon spotted frog (Rana pretiosa) monitoring data collected from 2007 to 2014 following their reintroduction within the Klamath Basin, Oregon, USA. The number of egg masses encountered and the estimated adult and metamorph abundances generally increased following reintroduction. We found that survival probability from egg to metamorph ranged from 0.01 in 2008 to 0.09 in 2009 and was not related to minimum spring temperatures, metamorph survival probability ranged from 0.13 in 2010–2011 to 0.86 in 2012–2013 and was positively related to mean monthly temperatures (logit-scale slope = 2.37), adult survival probability was lower for founders (0.40) than individuals recruited after reintroduction (0.56), and the mean number of egg masses per adult female was 0.74. Our study is the first to test hypotheses concerning Oregon spotted frog egg-to-metamorph and metamorph-to-adult transition probabilities in the wild and document their response at multiple life stages following reintroduction. Furthermore, we provide an example to illustrate how the structure of our integrated population model serves as a useful foundation for amphibian decision-support models within adaptive management programs. The integration of multiple, but related, data sets has an advantage of being able to estimate complex ecological relationships across multiple life stages, offering a modeling framework that accommodates uncertainty, enforces parsimony, and ensures all model parameters can be confronted with monitoring data.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.1564","usgsCitation":"Duarte, A., Pearl, C., Adams, M.J., and Peterson, J., 2017, A new parameterization for integrated population models to document amphibian reintroductions: Ecological Applications, v. 27, no. 6, p. 1761-1775, https://doi.org/10.1002/eap.1564.","productDescription":"15 p.","startPage":"1761","endPage":"1775","ipdsId":"IP-079934","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":348393,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-07","publicationStatus":"PW","scienceBaseUri":"5a07e88ae4b09af898c8cb81","contributors":{"authors":[{"text":"Duarte, Adam","contributorId":28492,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":6960,"text":"Department of Biology, Texas State University","active":true,"usgs":false}],"preferred":false,"id":716911,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pearl, Christopher 0000-0003-2943-7321 christopher_pearl@usgs.gov","orcid":"https://orcid.org/0000-0003-2943-7321","contributorId":172669,"corporation":false,"usgs":true,"family":"Pearl","given":"Christopher","email":"christopher_pearl@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":716910,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Adams, M. J. 0000-0001-8844-042X mjadams@usgs.gov","orcid":"https://orcid.org/0000-0001-8844-042X","contributorId":3133,"corporation":false,"usgs":false,"family":"Adams","given":"M.","email":"mjadams@usgs.gov","middleInitial":"J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":716912,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":716909,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70192633,"text":"70192633 - 2017 - Imputation approaches for animal movement modeling","interactions":[],"lastModifiedDate":"2017-11-10T10:51:06","indexId":"70192633","displayToPublicDate":"2017-09-01T00:00:00","publicationYear":"2017","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":"Imputation approaches for animal movement modeling","docAbstract":"The analysis of telemetry data is common in animal ecological studies. While the collection of telemetry data for individual animals has improved dramatically, the methods to properly account for inherent uncertainties (e.g., measurement error, dependence, barriers to movement) have lagged behind. Still, many new statistical approaches have been developed to infer unknown quantities affecting animal movement or predict movement based on telemetry data. Hierarchical statistical models are useful to account for some of the aforementioned uncertainties, as well as provide population-level inference, but they often come with an increased computational burden. For certain types of statistical models, it is straightforward to provide inference if the latent true animal trajectory is known, but challenging otherwise. In these cases, approaches related to multiple imputation have been employed to account for the uncertainty associated with our knowledge of the latent trajectory. Despite the increasing use of imputation approaches for modeling animal movement, the general sensitivity and accuracy of these methods have not been explored in detail. We provide an introduction to animal movement modeling and describe how imputation approaches may be helpful for certain types of models. We also assess the performance of imputation approaches in two simulation studies. Our simulation studies suggests that inference for model parameters directly related to the location of an individual may be more accurate than inference for parameters associated with higher-order processes such as velocity or acceleration. Finally, we apply these methods to analyze a telemetry data set involving northern fur seals (Callorhinus ursinus) in the Bering Sea. Supplementary materials accompanying this paper appear online.
","language":"English","publisher":"Springer","doi":"10.1007/s13253-017-0294-5","usgsCitation":"Scharf, H., Hooten, M., and Johnson, D., 2017, Imputation approaches for animal movement modeling: Journal of Agricultural, Biological, and Environmental Statistics, v. 22, no. 3, p. 335-352, https://doi.org/10.1007/s13253-017-0294-5.","productDescription":"18 p.","startPage":"335","endPage":"352","ipdsId":"IP-083743","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":469562,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://arxiv.org/abs/1705.10310","text":"External Repository"},{"id":348558,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-11","publicationStatus":"PW","scienceBaseUri":"5a06c8c8e4b09af898c860fb","contributors":{"authors":[{"text":"Scharf, Henry","contributorId":200238,"corporation":false,"usgs":false,"family":"Scharf","given":"Henry","affiliations":[],"preferred":false,"id":721545,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":716605,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Devin S.","contributorId":47524,"corporation":false,"usgs":true,"family":"Johnson","given":"Devin S.","affiliations":[],"preferred":false,"id":721546,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192755,"text":"70192755 - 2017 - Effects of breeder turnover and harvest on group composition and recruitment in a social carnivore","interactions":[],"lastModifiedDate":"2017-11-08T12:48:50","indexId":"70192755","displayToPublicDate":"2017-09-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Effects of breeder turnover and harvest on group composition and recruitment in a social carnivore","docAbstract":"Estimates of carbon concentrations in Earth’s mantle vary over more than an order of magnitude, hindering our ability to understand mantle structure and mineralogy, partial melting, and the carbon cycle. CO2 concentrations in mantle-derived magmas supplying hotspot ocean island volcanoes yield our most direct constraints on mantle carbon, but are extensively modified by degassing during ascent. Here we show that undegassed magmatic and mantle carbon concentrations may be estimated in a Bayesian framework using diverse geologic information at an ocean island volcano. Our CO2 concentration estimates do not rely upon complex degassing models, geochemical tracer elements, assumed magma supply rates, or rare undegassed rock samples. Rather, we couple volcanic CO2 emission rates with probabilistic magma supply rates, which are obtained indirectly from magma storage and eruption rates. We estimate that the CO2content of mantle-derived magma supplying Hawai‘i’s active volcanoes is 0.97−0.19+0.25 wt%—roughly 40% higher than previously believed—and is supplied from a mantle source region with a carbon concentration of 263−62+81 ppm. Our results suggest that mantle plumes and ocean island basalts are carbon-rich. Our data also shed light on helium isotope abundances, CO2/Nb ratios, and may imply higher CO2 emission rates from ocean island volcanoes.
","language":"English","publisher":"Springer Nature","doi":"10.1038/ngeo3007","usgsCitation":"Anderson, K.R., and Poland, M.P., 2017, Abundant carbon in the mantle beneath Hawai`i: Nature Geoscience, v. 10, p. 704-708, https://doi.org/10.1038/ngeo3007.","productDescription":"5 p.","startPage":"704","endPage":"708","ipdsId":"IP-082423","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":345581,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.2530517578125,\n 18.906286495910905\n ],\n [\n -154.7698974609375,\n 18.906286495910905\n ],\n [\n -154.7698974609375,\n 20.287961155077717\n ],\n [\n -156.2530517578125,\n 20.287961155077717\n ],\n [\n -156.2530517578125,\n 18.906286495910905\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-21","publicationStatus":"PW","scienceBaseUri":"59b3ac32e4b08b1644d8f1b8","contributors":{"authors":[{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":709891,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":709892,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70195972,"text":"70195972 - 2017 - Application and evaluation of a rapid response earthquake-triggered landslide model to the 25 April 2015 Mw 7.8 Gorkha earthquake, Nepal","interactions":[],"lastModifiedDate":"2018-03-09T16:23:33","indexId":"70195972","displayToPublicDate":"2017-09-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3525,"text":"Tectonophysics","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Application and evaluation of a rapid response earthquake-triggered landslide model to the 25 April 2015 Mw 7.8 Gorkha earthquake, Nepal","title":"Application and evaluation of a rapid response earthquake-triggered landslide model to the 25 April 2015 Mw 7.8 Gorkha earthquake, Nepal","docAbstract":"The 25 April 2015 Mw 7.8 Gorkha earthquake produced strong ground motions across an approximately 250 km by 100 km swath in central Nepal. To assist disaster response activities, we modified an existing earthquake-triggered landslide model based on a Newmark sliding block analysis to estimate the extent and intensity of landsliding and landslide dam hazard. Landslide hazard maps were produced using Shuttle Radar Topography Mission (SRTM) digital topography, peak ground acceleration (PGA) information from the U.S. Geological Survey (USGS) ShakeMap program, and assumptions about the regional rock strength based on end-member values from previous studies. The instrumental record of seismicity in Nepal is poor, so PGA estimates were based on empirical Ground Motion Prediction Equations (GMPEs) constrained by teleseismic data and felt reports. We demonstrate a non-linear dependence of modeled landsliding on aggregate rock strength, where the number of landslides decreases exponentially with increasing rock strength. Model estimates are less sensitive to PGA at steep slopes (> 60°) compared to moderate slopes (30–60°). We compare forward model results to an inventory of landslides triggered by the Gorkha earthquake. We show that moderate rock strength inputs over estimate landsliding in regions beyond the main slip patch, which may in part be related to poorly constrained PGA estimates for this event at far distances from the source area. Directly above the main slip patch, however, the moderate strength model accurately estimates the total number of landslides within the resolution of the model (landslides ≥ 0.0162 km2; observed n = 2214, modeled n = 2987), but the pattern of landsliding differs from observations. This discrepancy is likely due to the unaccounted for effects of variable material strength and local topographic amplification of strong ground motion, as well as other simplifying assumptions about source characteristics and their relationship to landsliding.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.tecto.2016.10.031","usgsCitation":"Gallen, S.F., Clark, M., Godt, J.W., Roback, K., and Niemi, N., 2017, Application and evaluation of a rapid response earthquake-triggered landslide model to the 25 April 2015 Mw 7.8 Gorkha earthquake, Nepal: Tectonophysics, v. 714-715, p. 173-187, https://doi.org/10.1016/j.tecto.2016.10.031.","productDescription":"15 p.","startPage":"173","endPage":"187","ipdsId":"IP-078904","costCenters":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"links":[{"id":469563,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.tecto.2016.10.031","text":"Publisher Index Page"},{"id":352395,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Nepal","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 84.53979492187499,\n 26.46073804319089\n ],\n [\n 86.98974609375,\n 26.46073804319089\n ],\n [\n 86.98974609375,\n 28.719496107557465\n ],\n [\n 84.53979492187499,\n 28.719496107557465\n ],\n [\n 84.53979492187499,\n 26.46073804319089\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"714-715","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee804e4b0da30c1bfc3ce","contributors":{"authors":[{"text":"Gallen, Sean F.","contributorId":139683,"corporation":false,"usgs":false,"family":"Gallen","given":"Sean","email":"","middleInitial":"F.","affiliations":[{"id":12879,"text":"Department of Earth and Environmental Sciences, University of Michigan, Ann Arbor","active":true,"usgs":false}],"preferred":false,"id":730749,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clark, Marin K.","contributorId":139684,"corporation":false,"usgs":false,"family":"Clark","given":"Marin K.","affiliations":[{"id":12879,"text":"Department of Earth and Environmental Sciences, University of Michigan, Ann Arbor","active":true,"usgs":false}],"preferred":false,"id":730750,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Godt, Jonathan W. 0000-0002-8737-2493 jgodt@usgs.gov","orcid":"https://orcid.org/0000-0002-8737-2493","contributorId":1166,"corporation":false,"usgs":true,"family":"Godt","given":"Jonathan","email":"jgodt@usgs.gov","middleInitial":"W.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":730748,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roback, Kevin","contributorId":200288,"corporation":false,"usgs":false,"family":"Roback","given":"Kevin","email":"","affiliations":[],"preferred":false,"id":730751,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Niemi, Nathan A","contributorId":203251,"corporation":false,"usgs":false,"family":"Niemi","given":"Nathan A","affiliations":[{"id":36590,"text":"Dept. of Earth and Environmental Sciences, University of Michigan, Ann Arbor","active":true,"usgs":false}],"preferred":false,"id":730752,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70192713,"text":"70192713 - 2017 - A suite of standard post-tagging evaluation metrics can help assess tag retention for field-based fish telemetry research","interactions":[],"lastModifiedDate":"2017-11-08T14:30:59","indexId":"70192713","displayToPublicDate":"2017-09-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3278,"text":"Reviews in Fish Biology and Fisheries","active":true,"publicationSubtype":{"id":10}},"title":"A suite of standard post-tagging evaluation metrics can help assess tag retention for field-based fish telemetry research","docAbstract":"Telemetry can inform many scientific and research questions if a context exists for integrating individual studies into the larger body of literature. Creating cumulative distributions of post-tagging evaluation metrics would allow individual researchers to relate their telemetry data to other studies. Widespread reporting of standard metrics is a precursor to the calculation of benchmarks for these distributions (e.g., mean, SD, 95% CI). Here we illustrate five types of standard post-tagging evaluation metrics using acoustically tagged Blue Catfish (Ictalurus furcatus) released into a Kansas reservoir. These metrics included: (1) percent of tagged fish detected overall, (2) percent of tagged fish detected daily using abacus plot data, (3) average number of (and percent of available) receiver sites visited, (4) date of last movement between receiver sites (and percent of tagged fish moving during that time period), and (5) number (and percent) of fish that egressed through exit gates. These metrics were calculated for one to three time periods: early (<10 d), during (weekly), and at the end of the study (5 months). Over three-quarters of our tagged fish were detected early (85%) and at the end (85%) of the study. Using abacus plot data, all tagged fish (100%) were detected at least one day and 96% were detected for > 5 days early in the study. On average, tagged Blue Catfish visited 9 (50%) and 13 (72%) of 18 within-reservoir receivers early and at the end of the study, respectively. At the end of the study, 73% of all tagged fish were detected moving between receivers. Creating statistical benchmarks for individual metrics can provide useful reference points. In addition, combining multiple metrics can inform ecology and research design. Consequently, individual researchers and the field of telemetry research can benefit from widespread, detailed, and standard reporting of post-tagging detection metrics.
","language":"English","publisher":"Springer","doi":"10.1007/s11160-017-9484-z","usgsCitation":"Gerber, K.M., Mather, M.E., and Smith, J.M., 2017, A suite of standard post-tagging evaluation metrics can help assess tag retention for field-based fish telemetry research: Reviews in Fish Biology and Fisheries, v. 27, no. 3, p. 651-664, https://doi.org/10.1007/s11160-017-9484-z.","productDescription":"14 p.","startPage":"651","endPage":"664","ipdsId":"IP-073195","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":348472,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-12","publicationStatus":"PW","scienceBaseUri":"5a0425b4e4b0dc0b45b4532b","contributors":{"authors":[{"text":"Gerber, Kayla M.","contributorId":200178,"corporation":false,"usgs":false,"family":"Gerber","given":"Kayla","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":721308,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mather, Martha E. 0000-0003-3027-0215 mather@usgs.gov","orcid":"https://orcid.org/0000-0003-3027-0215","contributorId":2580,"corporation":false,"usgs":true,"family":"Mather","given":"Martha","email":"mather@usgs.gov","middleInitial":"E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":716757,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Joseph M.","contributorId":106712,"corporation":false,"usgs":false,"family":"Smith","given":"Joseph","email":"","middleInitial":"M.","affiliations":[{"id":17855,"text":"School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA","active":true,"usgs":false},{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":721309,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70191827,"text":"70191827 - 2017 - Fraction of young water as an indicator of aquifer vulnerability along two regional flow paths in the Mississippi embayment aquifer system, southeastern USA","interactions":[],"lastModifiedDate":"2018-09-19T09:00:09","indexId":"70191827","displayToPublicDate":"2017-09-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Fraction of young water as an indicator of aquifer vulnerability along two regional flow paths in the Mississippi embayment aquifer system, southeastern USA","docAbstract":"Wells along two regional flow paths were sampled to characterize changes in water quality and the vulnerability to contamination of the Memphis aquifer across a range of hydrologic and land-use conditions in the southeastern United States. The flow paths begin in the aquifer outcrop area and end at public supply wells in the confined parts of the aquifer at Memphis, Tennessee. Age-date tracer (e.g. SF6, 3H, 14C) data indicate that a component of young water is present in the aquifer at most locations along both flow paths, which is consistent with previous studies at Memphis that documented leakage of shallow water into the Memphis aquifer locally where the overlying confining unit is thin or absent. Mixtures of young and old water were most prevalent where long-term pumping for public supply has lowered groundwater levels and induced downward movement of young water. The occurrence of nitrate, chloride and synthetic organic compounds was correlated to the fraction of young water along the flow paths. Oxic conditions persisted for 10 km or more down dip of the confining unit, and the presence of young water in confined parts of the aquifer suggest that contaminants such as nitrate-N have the potential for transport. Long-term monitoring data for one of the flow-path wells screened in the confined part of the aquifer suggest that the vulnerability of the aquifer as indicated by the fraction of young water is increasing over time.
","language":"English","publisher":"Springer","doi":"10.1007/s10040-017-1566-4","usgsCitation":"Kingsbury, J.A., Barlow, J.R., Jurgens, B.C., McMahon, P.B., and Carmichael, J.K., 2017, Fraction of young water as an indicator of aquifer vulnerability along two regional flow paths in the Mississippi embayment aquifer system, southeastern USA: Hydrogeology Journal, v. 25, no. 6, p. 1661-1678, https://doi.org/10.1007/s10040-017-1566-4.","productDescription":"18 p.","startPage":"1661","endPage":"1678","ipdsId":"IP-075337","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":438228,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7R78CCC","text":"USGS data release","linkHelpText":"Well characteristics, water quality and age-date tracer data for wells along two regional flow paths in the Memphis aquifer, southwest Tennessee"},{"id":347282,"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 -90.25,\n 34.75\n ],\n [\n -89,\n 34.75\n ],\n [\n -89,\n 35.5\n ],\n [\n -90.25,\n 35.5\n ],\n [\n -90.25,\n 34.75\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","issue":"6","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-08","publicationStatus":"PW","scienceBaseUri":"59f05121e4b0220bbd9a1d8a","contributors":{"authors":[{"text":"Kingsbury, James A. 0000-0003-4985-275X jakingsb@usgs.gov","orcid":"https://orcid.org/0000-0003-4985-275X","contributorId":883,"corporation":false,"usgs":true,"family":"Kingsbury","given":"James","email":"jakingsb@usgs.gov","middleInitial":"A.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":713241,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barlow, Jeannie R. B. 0000-0002-0799-4656 jbarlow@usgs.gov","orcid":"https://orcid.org/0000-0002-0799-4656","contributorId":3701,"corporation":false,"usgs":true,"family":"Barlow","given":"Jeannie","email":"jbarlow@usgs.gov","middleInitial":"R. B.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":394,"text":"Mississippi Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":713242,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X bjurgens@usgs.gov","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":127842,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","email":"bjurgens@usgs.gov","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":713243,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McMahon, Peter B. 0000-0001-7452-2379 pmcmahon@usgs.gov","orcid":"https://orcid.org/0000-0001-7452-2379","contributorId":724,"corporation":false,"usgs":true,"family":"McMahon","given":"Peter","email":"pmcmahon@usgs.gov","middleInitial":"B.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":713244,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carmichael, John K. 0000-0003-1099-841X jkcarmic@usgs.gov","orcid":"https://orcid.org/0000-0003-1099-841X","contributorId":4554,"corporation":false,"usgs":true,"family":"Carmichael","given":"John","email":"jkcarmic@usgs.gov","middleInitial":"K.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":713245,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70192235,"text":"70192235 - 2017 - Climate-driven variability in the occurrence of major floods across North America and Europe","interactions":[],"lastModifiedDate":"2017-10-24T12:18:51","indexId":"70192235","displayToPublicDate":"2017-09-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Climate-driven variability in the occurrence of major floods across North America and Europe","docAbstract":"Concern over the potential impact of anthropogenic climate change on flooding has led to a proliferation of studies examining past flood trends. Many studies have analysed annual-maximum flow trends but few have quantified changes in major (25–100 year return period) floods, i.e. those that have the greatest societal impacts. Existing major-flood studies used a limited number of very large catchments affected to varying degrees by alterations such as reservoirs and urbanisation. In the current study, trends in major-flood occurrence from 1961 to 2010 and from 1931 to 2010 were assessed using a very large dataset (>1200 gauges) of diverse catchments from North America and Europe; only minimally altered catchments were used, to focus on climate-driven changes rather than changes due to catchment alterations. Trend testing of major floods was based on counting the number of exceedances of a given flood threshold within a group of gauges. Evidence for significant trends varied between groups of gauges that were defined by catchment size, location, climate, flood threshold and period of record, indicating that generalizations about flood trends across large domains or a diversity of catchment types are ungrounded. Overall, the number of significant trends in major-flood occurrence across North America and Europe was approximately the number expected due to chance alone. Changes over time in the occurrence of major floods were dominated by multidecadal variability rather than by long-term trends. There were more than three times as many significant relationships between major-flood occurrence and the Atlantic Multidecadal Oscillation than significant long-term trends.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2017.07.027","usgsCitation":"Hodgkins, G.A., Whitfield, P.H., Burn, D.H., Hannaford, J., Renard, B., Stahl, K., Fleig, A.K., Madsen, H., Mediero, L., Korhonen, J., Murphy, C., and Wilson, D., 2017, Climate-driven variability in the occurrence of major floods across North America and Europe: Journal of Hydrology, v. 552, p. 704-717, https://doi.org/10.1016/j.jhydrol.2017.07.027.","productDescription":"14 p.","startPage":"704","endPage":"717","ipdsId":"IP-060483","costCenters":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"links":[{"id":469546,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1016/j.jhydrol.2017.07.027","text":"External Repository"},{"id":347222,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":347151,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S002216941730478X"}],"otherGeospatial":"Europe, North America","volume":"552","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f05121e4b0220bbd9a1d85","contributors":{"authors":[{"text":"Hodgkins, Glenn A. 0000-0002-4916-5565 gahodgki@usgs.gov","orcid":"https://orcid.org/0000-0002-4916-5565","contributorId":2020,"corporation":false,"usgs":true,"family":"Hodgkins","given":"Glenn","email":"gahodgki@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"preferred":true,"id":714910,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Whitfield, Paul H.","contributorId":198041,"corporation":false,"usgs":false,"family":"Whitfield","given":"Paul","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":714911,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burn, Donald H.","contributorId":198042,"corporation":false,"usgs":false,"family":"Burn","given":"Donald","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":714912,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hannaford, Jamie","contributorId":198043,"corporation":false,"usgs":false,"family":"Hannaford","given":"Jamie","email":"","affiliations":[],"preferred":false,"id":714913,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Renard, Benjamin","contributorId":177291,"corporation":false,"usgs":false,"family":"Renard","given":"Benjamin","email":"","affiliations":[],"preferred":false,"id":714914,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stahl, Kerstin","contributorId":198044,"corporation":false,"usgs":false,"family":"Stahl","given":"Kerstin","email":"","affiliations":[],"preferred":false,"id":714915,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fleig, Anne K.","contributorId":198045,"corporation":false,"usgs":false,"family":"Fleig","given":"Anne","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":714916,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Madsen, Henrik","contributorId":198046,"corporation":false,"usgs":false,"family":"Madsen","given":"Henrik","email":"","affiliations":[],"preferred":false,"id":714917,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mediero, Luis","contributorId":198047,"corporation":false,"usgs":false,"family":"Mediero","given":"Luis","email":"","affiliations":[],"preferred":false,"id":714918,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Korhonen, Johanna","contributorId":198048,"corporation":false,"usgs":false,"family":"Korhonen","given":"Johanna","email":"","affiliations":[],"preferred":false,"id":714919,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Murphy, Conor","contributorId":198049,"corporation":false,"usgs":false,"family":"Murphy","given":"Conor","email":"","affiliations":[],"preferred":false,"id":714920,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Wilson, Donna","contributorId":198051,"corporation":false,"usgs":false,"family":"Wilson","given":"Donna","email":"","affiliations":[],"preferred":false,"id":714922,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70194219,"text":"70194219 - 2017 - The planetary data system","interactions":[],"lastModifiedDate":"2018-01-19T16:20:21","indexId":"70194219","displayToPublicDate":"2017-09-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5586,"text":"Lunar and Planetary Information Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"The planetary data system","docAbstract":"In the early 1980s, the Space Science Board (SSB) of the National Research Council was concerned about the poor and inconsistent treatment of scientific information returned from NASA’s space science missions. The SSB formed a panel [The Committee on Data Management and Computation (CODMAC)] to assess the situation and make recommendations to NASA for improvements. The CODMAC panel issued a report [1,2] that led to a number of actions, one of which was the convening of a Planetary Data Workshop in November 1983 [3]. The key findings of that workshop were that (1) important datasets were being irretrievably lost, and (2) the use of planetary data by the wider community is constrained by inaccessibility and a lack of commonality in format and documentation. The report further stated, “Most participants felt the present system (of data archiving and access) is inadequate and immediate changes are necessary to insure retention of and access to these and future datasets.”
","language":"English","publisher":"Lunar and Planetary Institute","usgsCitation":"Acton, C., Slavney, S., Arvidson, R.E., Gaddis, L.R., Gordon, M., and Lavoie, S., 2017, The planetary data system: Lunar and Planetary Information Bulletin, no. 150, p. 2-11.","productDescription":"10 p.","startPage":"2","endPage":"11","ipdsId":"IP-092524","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":350070,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":349087,"type":{"id":15,"text":"Index Page"},"url":"https://www.lpi.usra.edu/publications/newsletters/lpib/lpib150.pdf"}],"issue":"150","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fb5be4b06e28e9c22fa8","contributors":{"authors":[{"text":"Acton, Charles","contributorId":200589,"corporation":false,"usgs":false,"family":"Acton","given":"Charles","affiliations":[],"preferred":false,"id":722743,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Slavney, Susan","contributorId":200590,"corporation":false,"usgs":false,"family":"Slavney","given":"Susan","email":"","affiliations":[],"preferred":false,"id":722744,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arvidson, Raymond E.","contributorId":106626,"corporation":false,"usgs":false,"family":"Arvidson","given":"Raymond","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":722745,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gaddis, Lisa R. 0000-0001-9953-5483 lgaddis@usgs.gov","orcid":"https://orcid.org/0000-0001-9953-5483","contributorId":2817,"corporation":false,"usgs":true,"family":"Gaddis","given":"Lisa","email":"lgaddis@usgs.gov","middleInitial":"R.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":722742,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gordon, Mitchell","contributorId":200591,"corporation":false,"usgs":false,"family":"Gordon","given":"Mitchell","affiliations":[],"preferred":false,"id":722746,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lavoie, Susan","contributorId":200592,"corporation":false,"usgs":false,"family":"Lavoie","given":"Susan","email":"","affiliations":[],"preferred":false,"id":722747,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70189769,"text":"ofr20171095 - 2017 - Results of hydrologic monitoring on landslide-prone coastal bluffs near Mukilteo, Washington","interactions":[],"lastModifiedDate":"2017-08-31T11:32:02","indexId":"ofr20171095","displayToPublicDate":"2017-08-31T12:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1095","title":"Results of hydrologic monitoring on landslide-prone coastal bluffs near Mukilteo, Washington","docAbstract":"A hydrologic monitoring network was installed to investigate landslide hazards affecting the railway corridor along the eastern shore of Puget Sound between Seattle and Everett, near Mukilteo, Washington. During the summer of 2015, the U.S. Geological Survey installed monitoring equipment at four sites equipped with instrumentation to measure rainfall and air temperature every 15 minutes. Two of the four sites are installed on contrasting coastal bluffs, one landslide scarred and one vegetated. At these two sites, in addition to rainfall and air temperature, volumetric water content, pore pressure, soil suction, soil temperature, and barometric pressure were measured every 15 minutes. The instrumentation was designed to supplement landslide-rainfall thresholds developed by the U.S. Geological Survey with a long-term goal of advancing the understanding of the relationship between landslide potential and hydrologic forcing along the coastal bluffs. Additionally, the system was designed to function as a prototype monitoring system to evaluate criteria for site selection, instrument selection, and placement of instruments. The purpose of this report is to describe the monitoring system, present the data collected since installation, and describe significant events represented within the dataset, which is published as a separate data release. The findings provide insight for building and configuring larger, modular monitoring networks.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171095","collaboration":"Prepared as part of a Technical Assistance Agreement with Sound Transit","usgsCitation":"Smith, J.B., Baum, R.L., Mirus, B.B., Michel, A.R., and Stark, B., 2017, Results of hydrologic monitoring on landslide-prone coastal bluffs near Mukilteo, Washington: U.S. Geological Survey Open-File Report 2017–1095, 48 p., https://doi.org/10.3133/ofr20171095.","productDescription":"Report: vii, 48 p.; Data Release","numberOfPages":"60","onlineOnly":"Y","ipdsId":"IP-086276","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":345113,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1095/ofr20171095.pdf","text":"Report","size":"4.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1095"},{"id":345112,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1095/coverthb.jpg"},{"id":345114,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7NZ85WX","text":"USGS Data Release","description":"USGS data release","linkHelpText":"Results of hydrologic monitoring on landslide-prone coastal bluffs near Mukilteo, Washington"}],"country":"United States","state":"Washington","city":"Mukilteo","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.35748291015625,\n 47.87352572966863\n ],\n [\n -122.29225158691406,\n 47.87352572966863\n ],\n [\n -122.29225158691406,\n 47.954064687296885\n ],\n [\n -122.35748291015625,\n 47.954064687296885\n ],\n [\n -122.35748291015625,\n 47.87352572966863\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
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Box 25046, MS-966
Denver, CO 80225
In this study, comprehensive two-dimensional (2D) gas chromatography–mass spectrometry (GC–MS), atmospheric pressure photoionization (APPI) quadrupole-Orbitrap mass spectrometry (MS), and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) were used to study the aromatic fractions of crude oil and oil shale pyrolysates (shale oils). The collected data were compared and combined in the double bond equivalence (DBE) versus carbon number plot to obtain a more complete understanding of the composition of the oil fractions. The numbers of peaks observed by each technique followed the order 2D GC–MS < Orbitrap MS < FT-ICR MS. The class distributions observed by Orbitrap MS and FT-ICR MS were similar to each other but different from that observed by 2D GC–MS. The DBE and carbon number distributions of the 2D GC–MS and Orbitrap MS data were similar for crude oil aromatics. The FT-ICR MS plots of DBE and carbon number showed an extended range of higher values relative to the other methods. For the aromatic fraction of an oil shale pyrolysate generated by the Fischer assay, only a few nitrogen-containing compounds were observed by 2D GC–MS but a large number of these compounds were detected by Orbitrap MS and FT-ICR MS. This comparison clearly shows that the data obtained from these three techniques can be combined to more completely characterize oil composition. The data obtained by Orbitrap MS and FT-ICR MS agreed well with one another, and the combined DBE versus carbon number plot provided more complete coverage of compounds present in the fractions. In addition, the chemical structure information provided by 2D GC–MS could be matched with the chemical formulas in the DBE versus carbon number plots, providing information not available in ultrahigh-resolution MS results. It was therefore concluded that the combination of 2D GC–MS, Orbitrap MS, and FT-ICR MS in the DBE versus carbon number space facilitates structural assignment of heavy oil components.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.energyfuels.7b00962","usgsCitation":"Cho, Y., Birdwell, J.E., Hur, M., Lee, J., Kim, B., and Kim, S., 2017, Extension of the analytical window for characterizing aromatic compounds in oils using a comprehensive suite of high-resolution mass spectrometry techniques and double bond equivalence versus carbon number plot: Energy & Fuels, v. 31, no. 8, p. 7874-7883, https://doi.org/10.1021/acs.energyfuels.7b00962.","productDescription":"10 p.","startPage":"7874","endPage":"7883","ipdsId":"IP-085425","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":345390,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-20","publicationStatus":"PW","scienceBaseUri":"59a9203ee4b07e1a023ccd99","contributors":{"authors":[{"text":"Cho, Yunju","contributorId":127785,"corporation":false,"usgs":false,"family":"Cho","given":"Yunju","email":"","affiliations":[{"id":7153,"text":"Kyungpook National University, Department of Chemistry, Daegu, South Korea","active":true,"usgs":false}],"preferred":false,"id":709161,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":709160,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hur, Manhoi","contributorId":177161,"corporation":false,"usgs":false,"family":"Hur","given":"Manhoi","email":"","affiliations":[],"preferred":false,"id":709164,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lee, Joonhee","contributorId":196065,"corporation":false,"usgs":false,"family":"Lee","given":"Joonhee","email":"","affiliations":[],"preferred":false,"id":709165,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kim, Byungjoo","contributorId":196063,"corporation":false,"usgs":false,"family":"Kim","given":"Byungjoo","email":"","affiliations":[],"preferred":false,"id":709162,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kim, Sunghwan","contributorId":196064,"corporation":false,"usgs":false,"family":"Kim","given":"Sunghwan","email":"","affiliations":[],"preferred":false,"id":709163,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70190432,"text":"70190432 - 2017 - Lessons from the Tōhoku tsunami: A model for island avifauna conservation prioritization","interactions":[],"lastModifiedDate":"2018-01-08T14:36:12","indexId":"70190432","displayToPublicDate":"2017-08-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Lessons from the Tōhoku tsunami: A model for island avifauna conservation prioritization","docAbstract":"Earthquake-generated tsunamis threaten coastal areas and low-lying islands with sudden flooding. Although human hazards and infrastructure damage have been well documented for tsunamis in recent decades, the effects on wildlife communities rarely have been quantified. We describe a tsunami that hit the world's largest remaining tropical seabird rookery and estimate the effects of sudden flooding on 23 bird species nesting on Pacific islands more than 3,800 km from the epicenter. We used global positioning systems, tide gauge data, and satellite imagery to quantify characteristics of the Tōhoku earthquake-generated tsunami (11 March 2011) and its inundation extent across four Hawaiian Islands. We estimated short-term effects of sudden flooding to bird communities using spatially explicit data from Midway Atoll and Laysan Island, Hawai'i. We describe variation in species vulnerability based on breeding phenology, nesting habitat, and life history traits. The tsunami inundated 21%–100% of each island's area at Midway Atoll and Laysan Island. Procellariformes (albatrosses and petrels) chick and egg losses exceeded 258,500 at Midway Atoll while albatross chick losses at Laysan Island exceeded 21,400. The tsunami struck at night and during the peak of nesting for 14 colonial seabird species. Strongly philopatric Procellariformes were vulnerable to the tsunami. Nonmigratory, endemic, endangered Laysan Teal (Anas laysanensis) were sensitive to ecosystem effects such as habitat changes and carcass-initiated epizootics of avian botulism, and its populations declined approximately 40% on both atolls post-tsunami. Catastrophic flooding of Pacific islands occurs periodically not only from tsunamis, but also from storm surge and rainfall; with sea-level rise, the frequency of sudden flooding events will likely increase. As invasive predators occupy habitat on higher elevation Hawaiian Islands and globally important avian populations are concentrated on low-lying islands, additional conservation strategies may be warranted to increase resilience of island biodiversity encountering tsunamis and rising sea levels.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.3092","usgsCitation":"Reynolds, M.H., Berkowitz, P., Klavitter, J., and Courtot, K., 2017, Lessons from the Tōhoku tsunami: A model for island avifauna conservation prioritization: Ecology and Evolution, v. 7, no. 13, p. 5873-5890, https://doi.org/10.1002/ece3.3092.","productDescription":"18 p.","startPage":"5873","endPage":"5890","ipdsId":"IP-079977","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":469575,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.3092","text":"Publisher Index Page"},{"id":438232,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F708647F","text":"USGS data release","linkHelpText":"Northwestern Hawaiian Islands: Impacts to Avifauna from the Tohoku Tsunami 2011"},{"id":345381,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"13","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-22","publicationStatus":"PW","scienceBaseUri":"59a9203fe4b07e1a023ccd9c","contributors":{"authors":[{"text":"Reynolds, Michelle H. 0000-0001-7253-8158 mreynolds@usgs.gov","orcid":"https://orcid.org/0000-0001-7253-8158","contributorId":3871,"corporation":false,"usgs":true,"family":"Reynolds","given":"Michelle","email":"mreynolds@usgs.gov","middleInitial":"H.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":709125,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berkowitz, Paul","contributorId":192592,"corporation":false,"usgs":false,"family":"Berkowitz","given":"Paul","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":709126,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Klavitter, John","contributorId":196052,"corporation":false,"usgs":false,"family":"Klavitter","given":"John","affiliations":[{"id":6927,"text":"USFWS, National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":709127,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Courtot, Karen 0000-0002-8849-4054 kcourtot@usgs.gov","orcid":"https://orcid.org/0000-0002-8849-4054","contributorId":140002,"corporation":false,"usgs":true,"family":"Courtot","given":"Karen","email":"kcourtot@usgs.gov","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":709128,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70190406,"text":"70190406 - 2017 - Prediction of forest canopy and surface fuels from Lidar and satellite time series data in a bark beetle-affected forest","interactions":[],"lastModifiedDate":"2017-08-30T14:09:12","indexId":"70190406","displayToPublicDate":"2017-08-30T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1689,"text":"Forests","active":true,"publicationSubtype":{"id":10}},"title":"Prediction of forest canopy and surface fuels from Lidar and satellite time series data in a bark beetle-affected forest","docAbstract":"Wildfire behavior depends on the type, quantity, and condition of fuels, and the effect that bark beetle outbreaks have on fuels is a topic of current research and debate. Remote sensing can provide estimates of fuels across landscapes, although few studies have estimated surface fuels from remote sensing data. Here we predicted and mapped field-measured canopy and surface fuels from light detection and ranging (lidar) and Landsat time series explanatory variables via random forest (RF) modeling across a coniferous montane forest in Colorado, USA, which was affected by mountain pine beetles (Dendroctonus ponderosae Hopkins) approximately six years prior. We examined relationships between mapped fuels and the severity of tree mortality with correlation tests. RF models explained 59%, 48%, 35%, and 70% of the variation in available canopy fuel, canopy bulk density, canopy base height, and canopy height, respectively (percent root-mean-square error (%RMSE) = 12–54%). Surface fuels were predicted less accurately, with models explaining 24%, 28%, 32%, and 30% of the variation in litter and duff, 1 to 100-h, 1000-h, and total surface fuels, respectively (%RMSE = 37–98%). Fuel metrics were negatively correlated with the severity of tree mortality, except canopy base height, which increased with greater tree mortality. Our results showed how bark beetle-caused tree mortality significantly reduced canopy fuels in our study area. We demonstrated that lidar and Landsat time series data contain substantial information about canopy and surface fuels and can be used for large-scale efforts to monitor and map fuel loads for fire behavior modeling at a landscape scale.
","language":"English","publisher":"MDPI","doi":"10.3390/f8090322","usgsCitation":"Bright, B.C., Hudak, A.T., Meddens, A.J., Hawbaker, T., Briggs, J.S., and Kennedy, R.E., 2017, Prediction of forest canopy and surface fuels from Lidar and satellite time series data in a bark beetle-affected forest: Forests, v. 9, no. 8, p. 1-22, https://doi.org/10.3390/f8090322.","productDescription":"Article 322; 22 p.","startPage":"1","endPage":"22","ipdsId":"IP-086687","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":469578,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f8090322","text":"Publisher Index Page"},{"id":345363,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.04415893554688,\n 39.904469075530415\n ],\n [\n -105.70220947265625,\n 39.904469075530415\n ],\n [\n -105.70220947265625,\n 40.32246702124231\n ],\n [\n -106.04415893554688,\n 40.32246702124231\n ],\n [\n -106.04415893554688,\n 39.904469075530415\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-30","publicationStatus":"PW","scienceBaseUri":"59a7ced1e4b0fd9b77d092a6","contributors":{"authors":[{"text":"Bright, Benjamin C.","contributorId":196021,"corporation":false,"usgs":false,"family":"Bright","given":"Benjamin","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":709004,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hudak, Andrew T.","contributorId":196022,"corporation":false,"usgs":false,"family":"Hudak","given":"Andrew","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":709005,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meddens, Arjan J.H.","contributorId":140349,"corporation":false,"usgs":false,"family":"Meddens","given":"Arjan","email":"","middleInitial":"J.H.","affiliations":[{"id":13466,"text":"Univ. of Idaho","active":true,"usgs":false}],"preferred":false,"id":709007,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":709003,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Briggs, Jenny S. 0000-0001-7454-6928 jsbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-7454-6928","contributorId":3087,"corporation":false,"usgs":true,"family":"Briggs","given":"Jenny","email":"jsbriggs@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":709008,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kennedy, Robert E.","contributorId":196025,"corporation":false,"usgs":false,"family":"Kennedy","given":"Robert","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":709010,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70186996,"text":"fs20173030 - 2017 - Monitoring the southwestern Wyoming landscape—A foundation for management and science","interactions":[],"lastModifiedDate":"2017-08-29T11:53:05","indexId":"fs20173030","displayToPublicDate":"2017-08-29T12:15:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3030","title":"Monitoring the southwestern Wyoming landscape—A foundation for management and science","docAbstract":"Natural resource monitoring involves repeated collections of resource condition data and analyses to detect possible changes and identify underlying causes of changes. For natural resource agencies, monitoring provides the foundation for management and science. Specifically, analyses of monitoring data allow managers to better understand effects of land-use and other changes on important natural resources and to achieve their conservation and management goals. Examples of natural resources monitored on public lands include wildlife habitats, plant productivity, animal movements and population trends, soil chemistry, and water quality and quantity. Broader definitions of monitoring also recognize the need for scientifically valid data to help support planning efforts and informed decisions, to develop adaptive management strategies, and to provide the means for evaluating management outcomes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20173030","usgsCitation":"Manier, D.J., Anderson, P.J., Assal, T.J., Chong, G.W., and Melcher, C.P., 2017, Monitoring the southwestern Wyoming landscape—A foundation for management and science: U.S. Geological Survey Fact Sheet 2017–3030, 6 p., https://doi.org/10.3133/fs20163030.","productDescription":"6 p.","onlineOnly":"N","ipdsId":"IP-081786","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":345007,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3030/fs20173030.pdf ","text":"Report","size":"2.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3030"},{"id":345006,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3030/coverthb.jpg"}],"country":"United 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Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
This report documents a process used to estimate net infiltration from precipitation, evapotranspiration (ET), and soil data acquired at two sites on Rainier Mesa. Rainier Mesa is a groundwater recharge area within the Nevada National Security Site where recharged water flows through bedrock fractures to a deep (450 meters) water table. The U.S. Geological Survey operated two ET stations on Rainier Mesa from 2002 to 2005 at sites characterized by pinyon-juniper and scrub-brush vegetative cover. Precipitation and ET data were corrected to remove measurement biases and gap-filled to develop continuous datasets. Net infiltration (percolation below the root zone) and changes in root-zone water storage were estimated using a monthly water-balance model.
Site-scale water-budget results indicate that the heavily-fractured welded-tuff bedrock underlying thin (<40 centimeters) topsoil is a critical water source for vegetation during dry periods. Annual precipitation during the study period ranged from fourth lowest (182 millimeters [mm]) to second highest (708 mm) on record (record = 55 years). Annual ET exceeded precipitation during dry years, indicating that the fractured-bedrock reservoir capacity is sufficient to meet atmospheric-evaporative demands and to sustain vegetation through extended dry periods. Net infiltration (82 mm) was simulated during the wet year after the reservoir was rapidly filled to capacity. These results support previous conclusions that preferential fracture flow was induced, resulting in an episodic recharge pulse that was detected in nearby monitoring wells. The occurrence of net infiltration only during the wet year is consistent with detections of water-level rises in nearby monitoring wells that occur only following wet years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175078","collaboration":"Prepared in cooperation with the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office under Interagency Agreement DE-NA0001654","usgsCitation":"Smith, D.W., Moreo, M.T., Garcia, C.A., Halford, K.J., and Fenelon, J.M., 2017, A process to estimate net infiltration using a site-scale water-budget approach, Rainier Mesa, Nevada National Security Site, Nevada, 2002–05: U.S. Geological Survey Scientific Investigations Report 2017-5078, 22 p., https://doi.org/10.3133/sir20175078.","productDescription":"Report: v, 22 p.; Data Release","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-070070","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":345229,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5078/coverthb.jpg"},{"id":345230,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5078/sir20175078.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5078"},{"id":345231,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7222SP5","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Supplemental data from—A process to estimate net infiltration using a site-scale water-budget approach, Rainier Mesa, Nevada National Security Site, 2002-05"}],"country":"United States","state":"Nevada","otherGeospatial":"Rainier Mesa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.1333,\n 37.1\n ],\n [\n -116.2667,\n 37.1\n ],\n [\n -116.2667,\n 37.2667\n ],\n [\n -116.1333,\n 37.2667\n ],\n [\n -116.1333,\n 37.1\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Rd.
Carson City, Nevada 89701
Machine learning techniques were applied to a large (n > 10,000) compliance monitoring database to predict the occurrence of several redox-active constituents in groundwater across a large watershed. Specifically, random forest classification was used to determine the probabilities of detecting elevated concentrations of nitrate, iron, and arsenic in the Fox, Wolf, Peshtigo, and surrounding watersheds in northeastern Wisconsin. Random forest classification is well suited to describe the nonlinear relationships observed among several explanatory variables and the predicted probabilities of elevated concentrations of nitrate, iron, and arsenic. Maps of the probability of elevated nitrate, iron, and arsenic can be used to assess groundwater vulnerability and the vulnerability of streams to contaminants derived from groundwater. Processes responsible for elevated concentrations are elucidated using partial dependence plots. For example, an increase in the probability of elevated iron and arsenic occurred when well depths coincided with the glacial/bedrock interface, suggesting a bedrock source for these constituents. Furthermore, groundwater in contact with Ordovician bedrock has a higher likelihood of elevated iron concentrations, which supports the hypothesis that groundwater liberates iron from a sulfide-bearing secondary cement horizon of Ordovician age. Application of machine learning techniques to existing compliance monitoring data offers an opportunity to broadly assess aquifer and stream vulnerability at regional and national scales and to better understand geochemical processes responsible for observed conditions.
","language":"English","publisher":"AGU","doi":"10.1002/2016WR020197","usgsCitation":"Tesoriero, A., Gronberg, J.A., Juckem, P.F., Miller, M.P., and Austin, B.P., 2017, Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification: Water Resources Research, v. 53, no. 8, p. 7316-7331, https://doi.org/10.1002/2016WR020197.","productDescription":"16 p.","startPage":"7316","endPage":"7331","ipdsId":"IP-080660","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":469580,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016wr020197","text":"Publisher Index Page"},{"id":438235,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TD9VM1","text":"USGS data release","linkHelpText":"Source and model output layers used for the prediction and display of the probability of elevated concentrations of redox-sensitive constituents in groundwater in the Fox-Wolf-Peshtigo watershed in Wisconsin and Michigan"},{"id":345270,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"8","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-23","publicationStatus":"PW","scienceBaseUri":"59a67d3ee4b0fd9b77ce4764","contributors":{"authors":[{"text":"Tesoriero, Anthony J.","contributorId":40207,"corporation":false,"usgs":true,"family":"Tesoriero","given":"Anthony J.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":708857,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gronberg, Jo Ann M.","contributorId":192232,"corporation":false,"usgs":false,"family":"Gronberg","given":"Jo","email":"","middleInitial":"Ann M.","affiliations":[],"preferred":false,"id":708858,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Juckem, Paul F. 0000-0002-3613-1761 pfjuckem@usgs.gov","orcid":"https://orcid.org/0000-0002-3613-1761","contributorId":1905,"corporation":false,"usgs":true,"family":"Juckem","given":"Paul","email":"pfjuckem@usgs.gov","middleInitial":"F.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":708859,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Matthew P. 0000-0002-2537-1823 mamiller@usgs.gov","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":3919,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew","email":"mamiller@usgs.gov","middleInitial":"P.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":708860,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Austin, Brian P.","contributorId":195992,"corporation":false,"usgs":false,"family":"Austin","given":"Brian","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":708861,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70190392,"text":"70190392 - 2017 - Predictive framework for estimating exposure of birds to pharmaceuticals","interactions":[],"lastModifiedDate":"2017-08-30T10:19:54","indexId":"70190392","displayToPublicDate":"2017-08-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Predictive framework for estimating exposure of birds to pharmaceuticals","docAbstract":"We present and evaluate a framework for estimating concentrations of pharmaceuticals over time in wildlife feeding at wastewater treatment plants (WWTPs). The framework is composed of a series of predictive steps involving the estimation of pharmaceutical concentration in wastewater, accumulation into wildlife food items, and uptake by wildlife with subsequent distribution into, and elimination from, tissues. Because many pharmacokinetic parameters for wildlife are unavailable for the majority of drugs in use, a read-across approach was employed using either rodent or human data on absorption, distribution, metabolism, and excretion. Comparison of the different steps in the framework against experimental data for the scenario where birds are feeding on a WWTP contaminated with fluoxetine showed that estimated concentrations in wastewater treatment works were lower than measured concentrations; concentrations in food could be reasonably estimated if experimental bioaccumulation data are available; and read-across from rodent data worked better than human to bird read-across. The framework provides adequate predictions of plasma concentrations and of elimination behavior in birds but yields poor predictions of distribution in tissues. The approach holds promise, but it is important that we improve our understanding of the physiological similarities and differences between wild birds and domesticated laboratory mammals used in pharmaceutical efficacy/safety trials, so that the wealth of data available can be applied more effectively in ecological risk assessments.
","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.3771","usgsCitation":"Bean, T., Arnold, K.E., Lane, J.M., Bergstrom, E., Thomas-Oates, J., Rattner, B.A., and Boxall, A.B., 2017, Predictive framework for estimating exposure of birds to pharmaceuticals: Environmental Toxicology and Chemistry, v. 36, no. 9, p. 2335-2344, https://doi.org/10.1002/etc.3771.","productDescription":"10 p.","startPage":"2335","endPage":"2344","ipdsId":"IP-073187","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":469584,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://eprints.whiterose.ac.uk/112481/1/Bean_et_al_2017_Environmental_Toxicology_and_Chemistry.pdf","text":"External Repository"},{"id":345269,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"9","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-15","publicationStatus":"PW","scienceBaseUri":"59a67d3ee4b0fd9b77ce4761","contributors":{"authors":[{"text":"Bean, Thomas G. 0000-0002-3577-1994 tbean@usgs.gov","orcid":"https://orcid.org/0000-0002-3577-1994","contributorId":195993,"corporation":false,"usgs":true,"family":"Bean","given":"Thomas G.","email":"tbean@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":708864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arnold, Kathryn E.","contributorId":195994,"corporation":false,"usgs":false,"family":"Arnold","given":"Kathryn","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":708865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lane, Julie M.","contributorId":195995,"corporation":false,"usgs":false,"family":"Lane","given":"Julie","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":708866,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bergstrom, Ed","contributorId":195996,"corporation":false,"usgs":false,"family":"Bergstrom","given":"Ed","email":"","affiliations":[],"preferred":false,"id":708867,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thomas-Oates, Jane","contributorId":195997,"corporation":false,"usgs":false,"family":"Thomas-Oates","given":"Jane","email":"","affiliations":[],"preferred":false,"id":708868,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rattner, Barnett A. 0000-0003-3676-2843 brattner@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-2843","contributorId":4142,"corporation":false,"usgs":true,"family":"Rattner","given":"Barnett","email":"brattner@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":708863,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boxall, Allistair B.A.","contributorId":195998,"corporation":false,"usgs":false,"family":"Boxall","given":"Allistair","email":"","middleInitial":"B.A.","affiliations":[],"preferred":false,"id":708869,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70190372,"text":"70190372 - 2017 - Effects of environmental covariates and density on the catchability of fish populations and interpretation of catch per unit effort trends","interactions":[],"lastModifiedDate":"2017-08-29T11:37:39","indexId":"70190372","displayToPublicDate":"2017-08-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"title":"Effects of environmental covariates and density on the catchability of fish populations and interpretation of catch per unit effort trends","docAbstract":"Article for outlet: Fisheries Research. Abstract: Quantifying temporal and spatial trends in abundance or relative abundance is required to evaluate effects of harvest and changes in habitat for exploited and endangered fish populations. In many cases, the proportion of the population or stock that is captured (catchability or capture probability) is unknown but is often assumed to be constant over space and time. We used data from a large-scale mark-recapture study to evaluate the extent of spatial and temporal variation, and the effects of fish density, fish size, and environmental covariates, on the capture probability of rainbow trout (Oncorhynchus mykiss) in the Colorado River, AZ. Estimates of capture probability for boat electrofishing varied 5-fold across five reaches, 2.8-fold across the range of fish densities that were encountered, 2.1-fold over 19 trips, and 1.6-fold over five fish size classes. Shoreline angle and turbidity were the best covariates explaining variation in capture probability across reaches and trips. Patterns in capture probability were driven by changes in gear efficiency and spatial aggregation, but the latter was more important. Failure to account for effects of fish density on capture probability when translating a historical catch per unit effort time series into a time series of abundance, led to 2.5-fold underestimation of the maximum extent of variation in abundance over the period of record, and resulted in unreliable estimates of relative change in critical years. Catch per unit effort surveys have utility for monitoring long-term trends in relative abundance, but are too imprecise and potentially biased to evaluate population response to habitat changes or to modest changes in fishing effort.","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2017.01.005","usgsCitation":"Korman, J., and Yard, M.D., 2017, Effects of environmental covariates and density on the catchability of fish populations and interpretation of catch per unit effort trends: Fisheries Research, v. 189, p. 18-34, https://doi.org/10.1016/j.fishres.2017.01.005.","productDescription":"17 p.","startPage":"18","endPage":"34","ipdsId":"IP-078953","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":461422,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.fishres.2017.01.005","text":"Publisher Index Page"},{"id":345255,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"189","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59a67d3ee4b0fd9b77ce476e","contributors":{"authors":[{"text":"Korman, Josh","contributorId":139960,"corporation":false,"usgs":false,"family":"Korman","given":"Josh","email":"","affiliations":[{"id":13333,"text":"Ecometric Research Inc.","active":true,"usgs":false}],"preferred":false,"id":708761,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yard, Michael D. 0000-0002-6580-6027 myard@usgs.gov","orcid":"https://orcid.org/0000-0002-6580-6027","contributorId":169281,"corporation":false,"usgs":true,"family":"Yard","given":"Michael","email":"myard@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":708760,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70189580,"text":"sir20175079 - 2017 - Groundwater discharge by evapotranspiration, flow of water in unsaturated soil, and stable isotope water sourcing in areas of sparse vegetation, Amargosa Desert, Nye County, Nevada","interactions":[],"lastModifiedDate":"2018-01-24T14:12:49","indexId":"sir20175079","displayToPublicDate":"2017-08-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5079","title":"Groundwater discharge by evapotranspiration, flow of water in unsaturated soil, and stable isotope water sourcing in areas of sparse vegetation, Amargosa Desert, Nye County, Nevada","docAbstract":"This report documents methodology and results of a study to evaluate groundwater discharge by evapotranspiration (GWET) in sparsely vegetated areas of Amargosa Desert and improve understanding of hydrologic-continuum processes controlling groundwater discharge. Evapotranspiration and GWET rates were computed and characterized at three sites over 2 years using a combination of micrometeorological, unsaturated zone, and stable-isotope measurements. One site (Amargosa Flat Shallow [AFS]) was in a sparse and isolated area of saltgrass (Distichlis spicata) where the depth to groundwater was 3.8 meters (m). The second site (Amargosa Flat Deep [AFD]) was in a sparse cover of predominantly shadscale (Atriplex confertifolia) where the depth to groundwater was 5.3 m. The third site (Amargosa Desert Research Site [ADRS]), selected as a control site where GWET is assumed to be zero, was located in sparse vegetation dominated by creosote bush (Larrea tridentata) where the depth to groundwater was 110 m.
Results indicated that capillary rise brought groundwater to within 0.9 m (at AFS) and 3 m (at AFD) of land surface, and that GWET rates were largely controlled by the slow but relatively persistent upward flow of water through the unsaturated zone in response to atmospheric-evaporative demands. Greater GWET at AFS (50 ± 20 millimeters per year [mm/yr]) than at AFD (16 ± 15 mm/yr) corresponded with its shallower depth to the capillary fringe and constantly higher soil-water content. The stable-isotope dataset for hydrogen (δ2H) and oxygen (δ18O) illustrated a broad range of plant-water-uptake scenarios. The AFS saltgrass and AFD shadscale responded to changing environmental conditions and their opportunistic water use included the time- and depth-variable uptake of unsaturated-zone water derived from a combination of groundwater and precipitation. These results can be used to estimate GWET in other areas of Amargosa Desert where hydrologic conditions are similar.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175079","collaboration":"Prepared in cooperation with Nye County, Nevada, and the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office under Interagency Agreement DE-NA0001654","usgsCitation":"Moreo, M.T., Andraski, B.J., and Garcia, C.A., 2017, Groundwater discharge by evapotranspiration, flow of water in unsaturated soil, and stable isotope water sourcing in areas of sparse vegetation, Amargosa Desert, Nye County, Nevada: U.S. Geological Survey Scientific Investigations Report 2017–5079, 55 p., https://doi.org/10.3133/sir20175079.","productDescription":"Report: viii, 55 p.; Data Release","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-081689","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":438237,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ZS2VQR","text":"USGS data release","linkHelpText":"Selected Evapotranspiration Data, Amargosa Desert Research Site, Nye County, Nevada, 7/5/2011-1/1/2017"},{"id":345331,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7R49NZN","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Evapotranspiration, groundwater, and unsaturated-zone data, Amargosa Desert, Nye County, Nevada, 2011-13"},{"id":345286,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5079/sir20175079.pdf","text":"Report","size":"3.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5079"},{"id":345285,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5079/coverthb.jpg"}],"country":"United States","state":"Nevada","county":"Nye County","otherGeospatial":"Amargosa Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.3774871826172,\n 36.3588219885685\n ],\n [\n -116.20548248291016,\n 36.3588219885685\n ],\n [\n -116.20548248291016,\n 36.50384103238002\n ],\n [\n -116.3774871826172,\n 36.50384103238002\n ],\n [\n -116.3774871826172,\n 36.3588219885685\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Rd.
Carson City, Nevada 89701
Low-flow statistics are needed by water-resource engineers, planners, and managers to protect and manage the water resources of Alabama. The accuracy of these statistics is influenced by such factors as length of record and specific hydrologic conditions measured in those records. As such, it is generally recommended that flow statistics be updated about every 10 years to provide improved and representative low-flow characteristics. The previous investigation of low-flow characteristics for Alabama included data through September 1990. Since that time, Alabama has experienced several historic droughts highlighting the need to update the low-flow characteristics at U.S. Geological Survey streamgaging stations. Consequently, this investigation was undertaken in cooperation with a number of State and local agencies to update low-flow frequency and flow-duration statistics at 210 continuous-record streamgaging stations in Alabama and 67 stations from basins that are shared with surrounding States. The flow characteristics were computed on the basis of available data through March 2014.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175083","collaboration":"Prepared in cooperation with Alabama Association of Conservation Districts; Alabama Association of Resource Conservation and Development Councils; Alabama Department of Agriculture and Industries; Alabama Department of Conservation and Natural Resources—Division of Wildlife and Freshwater Fisheries; Alabama Department of Economic and Community Affairs—Office of Water Resources Division; Alabama Farmers Federation; Alabama Power; Choctawhatchee, Pea and Yellow Rivers Watershed Management Authority; Geological Survey of Alabama; and The University of Alabama—Water Policy and Law Institute","usgsCitation":"Feaster, T.D., and Lee, K.G., 2017, Low-flow frequency and flow-duration characteristics of selected streams in Alabama through March 2014: U.S. Geological Survey Scientific Investigations Report 2017–5083, 371 p., https://doi.org/10.3133/sir20175083.","productDescription":"viii, 371 p.","numberOfPages":"383","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-078394","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":345130,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5083/sir20175083.pdf","text":"Report","size":"8.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5083"},{"id":345129,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5083/coverthb.jpg"}],"country":"United States","state":"Alabama","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.956298828125,\n 30.259067203213018\n ],\n [\n -84.517822265625,\n 30.259067203213018\n ],\n [\n -84.517822265625,\n 35.33529320309328\n ],\n [\n -88.956298828125,\n 35.33529320309328\n ],\n [\n -88.956298828125,\n 30.259067203213018\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park
Nashville, TN 37211
The 2012 Coastal Master Plan utilized Habitat Suitability Indices (HSIs) to evaluate potential project effects on wildlife species. Even though HSIs quantify habitat condition, which may not directly correlate to species abundance, they remain a practical and tractable way to assess changes in habitat quality from various restoration actions. As part of the legislatively mandated five year update to the 2012 plan, the wildlife habitat suitability indices were updated and revised using literature and existing field data where available. The outcome of these efforts resulted in improved, or in some cases entirely new suitability indices. This report describes the development of the habitat suitability indices for the American alligator, Alligator mississippiensis.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"2017 Coastal Master Plan","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"Louisiana Coastal Protection and Restoration Authority","publisherLocation":"Baton Rouge, LA","usgsCitation":"Waddle, J., 2017, Alligator, Alligator mississippiensis, habitat suitability index model (Final), 24 p.","productDescription":"24 p.","ipdsId":"IP-069455","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":344529,"type":{"id":11,"text":"Document"},"url":"https://coastal.la.gov/wp-content/uploads/2017/04/Attachment-C3-10_FINAL03.09.2017.pdf","text":"Appendix C3-10 (This Publication)"},{"id":345178,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://coastal.la.gov/our-plan/2017-coastal-master-plan/","text":"2017 Coastal Master Plan"},{"id":345179,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Final","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59a3da30e4b077f005673223","contributors":{"authors":[{"text":"Waddle, J. Hardin 0000-0003-1940-2133 waddleh@usgs.gov","orcid":"https://orcid.org/0000-0003-1940-2133","contributorId":168952,"corporation":false,"usgs":true,"family":"Waddle","given":"J. Hardin","email":"waddleh@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":false,"id":707073,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70190167,"text":"sir20175089 - 2017 - Preliminary assessment of a water-quality monitoring program for total maximum daily loads in Johnson County, Kansas, January 2015 through June 2016","interactions":[],"lastModifiedDate":"2017-08-27T08:44:02","indexId":"sir20175089","displayToPublicDate":"2017-08-25T09:30:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5089","title":"Preliminary assessment of a water-quality monitoring program for total maximum daily loads in Johnson County, Kansas, January 2015 through June 2016","docAbstract":"Municipalities in Johnson County in northeastern Kansas are required to implement stormwater management programs to reduce pollutant discharges, protect water quality, and comply with applicable water-quality regulations in accordance with National Pollutant Discharge Elimination System permits for stormwater discharge. To this end, municipalities collect grab samples at streams entering and leaving their jurisdiction to determine levels of excessive nutrients, sediment, and fecal bacteria to characterize pollutants and understand the factors affecting them.
In 2014, the U.S. Geological Survey and the Johnson County Stormwater Management Program, with input from the Kansas Department of Health and Environment, initiated a 5-year monitoring program to satisfy minimum sampling requirements for each municipality as described by new stormwater permits issued to Johnson County municipalities. The purpose of this report is to provide a preliminary assessment of the monitoring program. The monitoring program is described, a preliminary assessment of the monitoring program design is provided using water-quality data collected during the first 2 years of the program, and the ability of the current monitoring network and sampling plan to provide data sufficient to quantify improvements in water quality resulting from implemented and planned best management practices is evaluated. The information in this initial report may be used to evaluate changes in data collection methods while data collection is still ongoing that may lead to improved data utility.
Discrete water-quality samples were collected at 27 sites and analyzed for nutrients, Escherichia coli (E. coli) bacteria, total suspended solids, and suspended-sediment concentration. In addition, continuous water-quality data (water temperature, pH, dissolved oxygen, specific conductance, turbidity, and nitrate plus nitrite) were collected at one site to characterize variability and provide a basis for comparison to discrete data. Base flow samples indicated that point sources are likely affecting nutrient concentrations and E. coli bacteria densities at several sites. Concentrations of all analytes in storm runoff samples were characterized by substantial variability among sites and samples. About one-half of the sites, representing different watersheds, had storm runoff samples with nitrogen concentrations greater than 10 milligrams per liter. About one-third of the sites, representing different watersheds, had storm runoff samples with total phosphorus concentrations greater than 3 milligrams per liter. Six sites had samples with E. coli densities greater than 100,000 colonies per 100 milliliters of water. Total suspended solids concentrations of about 12,000 milligrams per liter or greater occurred in samples from three sites.
Data collected for this monitoring program may be useful for some general assessment purposes but may also be limited in potential to fully inform stormwater management activities. Valuable attributes of the monitoring program design included incorporating many sites across the county for comparisons among watersheds and municipalities, using fixed-stage samplers to collect multiple samples during single events, collection of base flow samples in addition to storm samples to isolate possible point sources from stormwater sources, and use of continuous monitors to characterize variability. Limiting attributes of the monitoring program design included location of monitoring sites along municipal boundaries to satisfy permit requirements rather than using watershed-based criteria such as locations of tributaries, potential pollutant sources, and implemented management practices. Additional limiting attributes include having a large number of widespread sampling locations, which presented logistical challenges for predicting localized rainfall and collecting and analyzing samples during short timeframes associated with storms, and collecting storm samples at fixed-stage elevations only during the rising limb of storms, which does not characterize conditions over the storm hydrograph. The small number of samples collected per site resulted in a sample size too small to be representative of site conditions, including seasonal and hydrologic variability, and insufficient for meaningful statistical analysis or site-specific modeling.
Several measures could be taken to improve data utility and include redesigning the monitoring network according to watershed characteristics, incorporating a nested design in which data are collected at different scales (watershed, subwatershed, and best management practices), increasing sampling frequency, and combining different methods to allow for flexibility to focus on areas and conditions of particular interest. A monitoring design that would facilitate most of these improvements would be to focus efforts on a limited number of watersheds for several years, then cycle to the next set of watersheds for several years, eventually returning to previously monitored watersheds to document changes.
Redesign of the water-quality monitoring program requires considerable effort and commitment from municipalities of Johnson County. However, the long-term benefit likely is a monitoring program that results in improved stream conditions and more effective management practices and efficient expenditure of resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175089","collaboration":"Prepared in cooperation with the Johnson County [Kans.] Stormwater Management Program","usgsCitation":"Rasmussen, T.J., and Paxson, C.R., 2017, Preliminary assessment of a water-quality monitoring program for total maximum daily loads in Johnson County, Kansas, January 2015 through June 2016: U.S. Geological Survey Scientific Investigations Report 2017–5089, 20 p., https://doi.org/10.3133/sir20175089.","productDescription":"Report: vi, 20 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-082145","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":345128,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7GT5M2G","text":"USGS data release","description":"USGS data release","linkHelpText":"Water-quality data"},{"id":345101,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5089/coverthb.jpg"},{"id":345102,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5089/sir20175089.pdf","text":"Report","size":"2.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5089"}],"country":"United States","state":"Kansas","county":"Johnson County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.08,\n 38.7\n ],\n [\n -94.61,\n 38.7\n ],\n [\n -94.61,\n 39.08\n ],\n [\n -95.08,\n 39.08\n ],\n [\n -95.08,\n 38.7\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049
In 2015, the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) project conducted the Pacific Northwest Stream Quality Assessment (PNSQA) to investigate stream quality across the western part of the Pacific Northwest. The goal of the PNSQA was to assess the health of streams in the region by characterizing multiple water-quality factors that are stressors to in-stream aquatic life and by evaluating the relation between these stressors and the condition of biological communities. The effects of urbanization and agriculture on stream quality for the Puget Lowland and Willamette Valley Level III Ecoregions were the focus of this regional study. Findings will help inform the public and policymakers about human and environmental factors that are the most critical in affecting stream quality and, thus, provide insights into possible strategies to protect or improve the health of streams in the region.
Land-use data were used in the study to identify and select sites within the region that ranged in levels of urban and agricultural development. A total of 88 sites were selected across the region—69 were on streams that explicitly spanned a range of urban land use in their watersheds, 8 were on streams in agricultural watersheds, and 11 were reference sites with little or no development in their watersheds. Depending on the type of land use, sites were sampled for contaminants, nutrients, and sediment for either a 4- or 10-week period during April, May, and June 2015. This water-quality “index period” was immediately followed with an ecological survey of all sites that included stream habitat, benthic algae, benthic macroinvertebrates, and fish. Additionally, streambed sediment was collected during the ecological survey for analysis of sediment chemistry and toxicity testing.
This report provides a detailed description of the specific study components and methods of the PNSQA, including (1) surveys of stream habitat and aquatic biota, (2) discrete water sampling, (3) deployment of passive polar organic chemical integrative samplers for pesticides and pharmaceuticals, and (4) sampling of streambed sediment. At selected study sites, toxicity testing of streambed sediment, continuous water-quality monitoring, and daily pesticide sampling also were conducted and are described.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171103","collaboration":"National Water-Quality Assessment Project","usgsCitation":"Sheibley, R.W., Morace, J.L., Journey, C.A., Van Metre, P.C., Bell, A.H., Nakagaki, Naomi, Button, D.T., and Qi, S.L., 2017, Design and methods of the Pacific Northwest Stream Quality Assessment (PNSQA), 2015: U.S. Geological Survey Open-File Report 2017-1103, 46 p., https://doi.org/10.3133/ofr20171103.","productDescription":"Report: viii, 46 p.; Table; 2 Appendixes","onlineOnly":"Y","ipdsId":"IP-086097","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":345123,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1103/coverthb.jpg"},{"id":345124,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1103/ofr20171103.pdf","text":"Report","size":"18.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1103"},{"id":345125,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1103/ofr20171103_appendixa.xlsx","text":"Appendix A","size":"325 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2017-1103 Appendix A"},{"id":345126,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1103/ofr20171103_appendixb.xlsx","text":"Appendix B","size":"86 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2017-1103 Appendix B"},{"id":345127,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2017/1103/ofr20171103_table4.xlsx","text":"Table 4","size":"37 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2017-1103 Table 4"}],"country":"United States","state":"Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -127,\n 43.25\n ],\n [\n -120,\n 43.25\n ],\n [\n -120,\n 49\n ],\n [\n -127,\n 49\n ],\n [\n -127,\n 43.25\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
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