High radium (Ra) concentrations in potable portions of the Cambrian-Ordovician (C-O) aquifer system were investigated using water-quality data and environmental tracers (3H, 3Hetrit, SF6, 14C and 4Herad) of groundwater age from 80 public-supply wells (PSWs). Groundwater ages were estimated by calibration of tracers to lumped parameter models and ranged from modern (<50 yr) in upgradient, regionally unconfined areas to ancient (>1 Myr) in the most downgradient, confined portions of the potable system. More than 80 and 40 percent of mean groundwater ages were older than 1000 and 50,000 yr, respectively. Anoxic, Fe-reducing conditions and increased mineralization develop with time in the aquifer system and mobilize Ra into solution resulting in the frequent occurrence of combined Ra (Rac = 226Ra + 228Ra) at concentrations exceeding the USEPA MCL of 185 mBq/L (5 pCi/L). The distribution of the three Ra isotopes comprising total Ra (Rat = 224Ra + 226Ra + 228Ra) differed across the aquifer system. The concentrations of 224Ra and 228Ra were strongly correlated and comprised a larger proportion of the Rat concentration in samples from the regionally unconfined area, where arkosic sandstones provide an enhanced source for progeny from the 232Th decay series. 226Ra comprised a larger proportion of the Ratconcentration in samples from downgradient confined regions. Concentrations of Rat were significantly greater in samples from the regionally confined area of the aquifer system because of the increase in 226Ra concentrations there as compared to the regionally unconfined area. 226Ra distribution coefficients decreased substantially with anoxic conditions and increasing ionic strength of groundwater (mineralization), indicating that Ra is mobilized to solution from solid phases of the aquifer as adsorption capacity is diminished. The amount of 226Ra released from solid phases by alpha-recoil mechanisms and retained in solution increases relative to the amount of Ra sequestered by adsorption processes or co-precipitation with barite as adsorption capacity and the concentration of Ba decreases. Although 226Ra occurred at concentrations greater than 224Ra or 228Ra, the ingestion exposure risk was greater for 228Ra owing to its greater toxicity. In addition, 224Ra added substantial alpha-particle radioactivity to potable samples from the C-O aquifer system. Thus, monitoring for Ra isotopes and gross-alpha-activity (GAA) is important in upgradient, regionally unconfined areas as downgradient, and GAA measurements made within 72 h of sample collection would best capture alpha-particle radiation from the short-lived 224Ra.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2017.11.002","usgsCitation":"Stackelberg, P.E., Szabo, Z., and Jurgens, B., 2018, Radium mobility and the age of groundwater in public-drinking-water supplies from the Cambrian-Ordovician aquifer system, north-central USA: Applied Geochemistry, v. 89, p. 34-48, https://doi.org/10.1016/j.apgeochem.2017.11.002.","productDescription":"15 p.","startPage":"34","endPage":"48","ipdsId":"IP-084578","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":469075,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2017.11.002","text":"Publisher Index Page"},{"id":438031,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BR8QP0","text":"USGS data release","linkHelpText":"Data for Radium Mobility and the Age of Groundwater in Public-drinking-water Supplies from the Cambrian-Ordovician Aquifer System, North-Central USA"},{"id":352683,"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 -95.82275390625,\n 38.54816542304656\n ],\n [\n -84.462890625,\n 38.54816542304656\n ],\n [\n -84.462890625,\n 46.66451741754235\n ],\n [\n -95.82275390625,\n 46.66451741754235\n ],\n [\n -95.82275390625,\n 38.54816542304656\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"89","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee742e4b0da30c1bfc1ef","contributors":{"authors":[{"text":"Stackelberg, Paul E. 0000-0002-1818-355X pestack@usgs.gov","orcid":"https://orcid.org/0000-0002-1818-355X","contributorId":1069,"corporation":false,"usgs":true,"family":"Stackelberg","given":"Paul","email":"pestack@usgs.gov","middleInitial":"E.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":731426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Szabo, Zoltan 0000-0002-0760-9607 zszabo@usgs.gov","orcid":"https://orcid.org/0000-0002-0760-9607","contributorId":2240,"corporation":false,"usgs":true,"family":"Szabo","given":"Zoltan","email":"zszabo@usgs.gov","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":false,"id":731427,"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":1503,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant C.","email":"bjurgens@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":731428,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70197434,"text":"70197434 - 2018 - Deciphering the link between doubly uniparental inheritance of mtDNA and sex determination in bivalves: Clues from comparative transcriptomics","interactions":[],"lastModifiedDate":"2018-06-05T09:48:37","indexId":"70197434","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3832,"text":"Genome Biology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Deciphering the link between doubly uniparental inheritance of mtDNA and sex determination in bivalves: Clues from comparative transcriptomics","docAbstract":"Bivalves exhibit an astonishing diversity of sexual systems and sex-determining mechanisms. They can be gonochoric, hermaphroditic or androgenetic, with both genetic and environmental factors known to determine or influence sex. One unique sex-determining system involving the mitochondrial genome has also been hypothesized to exist in bivalves with doubly uniparental inheritance (DUI) of mtDNA. However, the link between DUI and sex determination remains obscure. In this study, we performed a comparative gonad transcriptomics analysis for two DUI-possessing freshwater mussel species to better understand the mechanisms underlying sex determination and DUI in these bivalves. We used a BLAST reciprocal analysis to identify orthologs between Venustaconcha ellipsiformis and Utterbackia peninsularis and compared our results with previously published sex-specific bivalve transcriptomes to identify conserved sex-determining genes. We also compared our data with other DUI species to identify candidate genes possibly involved in the regulation of DUI. A total of ∼12,000 orthologous relationships were found, with 2,583 genes differentially expressed in both species. Among these genes, key sex-determining factors previously reported in vertebrates and in bivalves (e.g., Sry, Dmrt1, Foxl2) were identified, suggesting that some steps of the sex-determination pathway may be deeply conserved in metazoans. Our results also support the hypothesis that a modified ubiquitination mechanism could be responsible for the retention of the paternal mtDNA in male bivalves, and revealed that DNA methylation could also be involved in the regulation of DUI. Globally, our results suggest that sets of genes associated with sex determination and DUI are similar in distantly-related DUI species.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gbe/evy019","usgsCitation":"Capt, C., Renaut, S., Ghiselli, F., Milani, L., Johnson, N.A., Sietman, B.E., Stewart, D., and Breton, S., 2018, Deciphering the link between doubly uniparental inheritance of mtDNA and sex determination in bivalves: Clues from comparative transcriptomics: Genome Biology and Evolution, v. 10, no. 2, p. 577-590, https://doi.org/10.1093/gbe/evy019.","productDescription":"14 p.","startPage":"577","endPage":"590","ipdsId":"IP-092196","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":469078,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gbe/evy019","text":"Publisher Index Page"},{"id":354709,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"2","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-19","publicationStatus":"PW","scienceBaseUri":"5b46e5d4e4b060350a15d226","contributors":{"authors":[{"text":"Capt, Charlotte","contributorId":205385,"corporation":false,"usgs":false,"family":"Capt","given":"Charlotte","email":"","affiliations":[{"id":37091,"text":"Université de Montréal","active":true,"usgs":false}],"preferred":false,"id":737135,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Renaut, Sébastien","contributorId":205386,"corporation":false,"usgs":false,"family":"Renaut","given":"Sébastien","affiliations":[{"id":37091,"text":"Université de Montréal","active":true,"usgs":false}],"preferred":false,"id":737136,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ghiselli, Fabrizio","contributorId":205387,"corporation":false,"usgs":false,"family":"Ghiselli","given":"Fabrizio","email":"","affiliations":[{"id":37091,"text":"Université de Montréal","active":true,"usgs":false}],"preferred":false,"id":737137,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Milani, Liliana","contributorId":205388,"corporation":false,"usgs":false,"family":"Milani","given":"Liliana","email":"","affiliations":[{"id":37091,"text":"Université de Montréal","active":true,"usgs":false}],"preferred":false,"id":737138,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Nathan A. 0000-0001-5167-1988 najohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-5167-1988","contributorId":4175,"corporation":false,"usgs":true,"family":"Johnson","given":"Nathan","email":"najohnson@usgs.gov","middleInitial":"A.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":737134,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sietman, Bernard E.","contributorId":196565,"corporation":false,"usgs":false,"family":"Sietman","given":"Bernard","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":737139,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stewart, Donald","contributorId":205389,"corporation":false,"usgs":false,"family":"Stewart","given":"Donald","affiliations":[{"id":37092,"text":"Acadia University","active":true,"usgs":false}],"preferred":false,"id":737140,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Breton, Sophie 0000-0002-8286-486X","orcid":"https://orcid.org/0000-0002-8286-486X","contributorId":196560,"corporation":false,"usgs":false,"family":"Breton","given":"Sophie","email":"","affiliations":[],"preferred":false,"id":737141,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70197459,"text":"70197459 - 2018 - Response to Lisovski et al.","interactions":[],"lastModifiedDate":"2018-06-05T14:14:50","indexId":"70197459","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1352,"text":"Current Biology","active":true,"publicationSubtype":{"id":10}},"title":"Response to Lisovski et al.","docAbstract":"Lisovski et al. [1] describe the widely recognized limitations of light-level geolocator data for identifying short-distance latitudinal movements, recommend that caution be used when interpreting such data, intimated that we did not use such caution and argued that environmental shading likely explained the Golden-winged Warbler (Vermivora chrysoptera) movements described in our 2015 report [2] . Lisovski et al. [1] conclude that the bird movements we reported could not be disentangled from estimation error in stationary animals caused by environmental shading. We argue that, to the contrary, these hypotheses can easily be disentangled because the premise that environmental shading caused synchronous and parallel error among geolocators is false. With their assertion that our location estimates could be biased by >3,500 km on a day with no observable local sources of shading, Lisovski et al. [1] have taken a position of incredulity toward all geolocator-based animal movement data published to date.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.cub.2017.12.025","usgsCitation":"Streby, H.M., Kramer, G.R., Peterson, S.M., Lehman, J.A., Buehler, D.A., and Andersen, D.E., 2018, Response to Lisovski et al.: Current Biology, v. 28, no. 3, p. R101-R102, https://doi.org/10.1016/j.cub.2017.12.025.","productDescription":"2 p.","startPage":"R101","endPage":"R102","ipdsId":"IP-092344","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":469069,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.cub.2017.12.025","text":"Publisher Index Page"},{"id":354727,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e5d4e4b060350a15d222","contributors":{"authors":[{"text":"Streby, Henry M.","contributorId":11024,"corporation":false,"usgs":false,"family":"Streby","given":"Henry","email":"","middleInitial":"M.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":737286,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kramer, Gunnar R.","contributorId":94184,"corporation":false,"usgs":false,"family":"Kramer","given":"Gunnar","email":"","middleInitial":"R.","affiliations":[{"id":34539,"text":"Minnesota Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":737287,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, Sean M.","contributorId":9354,"corporation":false,"usgs":false,"family":"Peterson","given":"Sean","email":"","middleInitial":"M.","affiliations":[{"id":34539,"text":"Minnesota Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false},{"id":13013,"text":"Department of Environmental Science, Policy and Management, University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":737288,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lehman, Justin A.","contributorId":166944,"corporation":false,"usgs":false,"family":"Lehman","given":"Justin","email":"","middleInitial":"A.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":737289,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buehler, David A.","contributorId":176238,"corporation":false,"usgs":false,"family":"Buehler","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":737290,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":199408,"corporation":false,"usgs":true,"family":"Andersen","given":"David","email":"dea@usgs.gov","middleInitial":"E.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":737241,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70196274,"text":"70196274 - 2018 - Long‐term trends in fall age ratios of black brant","interactions":[],"lastModifiedDate":"2018-03-30T10:50:59","indexId":"70196274","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Long‐term trends in fall age ratios of black brant","docAbstract":"Accurate estimates of the age composition of populations can inform past reproductive success and future population trajectories. We examined fall age ratios (juveniles:total birds) of black brant (Branta bernicla nigricans; brant) staging at Izembek National Wildlife Refuge near the tip of the Alaska Peninsula, southwest Alaska, USA, 1963 to 2015. We also investigated variation in fall age ratios associated with sampling location, an index of flock size, survey effort, day of season, observer, survey platform (boat‐ or land‐based) and tide stage. We analyzed data using logistic regression models implemented in a Bayesian framework. Mean predicted fall age ratio controlling for survey effort, day of year, and temporal and spatial variation was 0.24 (95% CL = 0.23, 0.25). Overall trend in age ratios was −0.6% per year (95% CL = −1.3%, 0.2%), resulting in an approximate 26% decline in the proportion of juveniles over the study period. We found evidence for variation across a range of variables implying that juveniles are not randomly distributed in space and time within Izembek Lagoon. Age ratios varied by location within the study area and were highly variable among years. They decreased with the number of birds aged (an index of flock size) and increased throughout September before leveling off in early October and declining in late October. Age ratios were similar among tide stages and observers and were lower during boat‐based (offshore) than land‐based (nearshore) surveys. Our results indicate surveys should be conducted annually during early to mid‐October to ensure the entire population is present and available for sampling, and throughout Izembek Lagoon to account for spatiotemporal variation in age ratios. Sampling should include a wide range of flock sizes representative of their distribution and occur in flocks located near and off shore. Further research evaluating the cause of declining age ratios in the fall population is necessary to inform management and predict long‐term population dynamics of brant.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.21388","usgsCitation":"Ward, D.H., Amundson, C.L., Stehn, R.A., and Dau, C.P., 2018, Long‐term trends in fall age ratios of black brant: Journal of Wildlife Management, v. 82, no. 2, p. 362-373, https://doi.org/10.1002/jwmg.21388.","productDescription":"12 p.","startPage":"362","endPage":"373","ipdsId":"IP-082174","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":461053,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.21388","text":"Publisher Index Page"},{"id":438037,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13578ZF","text":"USGS data release","linkHelpText":"Brant Age Ratio Model"},{"id":438036,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QIJIU2","text":"USGS data release","linkHelpText":"Data and Model-based Estimates from Black Brant (Branta bernicla nigricans) Fall Age Ratio Surveys at Izembek Lagoon, Alaska"},{"id":352991,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.42987060546875,\n 55.02802211299252\n ],\n [\n -162.46856689453125,\n 55.02802211299252\n ],\n [\n -162.46856689453125,\n 55.51774716789874\n ],\n [\n -163.42987060546875,\n 55.51774716789874\n ],\n [\n -163.42987060546875,\n 55.02802211299252\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"82","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-27","publicationStatus":"PW","scienceBaseUri":"5afee741e4b0da30c1bfc1e7","contributors":{"authors":[{"text":"Ward, David H. 0000-0002-5242-2526 dward@usgs.gov","orcid":"https://orcid.org/0000-0002-5242-2526","contributorId":3247,"corporation":false,"usgs":true,"family":"Ward","given":"David","email":"dward@usgs.gov","middleInitial":"H.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":732022,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Amundson, Courtney L. 0000-0002-0166-7224 camundson@usgs.gov","orcid":"https://orcid.org/0000-0002-0166-7224","contributorId":4833,"corporation":false,"usgs":true,"family":"Amundson","given":"Courtney","email":"camundson@usgs.gov","middleInitial":"L.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":732023,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stehn, Robert A.","contributorId":83986,"corporation":false,"usgs":true,"family":"Stehn","given":"Robert","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":732024,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dau, Christian P.","contributorId":26185,"corporation":false,"usgs":true,"family":"Dau","given":"Christian","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":732025,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196285,"text":"70196285 - 2018 - Image simulation and assessment of the colour and spatial capabilities of the Colour and Stereo Surface Imaging System (CaSSIS) on the ExoMars Trace Gas Orbiter","interactions":[],"lastModifiedDate":"2018-03-30T11:11:48","indexId":"70196285","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3454,"text":"Space Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Image simulation and assessment of the colour and spatial capabilities of the Colour and Stereo Surface Imaging System (CaSSIS) on the ExoMars Trace Gas Orbiter","docAbstract":"This study aims to assess the spatial and visible/near-infrared (VNIR) colour/spectral capabilities of the 4-band Colour and Stereo Surface Imaging System (CaSSIS) aboard the ExoMars 2016 Trace Grace Orbiter (TGO). The instrument response functions for the CaSSIS imager was used to resample spectral libraries, modelled spectra and to construct spectrally (i.e., in I/F space) and spatially consistent simulated CaSSIS image cubes of various key sites of interest and for ongoing scientific investigations on Mars. Coordinated datasets from Mars Reconnaissance Orbiter (MRO) are ideal, and specifically used for simulating CaSSIS. The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) provides colour information, while the Context Imager (CTX), and in a few cases the High-Resolution Imaging Science Experiment (HiRISE), provides the complementary spatial information at the resampled CaSSIS unbinned/unsummed pixel resolution (4.6 m/pixel from a 400-km altitude). The methodology used herein employs a Gram-Schmidt spectral sharpening algorithm to combine the ∼18–36 m/pixel CRISM-derived CaSSIS colours with I/F images primarily derived from oversampled CTX images. One hundred and eighty-one simulated CaSSIS 4-colour image cubes (at 18–36 m/pixel) were generated (including one of Phobos) based on CRISM data. From these, thirty-three “fully”-simulated image cubes of thirty unique locations on Mars (i.e., with 4 colour bands at 4.6 m/pixel) were made. All simulated image cubes were used to test both the colour capabilities of CaSSIS by producing standard colour RGB images, colour band ratio composites (CBRCs) and spectral parameters. Simulated CaSSIS CBRCs demonstrated that CaSSIS will be able to readily isolate signatures related to ferrous (Fe2+) iron- and ferric (Fe3+) iron-bearing deposits on the surface of Mars, ices and atmospheric phenomena. Despite the lower spatial resolution of CaSSIS when compared to HiRISE, the results of this work demonstrate that CaSSIS will not only compliment HiRISE-scale studies of various geological and seasonal phenomena, it will also enhance them by providing additional colour and geologic context through its wider and longer full-colour coverage (
In this study, we demonstrated that the Landsat-8 Operational Land Imager (OLI) sensor is a powerful tool that can provide periodic and system-wide information on the condition of drinking water reservoirs. The OLI is a multispectral radiometer (30 m spatial resolution) that allows ecosystem observations at spatial and temporal scales that allow the environmental community and water managers another means to monitor changes in water quality not feasible with field-based monitoring. Using the provisional Land Surface Reflectance product and field-collected chlorophyll-a (chl-a) concentrations from drinking water monitoring programs in North Carolina and Rhode Island, we compared five established approaches for estimating chl-aconcentrations using spectral data. We found that using the three band reflectance approach with a combination of OLI spectral bands 1, 3, and 5 produced the most promising results for accurately estimating chl-a concentrations in lakes (R2 value of 0.66; root mean square error value of 8.9 µg l−1). Using this model, we forecast the spatial and temporal variability of chl-a for Jordan Lake, a recreational and drinking water source in piedmont North Carolina and several small ponds that supply drinking water in southeastern Rhode Island.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431161.2018.1430912","usgsCitation":"Keith, D., Rover, J., Green, J., Zalewsky, B., Charpentier, M., Hursby, G., and Bishop, J., 2018, Monitoring algal blooms in drinking water reservoirs using the Landsat-8 Operational Land Imager: International Journal of Remote Sensing, v. 39, no. 9, p. 2818-2846, https://doi.org/10.1080/01431161.2018.1430912.","productDescription":"29 p.","startPage":"2818","endPage":"2846","ipdsId":"IP-096964","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":469076,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6020680","text":"External Repository"},{"id":355407,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.2,\n 35.6333\n ],\n [\n -78.8333,\n 35.6333\n ],\n [\n -78.8333,\n 35.8833\n ],\n [\n -79.2,\n 35.8833\n ],\n [\n -79.2,\n 35.6333\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"9","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-29","publicationStatus":"PW","scienceBaseUri":"5b46e5d2e4b060350a15d218","contributors":{"authors":[{"text":"Keith, Darryl","contributorId":206068,"corporation":false,"usgs":false,"family":"Keith","given":"Darryl","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":739317,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rover, Jennifer 0000-0002-3437-4030","orcid":"https://orcid.org/0000-0002-3437-4030","contributorId":26162,"corporation":false,"usgs":true,"family":"Rover","given":"Jennifer","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":739316,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Green, Jason","contributorId":206069,"corporation":false,"usgs":false,"family":"Green","given":"Jason","email":"","affiliations":[{"id":37231,"text":"NC DENR","active":true,"usgs":false}],"preferred":false,"id":739318,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zalewsky, Brian","contributorId":206070,"corporation":false,"usgs":false,"family":"Zalewsky","given":"Brian","email":"","affiliations":[{"id":37232,"text":"RI DEMP","active":true,"usgs":false}],"preferred":false,"id":739319,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Charpentier, Mike","contributorId":206071,"corporation":false,"usgs":false,"family":"Charpentier","given":"Mike","email":"","affiliations":[{"id":37233,"text":"Raytheon Company","active":true,"usgs":false}],"preferred":false,"id":739320,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hursby, Glen","contributorId":206072,"corporation":false,"usgs":false,"family":"Hursby","given":"Glen","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":739321,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bishop, Joseph","contributorId":206073,"corporation":false,"usgs":false,"family":"Bishop","given":"Joseph","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":739322,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70194932,"text":"70194932 - 2018 - Mineral commodity summaries 2018","interactions":[],"lastModifiedDate":"2018-06-08T09:10:46","indexId":"70194932","displayToPublicDate":"2018-01-31T15:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":6,"text":"USGS Unnumbered Series"},"seriesTitle":{"id":368,"text":"Mineral Commodity Summaries","active":false,"publicationSubtype":{"id":6}},"title":"Mineral commodity summaries 2018","docAbstract":"This report is the earliest Government publication to furnish estimates covering 2017 nonfuel mineral industry data. Data sheets contain information on the domestic industry structure, Government programs, tariffs, and 5-year salient statistics for more than 90 individual minerals and materials.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/70194932","usgsCitation":"U.S. Geological Survey, 2018, Mineral commodity summaries 2018: U.S. Geological Survey, 200 p., https://doi.org/10.3133/70194932.","productDescription":"200 p.","numberOfPages":"204","ipdsId":"IP-094560","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":350834,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/graphics/minerals-commodity-2018.jpg"},{"id":350835,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://minerals.usgs.gov/minerals/pubs/mcs/","text":"Mineral Commodity Summaries Index Page","linkFileType":{"id":5,"text":"html"},"description":"Link to page with all USGS Mineral Commodities Summaries"}],"contact":"Director, National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
Digital flood-inundation maps for a 12.6-mile reach of the Withlacoochee River from Skipper Bridge Road to St. Augustine Road (Georgia State Route 133) were developed to depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the U.S. Geological Survey (USGS) streamgage at Withlacoochee River at Skipper Bridge Road, near Bemiss, Ga. (023177483). Real-time stage information from this streamgage can be used with these maps to estimate near real-time areas of inundation. The forecasted peak-stage information for the USGS streamgage at Withlacoochee River at Skipper Bridge Road, near Bemiss, Ga. (023177483), can be used in conjunction with the maps developed for this study to show predicted areas of flood inundation.
A one-dimensional step-backwater model was developed using the U.S. Army Corps of Engineers Hydrologic Engineer-ing Center’s River Analysis System (HEC–RAS) software for the Withlacoochee River and was used to compute flood profiles for a 12.6-mile reach of the Withlacoochee River. The hydraulic model was then used to simulate 23 water-surface profiles at 1.0-foot (ft) intervals at the Withlacoochee River near the Bemiss streamgage. The profiles ranged from the National Weather Service action stage of 10.7 ft, which is 131.0 ft above the North American Vertical Datum of 1988 (NAVD 88), to a stage of 32.7 ft, which is 153.0 ft above NAVD 88. The simulated water-surface profiles were then combined with a geographic information system digital elevation model—derived from light detection and ranging (lidar) data having a 4.0-ft horizontal resolution—to delineate the area flooded at each 1.0-ft interval of stream stage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185011","collaboration":"Prepared in cooperation with the City of Valdosta, Georgia, and Lowndes County, Georgia","usgsCitation":"Musser, J.W., 2018, Flood-inundation maps for the Withlacoochee River from Skipper Bridge Road to St. Augustine Road, within the City of Valdosta, Georgia, and Lowndes County, Georgia: U.S. Geological Survey Scientific Investigations Report 2018–5011, 15 p., https://doi.org/10.3133/sir20185011.","productDescription":"Report: viii, 18 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087876","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":350785,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5011/coverthb.jpg"},{"id":350787,"rank":3,"type":{"id":30,"text":"Data 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South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
In Autumn 2015, USA National Phenology Network (USA-NPN) staff implemented new U.S. Geological Survey (USGS) data-management policies intended to ensure that the results of Federally funded research are made available to the public. The effort aimed both to improve USA-NPN data releases and to provide a model for similar programs within the USGS. This report provides an overview of the steps taken to ensure compliance, following the USGS Science Data Lifecycle, and provides lessons learned about the data-release process for USGS program leaders and data managers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181007","usgsCitation":"Rosemartin, A., Langseth, M.L., Crimmins, T.M., and Weltzin, J.F., 2018, Development and release of phenological data products—A case study in compliance with federal open data policy: U.S. Geological Survey Open-File Report 2018–1007, 13 p., https://doi.org/10.3133/ofr20181007.","productDescription":"iv, 13 p.","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-090322","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true}],"links":[{"id":350850,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1007/coverthb.jpg"},{"id":350851,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1007/ofr20181007.pdf","text":"Report","size":"350 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1007"}],"contact":"Ecosystems Mission Area
U.S. Geological Survey
12201 Sunrise Valley Dr., MS 300
Reston, VA 20192
Two identical Radar Stage Sensors from Forest Technology Systems were evaluated to determine if they are suitable for U.S. Geological Survey (USGS) hydrologic data collection. The sensors were evaluated in laboratory conditions to evaluate the distance accuracy of the sensor over the manufacturer’s specified operating temperatures and distance to water ranges. Laboratory results were compared to the manufacturer’s accuracy specification of ±0.007 foot (ft) and the USGS Office of Surface Water (OSW) policy requirement that water-level sensors have a measurement uncertainty of no more than 0.01 ft or 0.20 percent of the indicated reading. Both of the sensors tested were within the OSW policy requirement in both laboratory tests and within the manufacturer’s specification in the distance to water test over tested distances from 3 to 15 ft. In the temperature chamber test, both sensors were within the manufacturer’s specification for more than 90 percent of the data points collected over a temperature range of –40 to +60 degrees Celsius at a fixed distance of 8 ft. One sensor was subjected to an SDI-12 communication test, which it passed. A field test was conducted on one sensor at a USGS field site near Landon, Mississippi, from February 5 to March 29, 2016. Water-level measurements made by the radar during the field test were in agreement with those made by the Sutron Accubar Constant Flow Bubble Gauge.
Upon the manufacturer’s release of updated firmware version 1.09, additional SDI-12 and temperature testing was performed to evaluate added SDI-12 functions and verify that performance was unaffected by the update. At this time, an Axiom data logger is required to perform a firmware update on this sensor. The data confirmed the results of the original test. Based on the test results, the Radar Stage Sensor is a suitable choice for USGS hydrologic data collection.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171085","usgsCitation":"Kunkle, G.A., 2018, Evaluation of the Radar Stage Sensor manufactured by Forest Technology Systems—Results of laboratory and field testing: U.S. Geological Survey Open-File Report 2017–1085, 12 p., https://doi.org/10.3133/ofr20171085.","productDescription":"Report: iv, 12 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-083860","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":350803,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71C1VSR","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Evaluation of the Radar Stage Sensor Manufactured by Forest Technology Systems, Incorporated—Results of Laboratory and Field Testing"},{"id":350800,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1085/ofr20171085.pdf","text":"Report","size":"918 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1085"},{"id":350799,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1085/coverthb.jpg"}],"contact":"Chief, Hydrologic Instrumentation Facility
U.S. Geological Survey
Building 2101
Stennis Space Center, MS 39529
The U.S. Geological Survey (USGS) maintains and operates more than 8,200 continuous streamgages nationwide. Types of data that may be collected, computed, and stored for streamgages include streamgage height (water-surface elevation), streamflow, and water quality. The streamflow data allow scientists and engineers to calculate streamflow statistics, such as the 1-percent annual exceedance probability flood (also known as the 100-year flood), the mean flow, and the 7-day, 10-year low flow, which are used by managers to make informed water resource management decisions, at each streamgage location. Researchers, regulators, and managers also commonly need physical characteristics (basin characteristics) that describe the unique properties of a basin. Common uses for streamflow statistics and basin characteristics include hydraulic design, water-supply management, water-use appropriations, and flood-plain mapping for establishing flood-insurance rates and land-use zones. The USGS periodically publishes reports that update the values of basin characteristics and streamflow statistics at selected gaged locations (locations with streamgages), but these studies usually only update a subset of streamgages, making data retrieval difficult. Additionally, streamflow statistics and basin characteristics are most often needed at ungaged locations (locations without streamgages) for which published streamflow statistics and basin characteristics do not exist.
Missouri StreamStats is a web-based geographic information system that was created by the USGS in cooperation with the Missouri Department of Natural Resources to provide users with access to an assortment of tools that are useful for water-resources planning and management. StreamStats allows users to easily obtain the most recent published streamflow statistics and basin characteristics for streamgage locations and to automatically calculate selected basin characteristics and estimate streamflow statistics at ungaged locations.
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U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Demand for high-volume, short duration water withdrawals could create water stress to aquatic organisms in Fayetteville Shale streams sourced for hydraulic fracturing fluids. We estimated potential water stress using permitted water withdrawal volumes and actual water withdrawals compared to monthly median, low, and high streamflows. Risk for biological stress was considered at 20% of long-term median and 10% of high- and low-flow thresholds. Future well build-out projections estimated potential for continued stress. Most water was permitted from small, free-flowing streams and “frack” ponds (dammed streams). Permitted 12-h pumping volumes exceeded median streamflow at 50% of withdrawal sites in June, when flows were low. Daily water usage, from operator disclosures, compared to median streamflow showed possible water stress in 7–51% of catchments from June–November, respectively. If 100% of produced water was recycled, per-well water use declined by 25%, reducing threshold exceedance by 10%. Future water stress was predicted to occur in fewer catchments important for drinking water and species of conservation concern due to the decline in new well installations and increased use of recycled water. Accessible and precise withdrawal and streamflow data are critical moving forward to assess and mitigate water stress in streams that experience high-volume withdrawals.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.7b03304","usgsCitation":"Entrekin, S., Trainor, A., Saiers, J., Patterson, L., Maloney, K.O., Fargione, J., Kiesecker, J.M., Baruch-Mordo, S., Konschnik, K.E., Wiseman, H., Nicot, J., and Ryan, J.N., 2018, Water stress from high-volume hydraulic fracturing potentially threatens aquatic biodiversity and ecosystem services in Arkansas, United States: Environmental Science & Technology, v. 52, no. 4, p. 2349-2358, https://doi.org/10.1021/acs.est.7b03304.","productDescription":"10 p.","startPage":"2349","endPage":"2358","ipdsId":"IP-079892","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":352824,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","volume":"52","issue":"4","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-31","publicationStatus":"PW","scienceBaseUri":"5afee744e4b0da30c1bfc211","contributors":{"authors":[{"text":"Entrekin, Sally","contributorId":147949,"corporation":false,"usgs":false,"family":"Entrekin","given":"Sally","affiliations":[{"id":16964,"text":"University of Central Arkansas","active":true,"usgs":false}],"preferred":false,"id":731854,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Trainor, Anne","contributorId":191831,"corporation":false,"usgs":false,"family":"Trainor","given":"Anne","affiliations":[],"preferred":false,"id":731855,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Saiers, James","contributorId":191832,"corporation":false,"usgs":false,"family":"Saiers","given":"James","affiliations":[],"preferred":false,"id":731856,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Patterson, Lauren","contributorId":203604,"corporation":false,"usgs":false,"family":"Patterson","given":"Lauren","affiliations":[],"preferred":false,"id":731857,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maloney, Kelly O. 0000-0003-2304-0745 kmaloney@usgs.gov","orcid":"https://orcid.org/0000-0003-2304-0745","contributorId":4636,"corporation":false,"usgs":true,"family":"Maloney","given":"Kelly","email":"kmaloney@usgs.gov","middleInitial":"O.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":731858,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fargione, Joseph","contributorId":191828,"corporation":false,"usgs":false,"family":"Fargione","given":"Joseph","affiliations":[],"preferred":false,"id":731859,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kiesecker, Joseph M.","contributorId":146679,"corporation":false,"usgs":false,"family":"Kiesecker","given":"Joseph","email":"","middleInitial":"M.","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":731860,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Baruch-Mordo, Sharon","contributorId":191830,"corporation":false,"usgs":false,"family":"Baruch-Mordo","given":"Sharon","email":"","affiliations":[],"preferred":false,"id":731861,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Konschnik, Katherine E.","contributorId":191826,"corporation":false,"usgs":false,"family":"Konschnik","given":"Katherine","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":731862,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wiseman, Hannah","contributorId":191827,"corporation":false,"usgs":false,"family":"Wiseman","given":"Hannah","affiliations":[],"preferred":false,"id":731863,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Nicot, Jean-Philippe","contributorId":175575,"corporation":false,"usgs":false,"family":"Nicot","given":"Jean-Philippe","email":"","affiliations":[],"preferred":false,"id":731864,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Ryan, Joseph N.","contributorId":54290,"corporation":false,"usgs":false,"family":"Ryan","given":"Joseph","email":"","middleInitial":"N.","affiliations":[{"id":604,"text":"University of Colorado- Boulder","active":false,"usgs":true}],"preferred":false,"id":731865,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70194632,"text":"ofr20171158 - 2018 - Sea surface temperature estimates for the mid-Piacenzian Indian Ocean—Ocean Drilling Program sites 709, 716, 722, 754, 757, 758, and 763","interactions":[],"lastModifiedDate":"2018-01-31T10:21:02","indexId":"ofr20171158","displayToPublicDate":"2018-01-30T12:45:00","publicationYear":"2018","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":"2017-1158","title":"Sea surface temperature estimates for the mid-Piacenzian Indian Ocean—Ocean Drilling Program sites 709, 716, 722, 754, 757, 758, and 763","docAbstract":"Despite the wealth of global paleoclimate data available for the warm period in the middle of the Piacenzian Stage of the Pliocene Epoch (about 3.3 to 3.0 million years ago [Ma]; Dowsett and others, 2013, and references therein), the Indian Ocean has remained a region of sparse geographic coverage in terms of microfossil analysis. In an effort to characterize the surface Indian Ocean during this interval, we examined the planktic foraminifera from Ocean Drilling Program (ODP) sites 709, 716, 722, 754, 757, 758, and 763, encompassing a wide range of oceanographic conditions. We quantitatively analyzed the data for sea surface temperature (SST) estimation using both the modern analog technique (MAT) and a factor analytic transfer function. The data will contribute to the U.S. Geological Survey (USGS) Pliocene Research, Interpretation and Synoptic Mapping (PRISM) Project’s global SST reconstruction and climate model SST boundary condition for the mid-Piacenzian and will become part of the PRISM verification dataset designed to ground-truth Pliocene climate model simulations (Dowsett and others, 2013).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171158","usgsCitation":"Robinson, M.M., Dowsett, H.J., and Stoll, D.K., 2018, Sea surface temperature estimates for the mid-Piacenzian Indian Ocean—Ocean Drilling Program sites 709, 716, 722, 754, 757, 758, and 763: U.S. Geological Survey Open-File Report 2017–1158, 14 p., https://doi.org/10.3133/ofr20171158.","productDescription":"iv, 14 p.","numberOfPages":"19","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087996","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":350488,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1158/coverthb.jpg"},{"id":350489,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1158/ofr20171158.pdf","text":"Report","size":"11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1158"}],"otherGeospatial":"Indian Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 59.80,\n -30.93\n ],\n [\n 112.21,\n -30.93\n ],\n [\n 112.21,\n 16.62\n ],\n [\n 59.80,\n 16.62\n ],\n [\n 59.80,\n -30.93\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Eastern Geology and Paleoclimate Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
926A National Center
Reston, VA 20192
As part of the Barrier Island Evolution Research project, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted a nearshore geophysical survey around the northern Chandeleur Islands, Louisiana, in September 2015. The objective of the project is to improve the understanding of barrier island geomorphic evolution, particularly storm-related depositional and erosional processes that shape the islands over annual to interannual time scales (1–5 years). Collecting geophysical data can help researchers identify relations between the geologic history of the islands and their present day morphology and sediment distribution. High-resolution geophysical data collected along this rapidly changing barrier island system can provide a unique time-series dataset to further the analyses and geomorphological interpretations of this and other coastal systems, improving our understanding of coastal response and evolution over medium-term time scales (months to years). Subbottom profile data were collected in September 2015 offshore of the northern Chandeleur Islands, during USGS Field Activity Number 2015-331-FA. Data products, including raw digital chirp subbottom data, processed subbottom profile images, survey trackline map, navigation files, geographic information system data files and formal Federal Geographic Data Committee metadata, and Field Activity Collection System and operation logs are available for download.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1077","usgsCitation":"Forde, A.S., DeWitt, N.T., Fredericks, J.J., and Miselis, J.L., 2018, Chirp subbottom profile data collected in 2015 from the northern Chandeleur Islands, Louisiana: U.S. Geological Survey Data Series 1077, https://doi.org/10.3133/ds1077.\n\n","productDescription":"HMTL Report; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-091492","costCenters":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"links":[{"id":438048,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U1WJXM","text":"USGS data release","linkHelpText":"Archive of Chirp Subbottom Profile Data Collected in 2018 From the Northern Chandeleur Islands, Louisiana"},{"id":438047,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9E8VRGO","text":"USGS data release","linkHelpText":"Archive of Chirp Subbottom Profile Data Collected in 2017 From the Northern Chandeleur Islands, Louisiana"},{"id":438046,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NFK0K4","text":"USGS data release","linkHelpText":"Archive of Chirp Subbottom Profile Data Collected in 2016 From the Northern Chandeleur Islands, Louisiana"},{"id":350561,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1077/coverthb.jpg"},{"id":350562,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1077/index.html","text":"Report HTML","linkFileType":{"id":5,"text":"html"},"description":"DS 1077"},{"id":350842,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7668C5C","text":"USGS data release","description":"USGS data release","linkHelpText":"Archive of Chirp Subbottom Profile Data Collected in 2015 From the Northern Chandeleur Islands, Louisiana"}],"country":"United States","state":"Louisiana","otherGeospatial":"Northern Chandeleur Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.8167,\n 30.075\n ],\n [\n -88.8833,\n 30.075\n ],\n [\n -88.8833,\n 30.0167\n ],\n [\n -88.8167,\n 30.0167\n ],\n [\n -88.8167,\n 30.075\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
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600 4th Street South
St. Petersburg, FL 33701
Beginning roughly 2.6 million years ago, global climate entered a cooling phase known as the Pleistocene Epoch. As snow in northern latitudes compacted into ice several kilometers thick, it flowed as glaciers southward across the North American continent. These glaciers extended across the northern United States, dramatically altering the landscape they covered. East of the Rocky Mountains, the ice coalesced into continental glaciers (called the Laurentide Ice Sheet) that at times blanketed much of the north-central and northeastern United States. To the west of the Laurentide Ice Sheet, glaciers formed in the mountains of western Canada and the United States and coalesced into the Cordilleran ice sheet; this relatively smaller ice mass extended into the conterminous United States in the northernmost areas of western Montana, Idaho, and Washington. Throughout the Pleistocene, landscape alteration occurred by (1) glacial erosion of the rocks and sediments; (2) redeposition of the eroded earth materials in a form substantially different from their source rocks, in terms of texture and overall character; and (3) disruption of preexisting drainage patterns by the newly deposited sediments. In many cases, pre-glacial drainage systems (including, for example, the Mississippi River) were rerouted because their older drainage courses became blocked with glacial sediment.
The continental glaciers advanced and retreated many times across those areas. During each ice advance, or glaciation, erosion and deposition occurred, and the landscape was again altered. Through successive glaciations, the landscape and the bedrock surface gradually came to resemble their present configurations. As continental ice sheets receded and the Pleistocene ended, erosion and deposition of sediment (for example in stream valleys) continued to shape the landscape up to the present day (albeit to a lesser extent than during glaciation). The interval of time since the last recession of the glaciers is called the Holocene and, together with the Pleistocene, constitutes the Quaternary Period of geologic time; this publication characterizes the three-dimensional geometry of the Quaternary sediments and the bedrock surface that lies beneath.
The pre-glacial landscape was underlain mostly by weathered bedrock generally similar in nature to that found in many areas of the non-glaciated United States. Glacial erosion and redeposition of earth materials produced a young, mineral-rich soil that formed the basis for the highly productive agricultural economy in the U.S. midcontinent. Extensive buried sands and gravels within the glacial deposits also provided a stimulus to other economic sectors by serving as high-quality aquifers supplying groundwater to the region’s industry and cities. An understanding of the three-dimensional distribution of these glacial sediments has direct utility for addressing various societal issues including groundwater quality and supply, and landscape and soil response to earthquake-induced shaking.
The Quaternary sediment thickness map and bedrock topographic map shown here provide a regional overview and are intended to supplement the more detailed work on which they are based. Detailed mapping is particularly useful in populated areas for site-specific planning. In contrast, regional maps such as these serve to place local, detailed mapping in context; to permit the extrapolation of data into unmapped areas; and to depict large-scale regional geologic features and patterns that are beyond the scope of local, detailed mapping. They also can enhance the reader’s general understanding of the region’s landscape and geologic history and provide a source of information for regional decision making that could benefit by improved predictability of bedrock depth beneath the unconsolidated Quaternary sediments. To enable these maps to be analyzed in conjunction with other types of information, this publication also includes the map data in GIS compatible format.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3392","usgsCitation":"Soller, D.R., and Garrity, C.P., 2018, Quaternary sediment thickness and bedrock topography of the glaciated United States east of the Rocky Mountains: U.S. Geological Survey Scientific Investigations Map 3392, 2 sheets, scale 1:5,000,000. https://doi.org/10.3133/sim3392.","productDescription":"2 Sheets: 42.5 x 23.0 inches; Metadata; Read Me; Spatial Data","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-084769","costCenters":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"links":[{"id":350668,"rank":5,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3392/sim3392_spatialdata.zip","text":"Spatial Data","size":"16 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3392","linkHelpText":"– Contains raster image files, layer file, and 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\"}}]}","contact":"National Cooperative Geologic Mapping Program
U.S. Geological Survey
12201 Sunrise Valley Drive Mail Stop 908
Reston, VA 20192
Fax: (703) 648-6937
Flood-damage reduction in the United States has been a longstanding but elusive societal goal. The national strategy for reducing flood damage has shifted over recent decades from a focus on construction of flood-control dams and levee systems to a three-pronged strategy to (1) improve the design and operation of such structures, (2) provide more accurate and accessible flood forecasting, and (3) shift the Federal Emergency Management Agency (FEMA) National Flood Insurance Program to a more balanced, less costly flood-insurance paradigm. Expanding the availability and use of high-quality, three-dimensional (3D) elevation information derived from modern light detection and ranging (lidar) technologies to provide essential terrain data poses a singular opportunity to dramatically enhance the effectiveness of all three components of this strategy. Additionally, FEMA, the National Weather Service, and the U.S. Geological Survey (USGS) have developed tools and joint program activities to support the national strategy.
The USGS 3D Elevation Program (3DEP) has the programmatic infrastructure to produce and provide essential terrain data. This infrastructure includes (1) data acquisition partnerships that leverage funding and reduce duplicative efforts, (2) contracts with experienced private mapping firms that ensure acquisition of consistent, low-cost 3D elevation data, and (3) the technical expertise, standards, and specifications required for consistent, edge-to-edge utility across multiple collection platforms and public access unfettered by individual database designs and limitations.
High-quality elevation data, like that collected through 3DEP, are invaluable for assessing and documenting flood risk and communicating detailed information to both responders and planners alike. Multiple flood-mapping programs make use of USGS streamflow and 3DEP data. Flood insurance rate maps, flood documentation studies, and flood-inundation map libraries are products of these programs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173081","usgsCitation":"Carswell, W.J., Jr., and Lukas, Vicki, 2018, The 3D Elevation Program—Flood risk management: U.S. Geological Survey Fact Sheet 2017-3081, 6 p., https://doi.org/10.3133/fs20173081.","productDescription":"6 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073000","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":350200,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3081/coverthb2.jpg"},{"id":350201,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3081/fs20173081.pdf","text":"Report","size":"4.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3081"}],"contact":"Director, National Geospatial Program
U.S. Geological Survey, MS 511
12201 Sunrise Valley Drive
Reston, VA 20192
The growth rate of reptiles is plastic and often varies among individuals, populations, and years in response to environmental conditions. For an imperiled species, the growth rate of individual animals is an important component of demographic models, and changes in individual growth rates might precede changes in abundance. We analyzed a long-term dataset on the growth of Giant Gartersnakes (Thamnophis gigas) to characterize spatial and temporal variability and evaluate potential environmental predictors of growth. We collected data on the growth in snout–vent length (SVL) of Giant Gartersnakes over 22 yr (1995–2016) from eight sites distributed throughout the Sacramento Valley of California, USA. The von Bertalanffy growth curves indicated male Giant Gartersnakes grew faster toward shorter, asymptotic SVL than did females. Nearly equal variability in growth was attributable to differences among years and among sites. From 2003–2016 we collected data on precipitation, temperature, and the abundance of fish and anuran prey at each site and used these variables as predictors in growth models of Giant Gartersnakes. Snake growth was positively related to the amount of precipitation that fell during the prior water year and the abundance of anurans at a site. Fish and frog abundance interacted to affect snake growth: at low abundances of one prey type, the other positively affected growth, but the slope of this relationship decreased as alternative prey abundance increased. Our results highlight the plasticity of growth in this threatened snake species, point to potential environmental drivers of growth, and provide valuable data for demographic modeling efforts.
","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","doi":"10.1670/17-055","usgsCitation":"Rose, J.P., Halstead, B., Wylie, G.D., and Casazza, M.L., 2018, Spatial and temporal variability in growth of giant gartersnakes: Plasticity, precipitation, and prey: Journal of Herpetology, v. 52, no. 1, p. 40-49, https://doi.org/10.1670/17-055.","productDescription":"10 p.","startPage":"40","endPage":"49","ipdsId":"IP-086235","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":386725,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.78320312499999,\n 38.27268853598095\n ],\n [\n -120.38818359374997,\n 38.27268853598095\n ],\n [\n -120.38818359374997,\n 40.863679665481676\n ],\n [\n -122.78320312499999,\n 40.863679665481676\n ],\n [\n -122.78320312499999,\n 38.27268853598095\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"52","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rose, Jonathan P. 0000-0003-0874-9166 jprose@usgs.gov","orcid":"https://orcid.org/0000-0003-0874-9166","contributorId":199339,"corporation":false,"usgs":true,"family":"Rose","given":"Jonathan","email":"jprose@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":818253,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":818254,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wylie, Glenn D. 0000-0002-7061-6658 glenn_wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7061-6658","contributorId":3052,"corporation":false,"usgs":true,"family":"Wylie","given":"Glenn","email":"glenn_wylie@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":818255,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":818256,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193973,"text":"ofr20171148 - 2018 - Public views of wetlands and waterfowl conservation in the United States—Results of a survey to inform the 2018 update of the North American Waterfowl Management Plan","interactions":[],"lastModifiedDate":"2018-01-24T15:09:07","indexId":"ofr20171148","displayToPublicDate":"2018-01-24T15:20:00","publicationYear":"2018","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":"2017-1148","title":"Public views of wetlands and waterfowl conservation in the United States—Results of a survey to inform the 2018 update of the North American Waterfowl Management Plan","docAbstract":"This report provides information from a general public survey conducted in early 2017 to help inform the North American Waterfowl Management Plan (NAWMP) 2018 update. This report is intended for use by the NAWMP advisory committees and anyone interested in the human dimensions of wetlands and waterfowl management. A mail-out survey was sent to 5,000 addresses in the United States, which were selected randomly in proportion to the population of each State. A total of 1,030 completed surveys representing 49 States were returned, resulting in a 23 percent overall response rate.
When comparing the demographics of the respondents to the U.S. census data, this sample overrepresented people who are male, older, highly educated, and white. Data were weighted on gender and age to make the results more representative of the overall U.S. population. Additionally, this sample had higher participation rates in all wildlife-related recreation activities than has been found in previous studies; this indicates there may have been selection bias, with people interested in nature-related topics more likely to complete the survey. Therefore, results likely represent a segment of the U.S. public that is more oriented toward and aware of wildlife and conservation issues than the general public as a whole. Because of this bias, responses for each question were also broken down by recreationist type (hunters, anglers, wildlife viewers, and no wildlife-related recreation). Additionally, responses for each question were split by administrative flyway (Atlantic, Central, Mississippi, Pacific) and residency (urban, urban cluster, rural) to better understand the different groups.
Most respondents knew of wetlands in their local area or community, and more than half had visited wetlands in the previous 12 months. Of those who had visited wetlands, the most common reasons were for walking/hiking/biking and enjoying nature/picnicking. In addition, this sample was very concerned about the reduction or loss of ecosystem services resulting from wetlands degradation or loss. A majority of respondents were somewhat or very concerned about 9 out of 10 wetlands benefits, with hunting opportunities being the only benefit the majority of people were not concerned about. People were the most concerned about clean water, clean air, and providing a home for wildlife. In contrast, people were least concerned about hunting opportunities and wetlands providing scenic places for inspiration or spiritual renewal. Communication about wetlands that focuses on habitat, clean air, and clean water may resonate with the widest variety of people. However, if communication is targeted toward wildlife-related recreationists, including more information about the recreation benefits of wetlands and emphasizing habitat benefits may be the most effective.
Many people reported having participated in conservation behaviors in the last year. The most popular activity was making the yard more desirable to wildlife, with more than three-fourths of respondents participating, followed by donating money to support wildlife/habitat conservation and talking to others in their community about conservation issues. There was lower participation in conservation behavior specifically related to wetlands and waterfowl, with two-fifths of respondents voting for candidates or ballot issues to support wetlands/waterfowl conservation and one-third advocating for political action to conserve wetlands/waterfowl.
In order to better understand how to reach out to the public on nature-related topics, preferences in information channels and trust in information sources were explored. Respondents were mostly likely to want to receive their information through personal experience, by reading or accessing online content, and through watching visual media online. People were least likely to want to receive information through listening to recorded audio media, attending educational opportunities, and listening to live audio media. These results emphasize the importance of having content available online in an easily accessible and appealing format. Visual media in particular seems to be preferred across a wide variety of people. Additionally, people had the highest trust in scientific organizations, universities/educational organizations, and friends/family/neighbors/colleagues. The least trusted sources were national media/news, religious organizations, and local media/news. Urban respondents had higher trust levels overall, particularly for the government. Hunters and those in rural areas had lower levels of trust in the government but higher trust in family/friends.
In this sample, few respondents reported hunting waterfowl (5 percent) or hunting other game (16 percent) in the last year. Additionally, few respondents said they were very or somewhat likely to hunt waterfowl in the following 12 months. Even after considering that self-selection bias would make it more likely for hunters to respond to the survey, the relatively small number of respondents who identified as hunters reinforces that engagement of other wildlife-related recreationists is critical to meeting the third goal of the NAWMP 2012 revision—to increase numbers of wetlands/waterfowl conservationists. Many people also had negative perceptions of hunting. Half of the respondents stated that hunting would be unpleasant, and two-fifths believed hunting would be boring. In contrast, people had more favorable attitudes toward birdwatching, with only one-sixth saying it would be unpleasant and less than one-third saying it would be boring. A majority of respondents thought they could easily go hunting or birdwatching in the following 12 months. Overall, people had much more positive views toward birdwatching and expressed fewer barriers to participating in it. When asked what would prevent them from hunting, the most frequently stated reasons were moral opposition, no interest, personal health, and time constraints; for birdwatching, the most popular responses were nothing, no interest, and time constraints. These responses indicate it may be beneficial to move beyond hunting and find ways for other groups, such as birdwatchers, to play a more active role in conservation.
Although not many people hunted and many people tended to have negative attitudes toward hunting, over three-fourths of people said they knew a hunter. Given that wildlife viewers, those who did not participate in wildlife-related recreation, and urban residents tended to have negative attitudes toward hunting and (or) were not interested in participating, attempting to recruit them to participate in hunting may not be effective. However, given how many people across all groups knew a hunter and the relatively high levels of trust people had in their friends/family, hunters may be effective ambassadors for promoting waterfowl and wetlands conservation among nonhunters. Additionally, because people had less preference for viewing waterfowl and other game birds compared to their preference for seeing hummingbirds and birds of prey, conservation efforts that extend beyond waterfowl and include other species that benefit from wetlands may have more appeal to a broader range of people.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171148","usgsCitation":"Wilkins, E.J., and Miller, H.M., 2018, Public views of wetlands and waterfowl conservation in the United States—Results of a survey to inform the 2018 update of the North American Waterfowl Management Plan: U.S. Geological Survey Open-File Report 2017–1148, 134 p., https://doi.org/10.3133/ofr20171148.","productDescription":"xii, 134 p.","numberOfPages":"147","onlineOnly":"Y","ipdsId":"IP-088573","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":438050,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7G15ZQ6","text":"USGS data release","linkHelpText":"Results of a U.S. General Public Survey to Inform the 2018 North American Waterfowl Management Plan Update (2017)"},{"id":350493,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1148/ofr20171148.pdf","text":"Report","size":"8.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1148"},{"id":350492,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1148/coverthb.jpg"}],"contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
The U.S. Geological Survey, in cooperation with the New Hampshire Department of Environmental Services and the Department of Health and Human Services, has developed a hydrologic model to assess the effects of short- and long-term climate change on hydrology in New Hampshire. This report documents the model and datasets developed by using the model to predict how climate change will affect the hydrologic cycle and provide data that can be used by State and local agencies to identify locations that are vulnerable to the effects of climate change in areas across New Hampshire.
Future hydrologic projections were developed from the output of five general circulation models for two future climate scenarios. The scenarios are based on projected future greenhouse gas emissions and estimates of land-use and land-cover change within a projected global economic framework. An evaluation of the possible effect of projected future temperature on modeling of evapotranspiration is summarized to address concerns regarding the implications of the future climate on model parameters that are based on climate variables. The results of the model simulations are hydrologic projections indicating increasing streamflow across the State with large increases in streamflow during winter and early spring and general decreases during late spring and summer. Wide spatial variability in changes to groundwater recharge is projected, with general decreases in the Connecticut River Valley and at high elevations in the northern part of the State and general increases in coastal and lowland areas of the State. In general, total winter snowfall is projected to decrease across the State, but there is a possibility of increasing snow in some locations, particularly during November, February, and March. The simulated future changes in recharge and snowfall vary by watershed across the State. This means that each area of the State could experience very different changes, depending on topography or other factors. Therefore, planning for infrastructure and public safety needs to be flexible in order to address the range of possible outcomes indicated by the various model simulations. The absolute magnitude and timing of the daily streamflows, especially the larger floods, are not considered to be reliably simulated compared to changes in frequency and duration of daily streamflows and changes in accumulated monthly and seasonal streamflow volumes.
Simulated current and future streamflow, groundwater recharge, and snowfall datasets include simulated data derived from the five general circulation models used in this study for a current reference time period and two future time periods. Average monthly streamflow time series datasets are provided for 27 streamgages in New Hampshire. Fourteen of the 27 streamgages associated with daily streamflow time series showed a good calibration. Average monthly groundwater recharge and snowfall time series for the same reference time period and two future time periods are also provided for each of the 467 hydrologic response units that compose the model.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175143","collaboration":"Prepared in cooperation with the New Hampshire Department of Environmental Services and Department of Health and Human Services","usgsCitation":"Bjerklie, D.M., and Sturtevant, Luke, 2018, Simulated hydrologic response to climate change during the 21st century in New Hampshire: U.S. Geological Survey Scientific Investigations Report 2017–5143, 53 p., https://doi.org/10.3133/sir20175143.","productDescription":"Report: viii, 53 p.; 4 Tables; Data release","numberOfPages":"66","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-074537","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":395616,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76T0KJZ","text":"USGS data release","description":"USGS data release","linkHelpText":"Thirty- and ninety-year data sets of streamflow, groundwater recharge, and snowfall simulating potential hydrologic response to climate change in the 21st century in New Hampshire"},{"id":350514,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5143/tables/sir20175143_table4.csv","text":"Table 4","size":"10.8 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Streamflow percent change"},{"id":350513,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5143/tables/sir20175143_table3.csv","text":"Table 3","size":"6 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Streamgages in New Hampshire"},{"id":350512,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5143/sir20175143.pdf","text":"Report","size":"14.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5143"},{"id":350511,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5143/coverthb.jpg"},{"id":350515,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5143/tables/sir20175143_table5.csv","text":"Table 5","size":"10 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Mean monthly streamflow percent change"},{"id":350516,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5143/tables/sir20175143_table6.csv","text":"Table 6","size":"10.3 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Mean monthly streamflow percent change standard deviation"}],"country":"United States","state":"New 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Hampshire\",\"nation\":\"USA \"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
101 Pitkin Street
East Hartford, CT 06108
Digital flood-inundation maps for a 9.5-mile reach of the Patoka River in and near the city of Jasper, southwestern Indiana (Ind.), from the streamgage near County Road North 175 East, downstream to State Road 162, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science web site at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage Patoka River at Jasper, Ind. (station number 03375500). The Patoka streamgage is located at the upstream end of the 9.5-mile river reach. Near-real-time stages at this streamgage may be obtained from the USGS National Water Information System at https://waterdata.usgs.gov/ or the National Weather Service Advanced Hydrologic Prediction Service at http://water.weather.gov/ahps/, although flood forecasts and stages for action and minor, moderate, and major flood stages are not currently (2017) available at this site (JPRI3).
Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the most current stage-discharge relation at the Patoka River at Jasper, Ind., streamgage and the documented high-water marks from the flood of April 30, 2017. The calibrated hydraulic model was then used to compute five water-surface profiles for flood stages referenced to the streamgage datum ranging from 15 feet (ft), or near bankfull, to 19 ft. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging [lidar] data having a 0.98 ft vertical accuracy and 4.9 ft horizontal resolution) to delineate the area flooded at each water level.
The availability of these flood-inundation maps, along with real-time stage from the USGS streamgage at the Patoka River at Jasper, Ind., will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175138","collaboration":"Prepared in cooperation with the Indiana Department of Transportation","usgsCitation":"Fowler, K.K., 2018, Flood-inundation maps for the Patoka River in and near Jasper, southwestern Indiana: U.S. Geological Survey Scientific Investigations Report 2017–5138, 11 p., https://doi.org/10.3133/sir20175138.","productDescription":"Report: vii, 11 p.; Data Release","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-086512","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":350479,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5138/coverthb.jpg"},{"id":350480,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5138/sir20175138.pdf","text":"Report","size":"34.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5138"},{"id":350481,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7862DX0","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial Datasets and Surface-Water Hydraulic Model for the Patoka River in and near Jasper, Southwest Indiana, Flood-inundation Study"}],"country":"United States","state":"Indiana","city":"Jasper","otherGeospatial":"Patoka River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.95,\n 38.360839624761944\n ],\n [\n -86.875,\n 38.360839624761944\n ],\n [\n -86.875,\n 38.425\n ],\n [\n -86.95,\n 38.425\n ],\n [\n -86.95,\n 38.360839624761944\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd
Indianapolis, IN 46278
Monitoring vulnerable species is critical for their conservation. Thresholds or tipping points are commonly used to indicate when populations become vulnerable to extinction and to trigger changes in conservation actions. However, quantitative methods to determine such thresholds have not been well explored. The Louisiana black bear (Ursus americanus luteolus) was removed from the list of threatened and endangered species under the U.S. Endangered Species Act in 2016 and our objectives were to determine the most appropriate parameters and thresholds for monitoring and management action. Capture mark recapture (CMR) data from 2006 to 2012 were used to estimate population parameters and variances. We used stochastic population simulations and conditional classification trees to identify demographic rates for monitoring that would be most indicative of heighted extinction risk. We then identified thresholds that would be reliable predictors of population viability. Conditional classification trees indicated that annual apparent survival rates for adult females averaged over 5 years () was the best predictor of population persistence. Specifically, population persistence was estimated to be ≥95% over 100 years when
, suggesting that this statistic can be used as threshold to trigger management intervention. Our evaluation produced monitoring protocols that reliably predicted population persistence and was cost-effective. We conclude that population projections and conditional classification trees can be valuable tools for identifying extinction thresholds used in monitoring programs.
The Gas Hydrates Project at the U.S. Geological Survey (USGS) focuses on the study of methane hydrates in natural environments. The project is a collaboration between the USGS Energy Resources and the USGS Coastal and Marine Geology Programs and works closely with other U.S. Federal agencies, some State governments, outside research organizations, and international partners. The USGS studies the formation and distribution of gas hydrates in nature, the potential of hydrates as an energy resource, and the interaction between methane hydrates and the environment. The USGS Gas Hydrates Project carries out field programs and participates in drilling expeditions to study marine and terrestrial gas hydrates. USGS scientists also acquire new geophysical data and sample sediments, the water column, and the atmosphere in areas where gas hydrates occur. In addition, project personnel analyze datasets provided by partners and manage unique laboratories that supply state-of-the-art analytical capabilities to advance national and international priorities related to gas hydrates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173079","usgsCitation":"Ruppel, C.D., 2018, The U.S. Geological Survey’s Gas Hydrates Project: U.S. Geological Survey Fact Sheet 2017–3079, 4 p., https://doi.org/10.3133/fs20173079.","productDescription":"4 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-081104","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":350440,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/fs20173080","text":"Fact Sheet 2017–3080","linkHelpText":"- Gas Hydrate in Nature"},{"id":350438,"rank":3,"type":{"id":18,"text":"Project Site"},"url":"https://woodshole.er.usgs.gov/project-pages/hydrates/index.html","text":"Overview of the U.S. Geological Survey’s Gas Hydrates Project: "},{"id":350439,"rank":4,"type":{"id":18,"text":"Project Site"},"url":"https://energy.usgs.gov/OilGas/UnconventionalOilGas/GasHydrates.aspx","text":"U.S. Geological Survey’s Energy Resources Program gas hydrates site:"},{"id":350437,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3079/fs20173079.pdf","text":"Report","size":"547 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3079"},{"id":350436,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3079/coverthb.jpg"}],"contact":"Coastal and Marine Geology Program Coordinator
Energy Resources Program Coordinator
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Armenia is a landlocked country located in the mountainous Caucasus region between Asia and Europe. It shares borders with the countries of Georgia on the north, Azerbaijan on the east, Iran on the south, and Turkey and Azerbaijan on the west. The Ararat Basin is a transboundary basin in Armenia and Turkey. The Ararat Basin (or Ararat Valley) is an intermountain depression that contains the Aras River and its tributaries, which also form the border between Armenia and Turkey and divide the basin into northern and southern regions. The Ararat Basin also contains Armenia’s largest agricultural and fish farming zone that is supplied by high-quality water from wells completed in the artesian aquifers that underlie the basin. Groundwater constitutes about 40 percent of all water use, and groundwater provides 96 percent of the water used for drinking purposes in Armenia. Since 2000, groundwater withdrawals and consumption in the Ararat Basin of Armenia have increased because of the growth of aquaculture and other uses. Increased groundwater withdrawals caused decreased springflow, reduced well discharges, falling water levels, and a reduction of the number of flowing artesian wells in the southern part of Ararat Basin in Armenia.
In 2016, the U.S. Geological Survey and the U.S. Agency for International Development (USAID) began a cooperative study in Armenia to share science and field techniques to increase the country’s capabilities for groundwater study and modeling. The purpose of this report is to describe the hydrogeologic framework and groundwater conditions of the Ararat Basin in Armenia based on data collected in 2016 and previous hydrogeologic studies. The study area includes the Ararat Basin in Armenia. This report was completed through a partnership with USAID/Armenia in the implementation of its Science, Technology, Innovation, and Partnerships effort through the Advanced Science and Partnerships for Integrated Resource Development program and associated partners, including the Government of Armenia, Armenia’s Hydrogeological Monitoring Center, and the USAID Global Development Lab and its GeoCenter.
The hydrogeologic framework of the Ararat Basin includes several basin-fill stratigraphic units consisting of interbedded dense clays, gravels, sands, volcanic basalts, and andesite deposits. Previously published cross sections and well lithologic logs were used to map nine general hydrogeologic units. Hydrogeologic units were mapped based on lithology and water-bearing potential. Water-level data measured in the water-bearing hydrogeologic units 2, 4, 6, and 8 in 2016 were used to create potentiometric surface maps. In hydrogeologic unit 2, the estimated direction of groundwater flow is from the west to north in the western part of the basin (away from the Aras River) and from north to south (toward the Aras River) in the eastern part of the basin. In hydrogeologic unit 4, the direction of groundwater flow is generally from west to east and north to south (toward the Aras River) except in the western part of the basin where groundwater flow is toward the north or northwest. Hydrogeologic unit 6 has the same general pattern of groundwater flow as unit 4. Hydrogeologic unit 8 is the deepest of the water-bearing units and is confined in the basin. Groundwater flow generally is from the south to north (away from the Aras River) in the western part of the basin and from west to east and north to south (toward the Aras River) elsewhere in the basin.
In addition to water levels, personnel from Armenia’s Hydrogeological Monitoring Center also measured specific conductance at 540 wells and temperature at 2,470 wells in the Ararat Basin using U.S. Geological Survey protocols in 2016. The minimum specific conductance was 377 microsiemens per centimeter (μS/cm), the maximum value was 4,000 μS/cm, and the mean was 998 μS/cm. The maximum water temperature was 24.2 degrees Celsius. An analysis between water temperature and well depth indicated no relation; however, spatially, most wells with cooler water temperatures were within the 2016 pressure boundary or in the western part of the basin. Wells with generally warmer water temperatures were in the eastern part of the basin.
Samples were collected from four groundwater sites and one surface-water site by the U.S. Geological Survey in 2016. The stable-isotope values were similar for all five sites, indicating similar recharge sources for the sampled wells. The Hrazdan River sample was consistent with the groundwater samples, indicating the river could serve as a source of recharge to the Ararat artesian aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175163","usgsCitation":"Valder, J.F., Carter, J.M., Medler, C.J., Thompson, R.F., and Anderson, M.T., 2018, Hydrogeologic framework and groundwater conditions of the Ararat Basin in Armenia: U.S. Geological Survey Scientific Investigations Report 2017–5163, 40 p., https://doi.org/10.3133/sir20175163.","productDescription":"Report: viii, 40 p.; Tables","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-088554","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":350454,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5163/sir20175163_table6.xls","text":"Table 6. Historical water-level and well yield data from various dates ranging from 1981 to 2013 in the Ararat Basin, Armenia","size":"96 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5163 Table 6"},{"id":350430,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5163/coverthb.jpg"},{"id":350452,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5163/sir20175163_table5.xlsx","text":"Table 5. Historical water-level data from 2007 in the Ararat Basin, Armenia, provided to the U.S. Geological Survey by Armenian partners","size":"200 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5163 Table 5"},{"id":350451,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5163/sir20175163_table4.xls","text":"Table 4. Hydrologic data provided to the U.S. Geological Survey from the 2016 well inventory conducted in the Ararat Basin, Armenia, by Armenian partners","size":"808 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5163 Table 4"},{"id":350434,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5163/sir20175163_table1.xlsx","text":"Table 1 Lithologic descriptions, land-surface elevations, geologic layer thicknesses, and hydrogeologic units of the Ararat Basin, Armenia","size":"792 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5163 Table 1"},{"id":350432,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5163/sir20175163.pdf","text":"Report","size":"12.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5163"}],"country":"Armenia","otherGeospatial":"Ararat Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 43.75,\n 39.75\n ],\n [\n 44.8,\n 39.75\n ],\n [\n 44.8,\n 40.25\n ],\n [\n 43.75,\n 40.25\n ],\n [\n 43.75,\n 39.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center, South Dakota Office
U.S. Geological Survey
1608 Mountain View Road
Rapid City, SD 57702
We apply linear deconvolution methods to derive mineral and glass proportions for eight field sample training sites at seven dune fields: (1) Algodones, California; (2) Big Dune, Nevada; (3) Bruneau, Idaho; (4) Great Kobuk Sand Dunes, Alaska; (5) Great Sand Dunes National Park and Preserve, Colorado; (6) Sunset Crater, Arizona; and (7) White Sands National Monument, New Mexico. These dune fields were chosen because they represent a wide range of mineral grain mixtures and allow us to gauge a better understanding of both compositional and sorting effects within terrestrial and extraterrestrial dune systems. We also use actual ASTER TIR emissivity imagery to map the spatial distribution of these minerals throughout the seven dune fields and evaluate the effects of degraded spectral resolution on the accuracy of mineral abundances retrieved. Our results show that hyperspectral data convolutions of our laboratory emissivity spectra outperformed multispectral data convolutions of the same data with respect to the mineral, glass and lithic abundances derived. Both the number and wavelength position of spectral bands greatly impacts the accuracy of linear deconvolution retrieval of feldspar proportions (e.g. K-feldspar vs. plagioclase) especially, as well as the detection of certain mafic and carbonate minerals. In particular, ASTER mapping results show that several of the dune sites display patterns such that less dense minerals typically have higher abundances near the center of the active and most evolved dunes in the field, while more dense minerals and glasses appear to be more abundant along the margins of the active dune fields.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.aeolia.2017.12.001","usgsCitation":"Hubbard, B.E., Hooper, D.M., Solano, F., and Mars, J., 2018, Determining mineralogical variations of aeolian deposits using thermal infrared emissivity and linear deconvolution methods: Aeolian Research, v. 30, p. 54-96, https://doi.org/10.1016/j.aeolia.2017.12.001.","productDescription":"43 p.","startPage":"54","endPage":"96","ipdsId":"IP-080975","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":461075,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.aeolia.2017.12.001","text":"Publisher Index Page"},{"id":438055,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7MS3QWM","text":"USGS data release","linkHelpText":"Visible, Near Infrared, Shortwave Infrared and Thermal Infrared Laboratory Spectra of Samples of Compositionally Variable Dune Fields in the Western United States and Alaska"},{"id":438054,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7CC0XTR","text":"USGS data release","linkHelpText":"Linear Deconvolution Mineral Maps of Compositionally Variable Dune Fields in the Western United States and Alaska"},{"id":350459,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, Arizona, California, Colorado, Idaho, Nevada, New Mexico","otherGeospatial":"Algodones, Big Dune, Bruneau, Great Kobuk Sand Dunes, Great Sand Dunes National Park and Preserve, Sunset Crater, White Sands National Monument","volume":"30","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60e451e4b06e28e9c14067","contributors":{"authors":[{"text":"Hubbard, Bernard E. 0000-0002-9315-2032 bhubbard@usgs.gov","orcid":"https://orcid.org/0000-0002-9315-2032","contributorId":2342,"corporation":false,"usgs":true,"family":"Hubbard","given":"Bernard","email":"bhubbard@usgs.gov","middleInitial":"E.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":725517,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooper, Donald M.","contributorId":197205,"corporation":false,"usgs":false,"family":"Hooper","given":"Donald","email":"","middleInitial":"M.","affiliations":[{"id":35998,"text":"WEX Foundation","active":true,"usgs":false},{"id":35997,"text":"Southwest Research Institute, San Antonio, TX","active":true,"usgs":false}],"preferred":false,"id":725518,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Solano, Federico 0000-0002-0308-5850 fsolanoc@usgs.gov","orcid":"https://orcid.org/0000-0002-0308-5850","contributorId":4302,"corporation":false,"usgs":true,"family":"Solano","given":"Federico","email":"fsolanoc@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":725519,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mars, John C. jmars@usgs.gov","contributorId":127493,"corporation":false,"usgs":true,"family":"Mars","given":"John C.","email":"jmars@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":725520,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70194509,"text":"sir20175140 - 2018 - Development of a hydraulic model and flood-inundation maps for the Wabash River near the Interstate 64 Bridge near Grayville, Illinois","interactions":[],"lastModifiedDate":"2018-07-25T10:40:07","indexId":"sir20175140","displayToPublicDate":"2018-01-16T11:30:00","publicationYear":"2018","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":"2017-5140","title":"Development of a hydraulic model and flood-inundation maps for the Wabash River near the Interstate 64 Bridge near Grayville, Illinois","docAbstract":"A two-dimensional hydraulic model and digital flood‑inundation maps were developed for a 30-mile reach of the Wabash River near the Interstate 64 Bridge near Grayville, Illinois. The flood-inundation maps, which can be accessed through the U.S. Geological Survey (USGS) Flood Inundation Mapping Science web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the Wabash River at Mount Carmel, Ill (USGS station number 03377500). Near-real-time stages at this streamgage may be obtained on the internet from the USGS National Water Information System at http://waterdata.usgs.gov/ or the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) at http://water.weather.gov/ahps/, which also forecasts flood hydrographs at this site (NWS AHPS site MCRI2). The NWS AHPS forecasts peak stage information that may be used with the maps developed in this study to show predicted areas of flood inundation.
Flood elevations were computed for the Wabash River reach by means of a two-dimensional, finite-volume numerical modeling application for river hydraulics. The hydraulic model was calibrated by using global positioning system measurements of water-surface elevation and the current stage-discharge relation at both USGS streamgage 03377500, Wabash River at Mount Carmel, Ill., and USGS streamgage 03378500, Wabash River at New Harmony, Indiana. The calibrated hydraulic model was then used to compute 27 water-surface elevations for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from less than the action stage (9 ft) to the highest stage (35 ft) of the current stage-discharge rating curve. The simulated water‑surface elevations were then combined with a geographic information system digital elevation model, derived from light detection and ranging data, to delineate the area flooded at each water level.
The availability of these maps, along with information on the internet regarding current stage from the USGS streamgage at Mount Carmel, Ill., and forecasted stream stages from the NWS AHPS, provides emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175140","collaboration":"Prepared in cooperation with the Indiana Department of Transportation; Illinois Department of Transportation","usgsCitation":"Boldt, J.A., 2018, Development of a hydraulic model and flood-inundation maps for the Wabash River near the Interstate 64 Bridge near Grayville, Illinois: U.S. Geological Survey Scientific Investigations Report 2017–5140, 13 p., https://doi.org/10.3133/sir20175140.","productDescription":"vi, 13 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087699","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":355963,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78P5ZCD","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial datasets and model for the flood-inundation study of the Wabash River near the Interstate 64 Bridge near Grayville, Illinois"},{"id":350289,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/sir20175117","text":"Scientific Investigations Report 2017–5117","linkHelpText":"- River Meander Modeling of the Wabash River near the Interstate 64 Bridge near Grayville, Illinois"},{"id":350288,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5140/sir20175140.pdf","text":"Report","size":"3.06 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5140"},{"id":350287,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5140/coverthb.jpg"}],"country":"United States","state":"Illinois","city":"Grayville","otherGeospatial":"Wabash River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.01456451416016,\n 38.153727245014004\n ],\n [\n -87.77870178222656,\n 38.153727245014004\n ],\n [\n -87.77870178222656,\n 38.338694087313534\n ],\n [\n -88.01456451416016,\n 38.338694087313534\n ],\n [\n -88.01456451416016,\n 38.153727245014004\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville, KY 40299