The Arctic is among the fastest warming regions on Earth, but it is also one with limited spatial coverage of multidecadal instrumental surface air temperature measurements. Consequently, atmospheric reanalyses are relatively unconstrained in this region, resulting in a large spread of estimated 30 year recent warming trends, which limits their use to investigate the mechanisms responsible for this trend. Here we present a surface temperature reconstruction over 1982–2011 at NEEM (North Greenland Eemian Ice Drilling Project, 51°W, 77°N), in North Greenland, based on the inversion of borehole temperature and inert gas isotope data. We find that NEEM has warmed by 2.7 ± 0.33°C over the past 30 years, from the long-term 1900–1970 average of −28.55 ± 0.29°C. The warming trend is principally caused by an increase in downward longwave heat flux. Atmospheric reanalyses underestimate this trend by 17%, underlining the need for more in situ observations to validate reanalyses.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016GL072212","usgsCitation":"Orsi, A.J., Kawamura, K., Masson-Delmotte, V., Fettweis, X., Box, J.E., Dahl-Jensen, D., Clow, G.D., Landais, A., and Severinghaus, J.P., 2017, The recent warming trend in North Greenland: Geophysical Research Letters, v. 44, no. 12, p. 6235-6243, https://doi.org/10.1002/2016GL072212.","productDescription":"9 p.","startPage":"6235","endPage":"6243","ipdsId":"IP-065150","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":469784,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016gl072212","text":"Publisher Index Page"},{"id":349054,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"12","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-28","publicationStatus":"PW","scienceBaseUri":"5a60fbbde4b06e28e9c23538","contributors":{"authors":[{"text":"Orsi, Anais J.","contributorId":140705,"corporation":false,"usgs":false,"family":"Orsi","given":"Anais","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":717177,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kawamura, Kenji","contributorId":195041,"corporation":false,"usgs":false,"family":"Kawamura","given":"Kenji","email":"","affiliations":[],"preferred":false,"id":717178,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Masson-Delmotte, Valerie","contributorId":198808,"corporation":false,"usgs":false,"family":"Masson-Delmotte","given":"Valerie","email":"","affiliations":[],"preferred":false,"id":717179,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fettweis, Xavier","contributorId":198810,"corporation":false,"usgs":false,"family":"Fettweis","given":"Xavier","email":"","affiliations":[],"preferred":false,"id":717181,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Box, Jason E.","contributorId":198809,"corporation":false,"usgs":false,"family":"Box","given":"Jason","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":717180,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dahl-Jensen, Dorthe","contributorId":198811,"corporation":false,"usgs":false,"family":"Dahl-Jensen","given":"Dorthe","email":"","affiliations":[],"preferred":false,"id":717182,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Clow, Gary D. 0000-0002-2262-3853 clow@usgs.gov","orcid":"https://orcid.org/0000-0002-2262-3853","contributorId":2066,"corporation":false,"usgs":true,"family":"Clow","given":"Gary","email":"clow@usgs.gov","middleInitial":"D.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":717176,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Landais, Amaelle","contributorId":198812,"corporation":false,"usgs":false,"family":"Landais","given":"Amaelle","email":"","affiliations":[],"preferred":false,"id":717183,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Severinghaus, Jeffrey P.","contributorId":140715,"corporation":false,"usgs":false,"family":"Severinghaus","given":"Jeffrey","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":717184,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70190739,"text":"70190739 - 2017 - Erosion characteristics and horizontal variability for small erosion depths in the Sacramento-San Joaquin River Delta, California, USA","interactions":[],"lastModifiedDate":"2017-09-13T15:42:25","indexId":"70190739","displayToPublicDate":"2017-06-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2923,"text":"Ocean Dynamics","active":true,"publicationSubtype":{"id":10}},"title":"Erosion characteristics and horizontal variability for small erosion depths in the Sacramento-San Joaquin River Delta, California, USA","docAbstract":"Erodibility of cohesive sediment in the Sacramento-San Joaquin River Delta (Delta) was investigated with an erosion microcosm. Erosion depths in the Delta and in the microcosm were estimated to be about one floc diameter over a range of shear stresses and times comparable to half of a typical tidal cycle. Using the conventional assumption of horizontally homogeneous bed sediment, data from 27 of 34 microcosm experiments indicate that the erosion rate coefficient increased as eroded mass increased, contrary to theory. We believe that small erosion depths, erosion rate coefficient deviation from theory, and visual observation of horizontally varying biota and texture at the sediment surface indicate that erosion cannot solely be a function of depth but must also vary horizontally. We test this hypothesis by developing a simple numerical model that includes horizontal heterogeneity, use it to develop an artificial time series of suspended-sediment concentration (SSC) in an erosion microcosm, then analyze that time series assuming horizontal homogeneity. A shear vane was used to estimate that the horizontal standard deviation of critical shear stress was about 30% of the mean value at a site in the Delta. The numerical model of the erosion microcosm included a normal distribution of initial critical shear stress, a linear increase in critical shear stress with eroded mass, an exponential decrease of erosion rate coefficient with eroded mass, and a stepped increase in applied shear stress. The maximum SSC for each step increased gradually, thus confounding identification of a single well-defined critical shear stress as encountered with the empirical data. Analysis of the artificial SSC time series with the assumption of a homogeneous bed reproduced the original profile of critical shear stress, but the erosion rate coefficient increased with eroded mass, similar to the empirical data. Thus, the numerical experiment confirms the small-depth erosion hypothesis. A linear model of critical shear stress and eroded mass is proposed to simulate small-depth erosion, assuming that the applied and critical shear stresses quickly reach equilibrium.
","language":"English","publisher":"Springer","doi":"10.1007/s10236-017-1047-2","usgsCitation":"Schoellhamer, D., Manning, A.J., and Work, P.A., 2017, Erosion characteristics and horizontal variability for small erosion depths in the Sacramento-San Joaquin River Delta, California, USA: Ocean Dynamics, v. 67, no. 6, p. 799-811, https://doi.org/10.1007/s10236-017-1047-2.","productDescription":"13 p.","startPage":"799","endPage":"811","ipdsId":"IP-053622","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":461533,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10236-017-1047-2","text":"Publisher Index Page"},{"id":345708,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.08007812499999,\n 37.79676317682161\n ],\n [\n -121.27670288085938,\n 37.79676317682161\n ],\n [\n -121.27670288085938,\n 38.36211833953394\n ],\n [\n -122.08007812499999,\n 38.36211833953394\n ],\n [\n -122.08007812499999,\n 37.79676317682161\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"67","issue":"6","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-24","publicationStatus":"PW","scienceBaseUri":"59ba43b9e4b091459a5629ba","contributors":{"authors":[{"text":"Schoellhamer, David H. 0000-0001-9488-7340 dschoell@usgs.gov","orcid":"https://orcid.org/0000-0001-9488-7340","contributorId":631,"corporation":false,"usgs":true,"family":"Schoellhamer","given":"David H.","email":"dschoell@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":710289,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Manning, Andrew J.","contributorId":175079,"corporation":false,"usgs":false,"family":"Manning","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":710290,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Work, Paul A. 0000-0002-2815-8040 pwork@usgs.gov","orcid":"https://orcid.org/0000-0002-2815-8040","contributorId":168561,"corporation":false,"usgs":true,"family":"Work","given":"Paul","email":"pwork@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":710291,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70189872,"text":"70189872 - 2017 - Aerodynamic roughness length estimation with lidar and imaging spectroscopy in a shrub-dominated dryland","interactions":[],"lastModifiedDate":"2017-11-22T16:53:38","indexId":"70189872","displayToPublicDate":"2017-06-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3052,"text":"Photogrammetric Engineering and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Aerodynamic roughness length estimation with lidar and imaging spectroscopy in a shrub-dominated dryland","docAbstract":"The aerodynamic roughness length (Z0 m) serves an important role in the flux exchange between the land surface and atmosphere. In this study, airborne lidar (ALS), terrestrial lidar (TLS), and imaging spectroscopy data were integrated to develop and test two approaches to estimate Z0 m over a shrub dominated dryland study area in south-central Idaho, USA. Sensitivity of the two parameterization methods to estimate Z0 m was analyzed. The comparison of eddy covariance-derived Z0 m and remote sensing-derived Z0 m showed that the accuracy of the estimated Z0 m heavily depends on the estimation model and the representation of shrub (e.g., Artemisia tridentata subsp. wyomingensis) height in the models. The geometrical method (RA1994) led to 9 percent (~0.5 cm) and 25% (~1.1 cm) errors at site 1 and site 2, respectively, which performed better than the height variability-based method (MR1994) with bias error of 20 percent and 48 percent at site 1 and site 2, respectively. The RA1994 model resulted in a larger range of Z0 m than the MR1994 method. We also found that the mean, median and 75th percentiles of heights (H75) from ALS provides the best Z0 m estimates in the MR1994 model, while the mean, median, and MLD (Median Absolute Deviation from Median Height), as well as AAD (Mean Absolute Deviation from Mean Height) heights from ALS provides the best Z0 m estimates in the RA1994 model. In addition, the fractional cover of shrub and grass, distinguished with ALS and imaging spectroscopy data, provided the opportunity to estimate the frontal area index at the pixel-level to assess the influence of grass and shrub on Z0m estimates in the RA1994 method. Results indicate that grass had little effect on Z0 m in the RA1994 method. The Z0 m estimations were tightly coupled with vegetation height and its local variance for the shrubs. Overall, the results demonstrate that the use of height and fractional cover from remote sensing data are promising for estimating Z0 m, and thus refining land surface models at regional scales in semiarid shrublands.
","language":"English","publisher":"American Society for Photogrammetry and Remote Sensing","doi":"10.14358/PERS.83.6.415","usgsCitation":"Li, A., Zhao, W., Mitchell, J., Glenn, N.F., Germino, M., Sankey, J.B., and Allen, R.M., 2017, Aerodynamic roughness length estimation with lidar and imaging spectroscopy in a shrub-dominated dryland: Photogrammetric Engineering and Remote Sensing, v. 83, no. 6, p. 415-427, https://doi.org/10.14358/PERS.83.6.415.","productDescription":"13 p.","startPage":"415","endPage":"427","ipdsId":"IP-080636","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":488694,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14358/pers.83.6.415","text":"Publisher Index Page"},{"id":344452,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.90576171874999,\n 42.09822241118974\n ],\n [\n -112.1044921875,\n 42.09822241118974\n ],\n [\n -112.1044921875,\n 44.315987905196906\n ],\n [\n -115.90576171874999,\n 44.315987905196906\n ],\n [\n -115.90576171874999,\n 42.09822241118974\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"83","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59804199e4b0a38ca2789336","contributors":{"authors":[{"text":"Li, Aihua","contributorId":169445,"corporation":false,"usgs":false,"family":"Li","given":"Aihua","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":706603,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhao, Wenguang","contributorId":195243,"corporation":false,"usgs":false,"family":"Zhao","given":"Wenguang","email":"","affiliations":[],"preferred":false,"id":706607,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mitchell, Jessica J","contributorId":195242,"corporation":false,"usgs":false,"family":"Mitchell","given":"Jessica J","affiliations":[],"preferred":false,"id":706605,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Glenn, Nancy F.","contributorId":195241,"corporation":false,"usgs":false,"family":"Glenn","given":"Nancy","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":706604,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Germino, Matthew J. 0000-0001-6326-7579 mgermino@usgs.gov","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":152582,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","email":"mgermino@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":706602,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sankey, Joel B. 0000-0003-3150-4992 jsankey@usgs.gov","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":3935,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel","email":"jsankey@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":706606,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Allen, Richard M.","contributorId":195244,"corporation":false,"usgs":false,"family":"Allen","given":"Richard","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":706608,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70192641,"text":"70192641 - 2017 - Alternative foraging strategies enable a mountain ungulate to persist after migration loss","interactions":[],"lastModifiedDate":"2017-11-07T11:19:46","indexId":"70192641","displayToPublicDate":"2017-06-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Alternative foraging strategies enable a mountain ungulate to persist after migration loss","docAbstract":"The persistence of many migratory ungulate populations worldwide is threatened due to anthropogenic impacts to seasonal ranges and migration routes. While many studies have linked migratory ungulate declines to migration disruption or loss, very few have explored the underlying factors that determine whether a population perishes or persists. In some cases, populations undergo severe declines and extirpation after migration loss; however, others appear able to persist as residents. We predict that to persist, populations must replace the traditional benefits of migration by altering the foraging strategies they employ as residents within one seasonal range. We propose the alternative foraging strategies (AFS) hypothesis as a framework for identifying various behavioral strategies that populations may use to cope with migration loss. We tested the hypothesis using the formerly migratory Teton bighorn sheep population in northwest Wyoming, which ceased migrating over 60 yr ago, but has persisted as a resident population. We used global positioning system data to evaluate winter and summer habitat selection and seasonal elevational movements for 28 adult female bighorn sheep (Ovis canadensis) from 2008 to 2010. Resource selection functions revealed that bighorn sheep employ winter foraging strategies to survive as residents by seeking out rugged, high-elevation, windswept ridgelines. Seasonal movement analyses indicated that bighorn sheep undergo a newly documented “abbreviated migration” strategy that is closely synchronized with vegetation green-up patterns within their one range. Bighorn sheep descend 500 m in elevation and travel up to 10 km in spring, gaining access to newly emergent forage approximately 30 d before it appears on their high-elevation winter and summer ranges. Our findings indicate that the Teton bighorn sheep population has persisted due to its habitat selection, AFS, and unique movement patterns, which allow migration loss to be mediated to some extent. The identification of AFS and the habitats that support them can help reveal the underlying benefits of migration and conserve populations in the face of future migration loss.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1855","usgsCitation":"Courtemanch, A.B., Kauffman, M., Kilpatrick, S., and Dewey, S., 2017, Alternative foraging strategies enable a mountain ungulate to persist after migration loss: Ecosphere, v. 8, no. 6, p. 1-16, https://doi.org/10.1002/ecs2.1855.","productDescription":"Article e01855; 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-084521","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":469808,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1855","text":"Publisher Index Page"},{"id":348356,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Teton Mountain Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.03744506835938,\n 43.43397432280115\n ],\n [\n -110.7147216796875,\n 43.43397432280115\n ],\n [\n -110.7147216796875,\n 43.866218006556394\n ],\n [\n -111.03744506835938,\n 43.866218006556394\n ],\n [\n -111.03744506835938,\n 43.43397432280115\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-19","publicationStatus":"PW","scienceBaseUri":"5a07e8dee4b09af898c8cbcb","contributors":{"authors":[{"text":"Courtemanch, Alyson B.","contributorId":198651,"corporation":false,"usgs":false,"family":"Courtemanch","given":"Alyson","email":"","middleInitial":"B.","affiliations":[{"id":35682,"text":"Wyoming Game and Fish Department, Jackson, WY","active":true,"usgs":false}],"preferred":false,"id":716631,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900 mkauffman@usgs.gov","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":189179,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew J.","email":"mkauffman@usgs.gov","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":716630,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kilpatrick, Steve","contributorId":198652,"corporation":false,"usgs":false,"family":"Kilpatrick","given":"Steve","email":"","affiliations":[],"preferred":false,"id":716632,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dewey, Sarah","contributorId":145757,"corporation":false,"usgs":false,"family":"Dewey","given":"Sarah","affiliations":[{"id":16229,"text":"National Park Service, Grand Teton National Park, PO Drawer 170, Moose, WY 83012 USA","active":true,"usgs":false}],"preferred":false,"id":716633,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188116,"text":"70188116 - 2017 - Scenario Evaluator for Electrical Resistivity survey pre-modeling tool","interactions":[],"lastModifiedDate":"2017-11-29T16:39:40","indexId":"70188116","displayToPublicDate":"2017-05-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Scenario Evaluator for Electrical Resistivity survey pre-modeling tool","docAbstract":"Geophysical tools have much to offer users in environmental, water resource, and geotechnical fields; however, techniques such as electrical resistivity imaging (ERI) are often oversold and/or overinterpreted due to a lack of understanding of the limitations of the techniques, such as the appropriate depth intervals or resolution of the methods. The relationship between ERI data and resistivity is nonlinear; therefore, these limitations depend on site conditions and survey design and are best assessed through forward and inverse modeling exercises prior to field investigations. In this approach, proposed field surveys are first numerically simulated given the expected electrical properties of the site, and the resulting hypothetical data are then analyzed using inverse models. Performing ERI forward/inverse modeling, however, requires substantial expertise and can take many hours to implement. We present a new spreadsheet-based tool, the Scenario Evaluator for Electrical Resistivity (SEER), which features a graphical user interface that allows users to manipulate a resistivity model and instantly view how that model would likely be interpreted by an ERI survey. The SEER tool is intended for use by those who wish to determine the value of including ERI to achieve project goals, and is designed to have broad utility in industry, teaching, and research.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12522","usgsCitation":"Terry, N., Day-Lewis, F.D., Robinson, J.L., Slater, L., Halford, K.J., Binley, A., Lane, J.W., and Werkema, D.D., 2017, Scenario Evaluator for Electrical Resistivity survey pre-modeling tool: Groundwater, v. 55, no. 6, p. 885-890, https://doi.org/10.1111/gwat.12522.","productDescription":"6 p.","startPage":"885","endPage":"890","ipdsId":"IP-085916","costCenters":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"links":[{"id":469814,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6145077","text":"External Repository"},{"id":438325,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7028PQ1","text":"USGS data release","linkHelpText":"Scenario Evaluator for Electrical Resistivity (SEER) Survey Design Tool"},{"id":341955,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-23","publicationStatus":"PW","scienceBaseUri":"592fd631e4b0e9bd0ea89692","contributors":{"authors":[{"text":"Terry, Neil 0000-0002-3965-340X nterry@usgs.gov","orcid":"https://orcid.org/0000-0002-3965-340X","contributorId":192554,"corporation":false,"usgs":true,"family":"Terry","given":"Neil","email":"nterry@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":696814,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day-Lewis, Frederick D. 0000-0003-3526-886X daylewis@usgs.gov","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":1672,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","email":"daylewis@usgs.gov","middleInitial":"D.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":696815,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Robinson, Judith L.","contributorId":152119,"corporation":false,"usgs":false,"family":"Robinson","given":"Judith","email":"","middleInitial":"L.","affiliations":[{"id":18871,"text":"Rutgers University-Newark, Dept. of Earth & Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":696816,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Slater, Lee D. 0000-0003-0292-746X","orcid":"https://orcid.org/0000-0003-0292-746X","contributorId":192555,"corporation":false,"usgs":false,"family":"Slater","given":"Lee D.","affiliations":[],"preferred":false,"id":696817,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Halford, Keith J. 0000-0002-7322-1846 khalford@usgs.gov","orcid":"https://orcid.org/0000-0002-7322-1846","contributorId":1374,"corporation":false,"usgs":true,"family":"Halford","given":"Keith","email":"khalford@usgs.gov","middleInitial":"J.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":696818,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Binley, Andrew 0000-0002-0938-9070","orcid":"https://orcid.org/0000-0002-0938-9070","contributorId":192556,"corporation":false,"usgs":false,"family":"Binley","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":696819,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lane, John W. Jr. 0000-0002-3558-243X jwlane@usgs.gov","orcid":"https://orcid.org/0000-0002-3558-243X","contributorId":189168,"corporation":false,"usgs":true,"family":"Lane","given":"John","suffix":"Jr.","email":"jwlane@usgs.gov","middleInitial":"W.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":false,"id":696820,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Werkema, Dale D.","contributorId":40488,"corporation":false,"usgs":false,"family":"Werkema","given":"Dale","email":"","middleInitial":"D.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":696821,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70188108,"text":"70188108 - 2017 - Seasonal and diel environmental conditions predict western pond turtle (Emys marmorata) behavior at a perennial and an ephemeral stream in Sequoia National Park, California","interactions":[],"lastModifiedDate":"2017-06-14T11:58:12","indexId":"70188108","displayToPublicDate":"2017-05-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1210,"text":"Chelonian Conservation and Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Seasonal and diel environmental conditions predict western pond turtle (Emys marmorata) behavior at a perennial and an ephemeral stream in Sequoia National Park, California","title":"Seasonal and diel environmental conditions predict western pond turtle (Emys marmorata) behavior at a perennial and an ephemeral stream in Sequoia National Park, California","docAbstract":"Managers making decisions may benefit from a well-informed understanding of a species' population size and trends. Given the cryptic nature and habitat characteristics of the western pond turtle (Emys marmorata), however, imperfect detection may be high and population estimates are frequently varied and unreliable. As a case study to investigate this issue, we used temperature dataloggers to examine turtle behavior at 2 long-term monitoring sites with different hydrological characteristics in Sequoia National Park, California, to determine if common stream-survey techniques are consistent with site-specific turtle behavior. Sycamore Creek is an intermittent stream that dries up every summer while the North Fork Kaweah River flows year-round. We found that while turtles spent most of the recorded time in the water (55% in Sycamore Creek and 82% in the North Fork Kaweah River), the timing of traditional surveys only coincided with the turtles' aquatic activity in the North Fork Kaweah River. At Sycamore Creek, turtles were most likely to be in the water at night. In contrast, failure to detect turtles in North Fork Kaweah River is likely owing to the larger size and complexity of the underwater habitat. In both streams, turtles were also more likely to be in the water in the weeks leading up to important changes in hydroperiods. Our findings illustrate the effects that differences in water permanence can have on turtle behavior within the same watershed and how phenotypic plasticity may then affect detection during surveys. Our study highlights the importance of tailoring survey practices to the site-specific behavioral traits of the target species.
","language":"English","publisher":"Chelonian Research Foundation","doi":"10.2744/CCB-1240.1","usgsCitation":"Ruso, G., Meyer, E., and Das, A., 2017, Seasonal and diel environmental conditions predict western pond turtle (Emys marmorata) behavior at a perennial and an ephemeral stream in Sequoia National Park, California: Chelonian Conservation and Biology, v. 16, no. 1, p. 20-28, https://doi.org/10.2744/CCB-1240.1.","productDescription":"9 p.","startPage":"20","endPage":"28","ipdsId":"IP-082015","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":495027,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2744/ccb-1240.1","text":"Publisher Index Page"},{"id":341947,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sequoia National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.69010925292969,\n 36.41078375301565\n ],\n [\n -118.4271240234375,\n 36.41078375301565\n ],\n [\n -118.4271240234375,\n 36.563151553545985\n ],\n [\n -118.69010925292969,\n 36.563151553545985\n ],\n [\n -118.69010925292969,\n 36.41078375301565\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"592fd633e4b0e9bd0ea896a1","contributors":{"authors":[{"text":"Ruso, Gabrielle gruso@usgs.gov","contributorId":192549,"corporation":false,"usgs":true,"family":"Ruso","given":"Gabrielle","email":"gruso@usgs.gov","affiliations":[],"preferred":true,"id":696773,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meyer, Erik","contributorId":192550,"corporation":false,"usgs":false,"family":"Meyer","given":"Erik","email":"","affiliations":[],"preferred":false,"id":696774,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Das, Adrian J. 0000-0002-3937-2616 adas@usgs.gov","orcid":"https://orcid.org/0000-0002-3937-2616","contributorId":3842,"corporation":false,"usgs":true,"family":"Das","given":"Adrian J.","email":"adas@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":696772,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188028,"text":"70188028 - 2017 - Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data","interactions":[],"lastModifiedDate":"2017-08-03T08:35:21","indexId":"70188028","displayToPublicDate":"2017-05-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2035,"text":"International Journal of Digital Earth","active":true,"publicationSubtype":{"id":10}},"title":"Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data","docAbstract":"Mapping croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the increase in global population to nearly 10 billion by the year 2050, the need for routine, rapid, and automated cropland mapping year-after-year and/or season-after-season is of great importance. The overarching goal of this study was to generate standard and routine cropland products, year-after-year, over very large areas through the use of two novel methods: (a) quantitative spectral matching techniques (QSMTs) applied at continental level and (b) rule-based Automated Cropland Classification Algorithm (ACCA) with the ability to hind-cast, now-cast, and future-cast. Australia was chosen for the study given its extensive croplands, rich history of agriculture, and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing. This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m normalized difference vegetation index 16-day composite time-series data for 16 years: 2000 through 2015. The products consisted of: (1) cropland extent/areas versus cropland fallow areas, (2) irrigated versus rainfed croplands, and (3) cropping intensities: single, double, and continuous cropping. An accurate reference cropland product (RCP) for the year 2014 (RCP2014) produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015. A comparison between the ACCA-derived cropland products (ACPs) for the year 2014 (ACP2014) versus RCP2014 provided an overall agreement of 89.4% (kappa = 0.814) with six classes: (a) producer’s accuracies varying between 72% and 90% and (b) user’s accuracies varying between 79% and 90%. ACPs for the individual years 2000–2013 and 2015 (ACP2000–ACP2013, ACP2015) showed very strong similarities with several other studies. The extent and vigor of the Australian croplands versus cropland fallows were accurately captured by the ACCA algorithm for the years 2000–2015, thus highlighting the value of the study in food security analysis. The ACCA algorithm and the cropland products are released through http://croplands.org/app/map and http://geography.wr.usgs.gov/science/croplands/algorithms/australia_250m.html
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0000-0001-5914-4269","orcid":"https://orcid.org/0000-0001-5914-4269","contributorId":192328,"corporation":false,"usgs":false,"family":"Poehnelt","given":"Justin","email":"","affiliations":[],"preferred":false,"id":696244,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yadav, Kamini","contributorId":192329,"corporation":false,"usgs":false,"family":"Yadav","given":"Kamini","affiliations":[],"preferred":false,"id":696246,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rao, Mahesh N.","contributorId":127588,"corporation":false,"usgs":false,"family":"Rao","given":"Mahesh","email":"","middleInitial":"N.","affiliations":[{"id":7067,"text":"Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":696245,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Massey, Richard 0000-0002-4831-8718 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distribution in an endangered cryptic bird","docAbstract":"Accurate estimates of the distribution and abundance of endangered species are crucial to determine their status and plan recovery options, but such estimates are often difficult to obtain for species with low detection probabilities or that occur in inaccessible habitats. The Puaiohi (Myadestes palmeri) is a cryptic species endemic to Kauaʻi, Hawai‘i, and restricted to high elevation ravines that are largely inaccessible. To improve current population estimates, we developed an approach to model distribution and abundance of Puaiohi across their range by linking occupancy surveys to habitat characteristics, territory density, and landscape attributes. Occupancy per station ranged from 0.17 to 0.82, and was best predicted by the number and vertical extent of cliffs, cliff slope, stream width, and elevation. To link occupancy estimates with abundance, we used territory mapping data to estimate the average number of territories per survey station (0.44 and 0.66 territories per station in low and high occupancy streams, respectively), and the average number of individuals per territory (1.9). We then modeled Puaiohi occupancy as a function of two remote-sensed measures of habitat (stream sinuosity and elevation) to predict occupancy across its entire range. We combined predicted occupancy with estimates of birds per station to produce a global population estimate of 494 (95% CI 414–580) individuals. Our approach is a model for using multiple independent sources of information to accurately track population trends, and we discuss future directions for modeling abundance of this, and other, rare species.
","language":"English","publisher":"Springer","doi":"10.1007/s10531-017-1313-0","usgsCitation":"Crampton, L.H., Brinck, K.W., Pias, K.E., Heindl, B.A., Savre, T., Diegmann, J.S., and Paxton, E., 2017, Linking occupancy surveys with habitat characteristics to estimate abundance and distribution in an endangered cryptic bird: Biodiversity and Conservation, v. 26, no. 7, p. 1525-1539, https://doi.org/10.1007/s10531-017-1313-0.","productDescription":"15 p.","startPage":"1525","endPage":"1539","ipdsId":"IP-079988","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":341967,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-17","publicationStatus":"PW","scienceBaseUri":"592fd630e4b0e9bd0ea8968a","contributors":{"authors":[{"text":"Crampton, Lisa H.","contributorId":192559,"corporation":false,"usgs":false,"family":"Crampton","given":"Lisa","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":696837,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brinck, Kevin W. 0000-0001-7581-2482 kbrinck@usgs.gov","orcid":"https://orcid.org/0000-0001-7581-2482","contributorId":150936,"corporation":false,"usgs":false,"family":"Brinck","given":"Kevin","email":"kbrinck@usgs.gov","middleInitial":"W.","affiliations":[{"id":13351,"text":"University of Hawaii Cooperative Studies Unit","active":true,"usgs":false}],"preferred":false,"id":696838,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pias, Kyle E.","contributorId":192560,"corporation":false,"usgs":false,"family":"Pias","given":"Kyle","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":696839,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heindl, Barbara A. P.","contributorId":192561,"corporation":false,"usgs":false,"family":"Heindl","given":"Barbara","email":"","middleInitial":"A. P.","affiliations":[],"preferred":false,"id":696840,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Savre, Thomas","contributorId":192562,"corporation":false,"usgs":false,"family":"Savre","given":"Thomas","email":"","affiliations":[],"preferred":false,"id":696841,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Diegmann, Julia S.","contributorId":192563,"corporation":false,"usgs":false,"family":"Diegmann","given":"Julia","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":696842,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Paxton, Eben H. 0000-0001-5578-7689 epaxton@usgs.gov","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":438,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben H.","email":"epaxton@usgs.gov","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":false,"id":696836,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70185018,"text":"sir20175020 - 2017 - Hydrogeologic framework and selected components of the groundwater budget for the upper Umatilla River Basin, Oregon","interactions":[],"lastModifiedDate":"2017-06-01T08:28:44","indexId":"sir20175020","displayToPublicDate":"2017-05-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5020","title":"Hydrogeologic framework and selected components of the groundwater budget for the upper Umatilla River Basin, Oregon","docAbstract":"This report presents a summary of the hydrogeology of the upper Umatilla River Basin, Oregon, based on characterization of the hydrogeologic framework, horizontal and vertical directions of groundwater flow, trends in groundwater levels, and components of the groundwater budget. The conceptual model of the groundwater flow system integrates available data and information on the groundwater resources of the upper Umatilla River Basin and provides insights regarding key hydrologic processes, such as the interaction between the groundwater and surface water systems and the hydrologic budget.
The conceptual groundwater model developed for the study area divides the groundwater flow system into five hydrogeologic units: a sedimentary unit, three Columbia River basalt units, and a basement rock unit. The sedimentary unit, which is not widely used as a source of groundwater in the upper basin, is present primarily in the lowlands and consists of conglomerate, loess, silt and sand deposits, and recent alluvium. The Columbia River Basalt Group is a series of Miocene flood basalts that are present throughout the study area. The basalt is uplifted in the southeastern half of the study area, and either underlies the sedimentary unit, or is exposed at the surface. The interflow zones of the flood basalts are the primary aquifers in the study area. Beneath the flood basalts are basement rocks composed of Paleogene to Pre-Tertiary sedimentary, volcanic, igneous, and metamorphic rocks that are not used as a source of groundwater in the upper Umatilla River Basin.
The major components of the groundwater budget in the upper Umatilla River Basin are (1) groundwater recharge, (2) groundwater discharge to surface water and wells, (3) subsurface flow into and out of the basin, and (4) changes in groundwater storage.
Recharge from precipitation occurs primarily in the upland areas of the Blue Mountains. Mean annual recharge from infiltration of precipitation for the upper Umatilla River Basin during 1951–2010 is about 9.6 inches per year (in/yr). Annual recharge from precipitation for water year 2010 ranged from 3 in. in the lowland area to about 30 in. in the Blue Mountains. Using Kahle and others (2011) data and methods from the Columbia Plateau regional model, average annual recharge from irrigation is estimated to be about 2.2 in/yr for the 13 square miles of irrigated land in the upper Umatilla River Basin.
Groundwater discharges to streams throughout the year and is a large component of annual streamflow in the upper Umatilla River Basin. Upward vertical hydraulic gradients near the Umatilla River indicate the potential for groundwater discharge. Groundwater discharge to the Umatilla River generally occurs in the upper part of the basin, upstream from the main stem.
Groundwater development in the upper Umatilla River Basin began sometime after 1950 (Davies-Smith and others, 1988; Gonthier and Bolke, 1991). By water year 2010, groundwater use in the upper Umatilla River Basin was approximately 11,214 acre-feet (acre-ft). Total groundwater withdrawals for the study area were estimated at 7,575 acre-ft for irrigation, 3,173 acre-ft for municipal use, and 466 acre-ft for domestic use.
Total groundwater flow into or from the study area depends locally on geology and hydraulic head distribution. Estimates of subsurface flow were calculated using the U.S. Geological Survey Columbia Plateau regional groundwater flow model. Net flux values range from 25,000 to 27,700 acre-ft per year and indicate that groundwater is moving out of the upper Umatilla River Basin into the lower Umatilla River Basin.
Water level changes depend on storage changes within an aquifer, and storage changes depend on the storage properties of the aquifer, as well as recharge to or discharge from the aquifer. Groundwater level data in the upper Umatilla River Basin are mostly available from wells in Columbia River basalt units, which indicate areas of long-term water level declines in the Grande Ronde basalt unit near Pendleton and Athena, Oregon. Groundwater levels in the Wanapum basalt unit do not show long-term declines in the upper Umatilla River Basin. Because of pumping, some areas in the upper Umatilla River Basin have shown a decrease, or reversal, in the upward vertical head gradient.
Key data needs are improvement of the spatial and temporal distribution of water-level data collection and continued monitoring of streamflow gaging sites. Additionally, refinement of recharge estimates would enhance understanding of the processes that provide the groundwater resources in the upper Umatilla River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175020","collaboration":"Prepared in cooperation with Confederated Tribes of the Umatilla Indian Reservation","usgsCitation":"Herrera, N.B., Ely, Kate, Mehta, Smita, Stonewall, A.J., Risley, J.C., Hinkle, S.R., and Conlon, T.D., 2017, Hydrogeologic framework and selected components of the groundwater budget for the upper Umatilla River Basin, Oregon: U.S. Geological Survey Scientific Investigations Report 2017–5020, 57 p., https://doi.org/10.3133/sir20175020.","productDescription":"vi, 57 p.","onlineOnly":"Y","ipdsId":"IP-049734","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":341899,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5020/coverthb.jpg"},{"id":341900,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5020/sir20175020.pdf","text":"Report","size":"16.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5020"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Umatilla River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.9,\n 45.35\n ],\n [\n -118,\n 45.35\n ],\n [\n -118,\n 45.93\n ],\n [\n -118.9,\n 45.93\n ],\n [\n -118.9,\n 45.35\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
The Bureau of Ocean Energy Management researchers often require detailed information regarding emergent marsh vegetation types (such as fresh, intermediate, brackish, and saline) for modeling habitat capacities and mitigation. In response, the U.S. Geological Survey in cooperation with the Bureau of Ocean Energy Management produced a detailed change classification of emergent marsh vegetation types in coastal Louisiana from 2007 and 2013. This study incorporates two existing vegetation surveys and independent variables such as Landsat Thematic Mapper multispectral satellite imagery, high-resolution airborne imagery from 2007 and 2013, bare-earth digital elevation models based on airborne light detection and ranging, alternative contemporary land-cover classifications, and other spatially explicit variables. An image classification based on image objects was created from 2007 and 2013 National Agriculture Imagery Program color-infrared aerial photography. The final products consisted of two 10-meter raster datasets. Each image object from the 2007 and 2013 spatial datasets was assigned a vegetation classification by using a simple majority filter. In addition to those spatial datasets, we also conducted a change analysis between the datasets to produce a 10-meter change raster product. This analysis identified how much change has taken place and where change has occurred. The spatial data products show dynamic areas where marsh loss is occurring or where marsh type is changing. This information can be used to assist and advance conservation efforts for priority natural resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175044","collaboration":"Prepared in cooperation with Bureau of Ocean Energy Management","usgsCitation":"Hartley, S.B., Couvillion, B.R., and Enwright, N.M., 2017, Delineation of marsh types and marsh-type change in coastal Louisiana for 2007 and 2013: U.S. Geological Survey Scientific Investigations Report 2017–5044, 6 p., https://doi.org/10.3133/20175044.","productDescription":"Report: vi, 6 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-084395","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":341819,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5044/coverthb.jpg"},{"id":341820,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5044/sir20175044.pdf","text":"Report","size":"499 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5044"},{"id":341821,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7474820","text":"USGS - 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U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506
The Honolulu Board of Water Supply is currently concerned with the possibility of bacteria in the pumped water of the ‘Ewa Shaft (State well 3-2202-21). Groundwater from the ‘Ewa Shaft could potentially be used to meet future potable water needs in the ‘Ewa area on the island of O‘ahu. The source of the bacteria in the pumped water is unknown, although previous studies indicate that surface water may be lost to the subsurface near the site. The ‘Ewa Shaft consists of a vertical shaft, started near the south bank of Honouliuli Stream at an altitude of about 161 feet, and two horizontal infiltration tunnels near sea level. The shaft extracts groundwater from near the top of the freshwater lens in the Waipahu-Waiawa aquifer system within the greater Pearl Harbor Aquifer Sector, a designated Water Management Area.
The surface-water losses were evaluated with continuous groundwater-level data from the ‘Ewa Shaft and a nearby monitoring well, continuous stream-discharge data from U.S. Geological Survey streamflow-gaging station 16212490 (Honouliuli Stream at H-1 Freeway near Waipahu), and seepage-run measurements in Honouliuli Stream and its tributary. During storms, discharge at the Honouliuli Stream gaging station increases and groundwater levels at ‘Ewa Shaft and a nearby monitoring well also increase. The concurrent increase in water levels at ‘Ewa Shaft and the nearby monitoring well during storms indicates that regional groundwater-level changes related to increased recharge, reduced withdrawals (due to a decrease in demand during periods of rainfall), or both may be occurring; although these data do not preclude the possibility of local recharge from Honouliuli Stream. Discharge measurements from two seepage runs indicate that surface water in the immediate area adjacent to ‘Ewa Shaft infiltrates into the streambed and may later reach the groundwater system developed by the ‘Ewa Shaft. The estimated seepage loss rates in the vicinity of ‘Ewa Shaft from the two seepage runs generally ranged from 0.27 to 1.78 million gallons per day per mile of stream reach; although higher seepage rates may occur during periods of higher discharge in Honouliuli Stream. A potential source of bacteria in ‘Ewa Shaft maybe related to seepage from Honouliuli Stream; however, other sources of bacteria were not studied and cannot be excluded.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175042","collaboration":"Prepared in cooperation with the Honolulu Board of Water Supply","usgsCitation":"Rosa, S.N., 2017, Measuring surface-water loss in Honouliuli Stream near the ‘Ewa Shaft, O‘ahu, Hawai‘i: U.S. Geological Survey Scientific Investigations Report 2017–5042, 14 p., https://doi.org/10.3133/sir20175042.","productDescription":"vi, 16 p.","onlineOnly":"Y","ipdsId":"IP-082480","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":341865,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5042/coverthb.jpg"},{"id":341867,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5042/sir20175042.pdf","text":"Report","size":"2.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5042"}],"country":"United States","state":"Hawaii","otherGeospatial":"O‘ahu, Wai‘anae Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.0667,\n 21.3625\n ],\n [\n -158.0083,\n 21.3625\n ],\n [\n -158.0083,\n 21.4833\n ],\n [\n -158.0667,\n 21.4833\n ],\n [\n -158.0667,\n 21.3625\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
To explore steady state fractionation processes in the unsaturated zone (UZ), we measured argon, krypton, and xenon isotope ratios throughout a ∼110 m deep UZ at the United States Geological Survey (USGS) Amargosa Desert Research Site (ADRS) in Nevada, USA. Prior work has suggested that gravitational settling should create a nearly linear increase in heavy-to-light isotope ratios toward the bottom of stagnant air columns in porous media. Our high-precision measurements revealed a binary mixture between (1) expected steady state isotopic compositions and (2) unfractionated atmospheric air. We hypothesize that the presence of an unsealed pipe connecting the surface to the water table allowed for direct inflow of surface air in response to extensive UZ gas sampling prior to our first (2015) measurements. Observed isotopic resettling in deep UZ samples collected a year later, after sealing the pipe, supports this interpretation. Data and modeling each suggest that the strong influence of gravitational settling and weaker influences of thermal diffusion and fluxes of CO2 and water vapor accurately describe steady state isotopic fractionation of argon, krypton, and xenon within the UZ. The data confirm that heavy noble gas isotopes are sensitive indicators of UZ depth. Based on this finding, we outline a potential inverse approach to quantify past water table depths from noble gas isotope measurements in paleogroundwater, after accounting for fractionation during dissolution of UZ air and bubbles.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016WR019655","usgsCitation":"Seltzer, A.M., Severinghaus, J.P., Andraski, B.J., and Stonestrom, D.A., 2017, Steady state fractionation of heavy noble gas isotopes in a deep unsaturated zone: Water Resources Research, v. 53, no. 4, p. 2716-2732, https://doi.org/10.1002/2016WR019655.","productDescription":"17 p.","startPage":"2716","endPage":"2732","ipdsId":"IP-078005","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":469820,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016wr019655","text":"Publisher Index Page"},{"id":341817,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","county":"Nye County","otherGeospatial":"Amargosa Desert Research Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.69602632522582,\n 36.76556695045376\n ],\n [\n -116.68630599975586,\n 36.76556695045376\n ],\n [\n -116.68630599975586,\n 36.76969233214548\n ],\n [\n -116.69602632522582,\n 36.76969233214548\n ],\n [\n -116.69602632522582,\n 36.76556695045376\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"53","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-06","publicationStatus":"PW","scienceBaseUri":"592d8edce4b08f9d15be7b79","contributors":{"authors":[{"text":"Seltzer, Alan M.","contributorId":192321,"corporation":false,"usgs":false,"family":"Seltzer","given":"Alan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":696221,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Severinghaus, Jeffrey P.","contributorId":140715,"corporation":false,"usgs":false,"family":"Severinghaus","given":"Jeffrey","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":696222,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Andraski, Brian J. 0000-0002-2086-0417 andraski@usgs.gov","orcid":"https://orcid.org/0000-0002-2086-0417","contributorId":168800,"corporation":false,"usgs":true,"family":"Andraski","given":"Brian","email":"andraski@usgs.gov","middleInitial":"J.","affiliations":[{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true},{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":false,"id":696223,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stonestrom, David A. 0000-0001-7883-3385 dastones@usgs.gov","orcid":"https://orcid.org/0000-0001-7883-3385","contributorId":2280,"corporation":false,"usgs":true,"family":"Stonestrom","given":"David","email":"dastones@usgs.gov","middleInitial":"A.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":696220,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188075,"text":"70188075 - 2017 - Relationships between gas field development and the presence and abundance of pygmy rabbits in southwestern Wyoming","interactions":[],"lastModifiedDate":"2018-08-10T16:14:26","indexId":"70188075","displayToPublicDate":"2017-05-30T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Relationships between gas field development and the presence and abundance of pygmy rabbits in southwestern Wyoming","docAbstract":"More than 5957 km2 in southwestern Wyoming is currently covered by operational gas fields, and further development is projected through 2030. Gas fields fragment landscapes through conversion of native vegetation to roads, well pads, pipeline corridors, and other infrastructure elements. The sagebrush steppe landscape where most of this development is occurring harbors 24 sagebrush-associated species of greatest conservation need, but the effects of gas energy development on most of these species are unknown. Pygmy rabbits (Brachylagus idahoensis) are one such species. In 2011, we began collecting three years of survey data to examine the relationship between gas field development density and pygmy rabbit site occupancy patterns on four major Wyoming gas fields (Continental Divide–Creston–Blue Gap, Jonah, Moxa Arch, Pinedale Anticline Project Area). We surveyed 120 plots across four gas fields, with plots distributed across the density gradient of gas well pads on each field. In a 1 km radius around the center of each plot, we measured the area covered by each of 10 gas field infrastructure elements and by shrub cover using 2012 National Agriculture Imagery Program imagery. We then modeled the relationship between gas field elements, pygmy rabbit presence, and two indices of pygmy rabbit abundance. Gas field infrastructure elements—specifically buried utility corridors and a complex of gas well pads, adjacent disturbed areas, and well pad access roads—were negatively correlated with pygmy rabbit presence and abundance indices, with sharp declines apparent after approximately 2% of the area consisted of gas field infrastructure. We conclude that pygmy rabbits in southwestern Wyoming may be sensitive to gas field development at levels similar to those observed for greater sage-grouse, and may suffer local population declines at lower levels of development than are allowed in existing plans and policies designed to conserve greater sage-grouse by limiting the surface footprint of energy development. Buried utilities, gas well pads, areas adjacent to well pads, and well pad access roads had the strongest negative correlation with pygmy rabbit presence and abundance. Minimizing the surface footprint of these elements may reduce negative impacts of gas energy development on pygmy rabbits.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1817","usgsCitation":"Germaine, S.S., Carter, S.K., Ignizio, D.A., and Freeman, A.T., 2017, Relationships between gas field development and the presence and abundance of pygmy rabbits in southwestern Wyoming: Ecosphere, v. 8, no. 5, e01817: 19 p., https://doi.org/10.1002/ecs2.1817.","productDescription":"e01817: 19 p.","ipdsId":"IP-080749","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true}],"links":[{"id":469821,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1817","text":"Publisher Index Page"},{"id":438328,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BR8QDD","text":"USGS data release","linkHelpText":"Analysis of Land Disturbance and Pygmy Rabbit Occupancy Values Associated With Oil and Gas Extraction in Southwestern Wyoming, 2012"},{"id":341871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112,\n 40\n ],\n [\n -105,\n 40\n ],\n [\n -105,\n 45.75\n ],\n [\n -112,\n 45.75\n ],\n [\n -112,\n 40\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-15","publicationStatus":"PW","scienceBaseUri":"592e84b7e4b092b266f10d1f","contributors":{"authors":[{"text":"Germaine, Stephen S. 0000-0002-7614-2676 germaines@usgs.gov","orcid":"https://orcid.org/0000-0002-7614-2676","contributorId":192417,"corporation":false,"usgs":true,"family":"Germaine","given":"Stephen","email":"germaines@usgs.gov","middleInitial":"S.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":696470,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carter, Sarah K. 0000-0003-3778-8615","orcid":"https://orcid.org/0000-0003-3778-8615","contributorId":192418,"corporation":false,"usgs":true,"family":"Carter","given":"Sarah","email":"","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":696471,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ignizio, Drew A. 0000-0001-8054-5139 dignizio@usgs.gov","orcid":"https://orcid.org/0000-0001-8054-5139","contributorId":139842,"corporation":false,"usgs":true,"family":"Ignizio","given":"Drew","email":"dignizio@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":696472,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Freeman, Aaron T. 0000-0001-9395-5604 afreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-9395-5604","contributorId":5293,"corporation":false,"usgs":true,"family":"Freeman","given":"Aaron","email":"afreeman@usgs.gov","middleInitial":"T.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":696473,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70187975,"text":"70187975 - 2017 - Declines in low-elevation subalpine tree populations outpace growth in high-elevation populations with warming","interactions":[],"lastModifiedDate":"2017-11-22T16:56:07","indexId":"70187975","displayToPublicDate":"2017-05-26T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2242,"text":"Journal of Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Declines in low-elevation subalpine tree populations outpace growth in high-elevation populations with warming","docAbstract":"Figs (Ficus sp.) are often considered as keystone resources which strongly influence tropical forest ecosystems. We used long-term tree-census data to track the population dynamics of two abundant free-standing fig species, Ficus insipida and F. yoponensis, on Barro Colorado Island (BCI), a 15.6-km2 island in Lake Gatún, Panama. Vegetation cover on BCI consists of a mosaic of old growth (>400 years) and maturing (about 90–150 year old) secondary rainforest. Locations and conditions of fig trees have been mapped and monitored on BCI for more than 35 years (1973–2011), with a focus on the Lutz Catchment area (25 ha). The original distribution of the fig trees shortly after the construction of the Panama Canal was derived from an aerial photograph from 1927 and was compared with previous land use and forest status. The distribution of both fig species (~850 trees) is restricted to secondary forest. Of the original 119 trees observed in Lutz Catchment in 1973, >70% of F. insipida and >90% of F. yoponensis had died by 2011. Observations in other areas on BCI support the trend of declining free-standing figs. We interpret the decline of these figs on BCI as a natural process within a maturing tropical lowland forest. Senescence of the fig trees appears to have been accelerated by severe droughts such as the strong El Niño event in the year 1982/83. Because figs form such an important food resource for frugivores, this shift in resource availability is likely to have cascading effects on frugivore populations.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0177060","usgsCitation":"Albrecht, L., Stallard, R.F., and Kalko, E., 2017, Land use history and population dynamics of free-standing figs in a maturing forest: PLoS ONE, v. 12, no. 5, p. 1-18, https://doi.org/10.1371/journal.pone.0177060.","productDescription":"e0177060; 18 p.","startPage":"1","endPage":"18","ipdsId":"IP-087054","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":469826,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0177060","text":"Publisher Index Page"},{"id":341795,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-24","publicationStatus":"PW","scienceBaseUri":"59293e92e4b016f7a94076e8","contributors":{"authors":[{"text":"Albrecht, Larissa","contributorId":192297,"corporation":false,"usgs":false,"family":"Albrecht","given":"Larissa","email":"","affiliations":[],"preferred":false,"id":696145,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stallard, Robert F. 0000-0001-8209-7608 stallard@usgs.gov","orcid":"https://orcid.org/0000-0001-8209-7608","contributorId":1924,"corporation":false,"usgs":true,"family":"Stallard","given":"Robert","email":"stallard@usgs.gov","middleInitial":"F.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":696144,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kalko, Elisabeth","contributorId":192298,"corporation":false,"usgs":false,"family":"Kalko","given":"Elisabeth","email":"","affiliations":[],"preferred":false,"id":696146,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70187999,"text":"70187999 - 2017 - Trends in Rainbow Trout recruitment, abundance, survival, and growth during a boom-and-bust cycle in a tailwater fishery","interactions":[],"lastModifiedDate":"2017-08-03T08:31:55","indexId":"70187999","displayToPublicDate":"2017-05-26T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Trends in Rainbow Trout recruitment, abundance, survival, and growth during a boom-and-bust cycle in a tailwater fishery","docAbstract":"Data from a large-scale mark-recapture study was used in an open population model to determine the cause for long-term trends in growth and abundance of a Rainbow Trout Oncorhynchus mykiss population in the tailwater of Glen Canyon Dam, AZ. Reduced growth affected multiple life stages and processes causing negative feedbacks that regulated the abundance of the population, including: higher mortality of larger fish; lower rates of recruitment (young of year) in years when growth was reduced; and lower rates of sexual maturation the following year. High and steady flows during spring and summer of 2011 resulted in very large recruitment event. The population declined 10-fold by 2016 due a combination of lower recruitment and reduced survival of larger trout. Survival rates for trout ≥ 225 mm in 2014, 2015, and 2016 were 11%, 21%, and 22% lower than average survival rates between 2012 and 2013, respectively. Abundance at the end of the study would have been three- to five-fold higher had survival rates for larger trout remained at the elevated levels estimated for 2012 and 2013. Growth declined between 2012 and 2014 owing to reduced prey availability, which led to very poor fish condition by fall of 2014 (~0.9-0.95). Poor condition in turn resulted in low survival rates of larger fish during fall of 2014 and winter of 2015, which contributed to the population collapse. In Glen Canyon, large recruitment events driven by high flows can lead to increases in the population that cannot be sustained due to limitations in prey supply. In the absence of being able to regulate prey supply, flows which reduce the probability of large recruitment events can be used to avoid boom-and-bust population cycles. Our study demonstrates that mark-recapture is a very informative approach for understanding the dynamics of tailwater trout populations.","language":"English","publisher":"Taylor & Francis","doi":"10.1080/00028487.2017.1317663","usgsCitation":"Korman, J., Yard, M.D., and Kennedy, T., 2017, Trends in Rainbow Trout recruitment, abundance, survival, and growth during a boom-and-bust cycle in a tailwater fishery: Transactions of the American Fisheries Society, v. 146, no. 5, p. 1043-1057, https://doi.org/10.1080/00028487.2017.1317663.","productDescription":"15 p.","startPage":"1043","endPage":"1057","ipdsId":"IP-084735","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":469822,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/journal_contribution/Trends_in_Rainbow_Trout_Recruitment_Abundance_Survival_and_Growth_during_a_Boom-and-Bust_Cycle_in_a_Tailwater_Fishery/5270857","text":"External Repository"},{"id":341791,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"146","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-24","publicationStatus":"PW","scienceBaseUri":"59293e90e4b016f7a94076d8","contributors":{"authors":[{"text":"Korman, Josh","contributorId":139960,"corporation":false,"usgs":false,"family":"Korman","given":"Josh","email":"","affiliations":[{"id":13333,"text":"Ecometric Research Inc.","active":true,"usgs":false}],"preferred":false,"id":696169,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yard, Micheal D. myard@usgs.gov","contributorId":147386,"corporation":false,"usgs":true,"family":"Yard","given":"Micheal","email":"myard@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":696168,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kennedy, Theodore A. tkennedy@usgs.gov","contributorId":140027,"corporation":false,"usgs":true,"family":"Kennedy","given":"Theodore A.","email":"tkennedy@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":696170,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70187992,"text":"70187992 - 2017 - Deleterious effects of net clogging on the quantification of stream drift","interactions":[],"lastModifiedDate":"2017-06-27T13:18:18","indexId":"70187992","displayToPublicDate":"2017-05-26T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Deleterious effects of net clogging on the quantification of stream drift","docAbstract":"Drift studies are central to stream and river ecological research. However, a fundamental aspect of quantifying drift — how net clogging affects the accuracy of results — has been widely ignored. Utilizing approaches from plankton and suspended sediment studies in oceanography and hydrology, we examined the rate and dynamics of net clogging across a range of conditions. We found that nets clog nonlinearly over time and that suspended solid concentrations and net mesh size exerted a strong effect on clogging rates. Critically, net clogging introduced unpredictable biases in resultant data due to the inaccuracies in water volume estimates introduced by progressive clogging. This renders the widespread approach to linearly “correct” for clogging inadequate. Using a meta-analysis of 77 drift studies spanning 25 years, we demonstrate that the detrimental effects of net clogging are routinely unappreciated, even though the results of most of these studies were likely affected by clogging. We close by describing an approach for avoiding net clogging, which will increase the accuracy and reproducibility of results in future freshwater, lotic drift studies.
","language":"English","publisher":"NRC Research Press","doi":"10.1139/cjfas-2016-0365","usgsCitation":"Muehlbauer, J.D., Kennedy, T., Copp, A.J., and Sabol, T.A., 2017, Deleterious effects of net clogging on the quantification of stream drift: Canadian Journal of Fisheries and Aquatic Sciences, v. 74, no. 7, p. 1041-1048, https://doi.org/10.1139/cjfas-2016-0365.","productDescription":"8 p.","startPage":"1041","endPage":"1048","ipdsId":"IP-078467","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":469823,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2016-0365","text":"External Repository"},{"id":438329,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71J97WD","text":"USGS data release","linkHelpText":"Stream Drift Sampling in Arizona, 2014Data"},{"id":341792,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"74","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59293e92e4b016f7a94076de","contributors":{"authors":[{"text":"Muehlbauer, Jeffrey D. 0000-0003-1808-580X jmuehlbauer@usgs.gov","orcid":"https://orcid.org/0000-0003-1808-580X","contributorId":5045,"corporation":false,"usgs":true,"family":"Muehlbauer","given":"Jeffrey","email":"jmuehlbauer@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":696164,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kennedy, Theodore A. tkennedy@usgs.gov","contributorId":140027,"corporation":false,"usgs":true,"family":"Kennedy","given":"Theodore A.","email":"tkennedy@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":696165,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Copp, Adam J. 0000-0001-7385-0055 acopp@usgs.gov","orcid":"https://orcid.org/0000-0001-7385-0055","contributorId":5194,"corporation":false,"usgs":true,"family":"Copp","given":"Adam","email":"acopp@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":696166,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sabol, Thomas A. 0000-0002-4299-2285 tsabol@usgs.gov","orcid":"https://orcid.org/0000-0002-4299-2285","contributorId":3403,"corporation":false,"usgs":true,"family":"Sabol","given":"Thomas","email":"tsabol@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":696167,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70189009,"text":"70189009 - 2017 - Avian influenza virus RNA in groundwater wells supplying poultry farms affected by the 2015 influenza outbreak","interactions":[],"lastModifiedDate":"2017-07-12T10:25:34","indexId":"70189009","displayToPublicDate":"2017-05-26T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5022,"text":"Environmental Science & Technology Letters","onlineIssn":"2328-8930","active":true,"publicationSubtype":{"id":10}},"title":"Avian influenza virus RNA in groundwater wells supplying poultry farms affected by the 2015 influenza outbreak","docAbstract":"During the 2015 outbreak of highly pathogenic avian influenza virus\n(HPAI) on poultry farms in the midwestern United States, concern was raised\nabout the potential for HPAI to contaminate groundwater. Our study objective was to evaluate the occurrence of HPAI in the groundwater supply wells on 13 outbreak-affected poultry farms in Iowa and Wisconsin. We sampled 20 wells, six waste-storage lagoons, and one pond. Three wells and one lagoon were positive for the matrix gene indicative of influenza A virus. Using a semi-nested qPCR assay specific to the H5 HPAI outbreak strain, one well was H5-positive, matching the outbreak virus hemagglutinin gene. Matrix gene-positive samples analyzed for avian influenza virus (AIV) by cell culture and embryonating egg culture were negative. Seven wells were positive by PCR for a poultry-specific parvovirus, thus providing corroborating evidence of virus transport pathways between poultry fecal wastes and groundwater. Our data suggest it is possible for AIV to be transported to groundwater, and during an outbreak, the potential for poultry farm wells to become contaminated with AIV should be considered.","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.estlett.7b00128","usgsCitation":"Borchardt, M.A., Spencer, S.K., Hubbard, L.E., Firnstahl, A.D., Stokdyk, J.P., and Kolpin, D.W., 2017, Avian influenza virus RNA in groundwater wells supplying poultry farms affected by the 2015 influenza outbreak: Environmental Science & Technology Letters, v. 4, no. 7, p. 268-272, https://doi.org/10.1021/acs.estlett.7b00128.","productDescription":"5 p.","startPage":"268","endPage":"272","ipdsId":"IP-078487","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":343120,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa, 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jstokdyk@usgs.gov","orcid":"https://orcid.org/0000-0003-2887-6277","contributorId":193848,"corporation":false,"usgs":true,"family":"Stokdyk","given":"Joel","email":"jstokdyk@usgs.gov","middleInitial":"P.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":702409,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kolpin, Dana W. 0000-0002-3529-6505 dwkolpin@usgs.gov","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":1239,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana","email":"dwkolpin@usgs.gov","middleInitial":"W.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":702410,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70186903,"text":"ofr20161050 - 2017 - Water-quality and lake-stage data for Wisconsin lakes, water years 2012–2013","interactions":[],"lastModifiedDate":"2018-02-06T11:47:04","indexId":"ofr20161050","displayToPublicDate":"2017-05-25T15:45:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1050","title":"Water-quality and lake-stage data for Wisconsin lakes, water years 2012–2013","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with local and other agencies, collects data at selected lakes throughout Wisconsin. These data, accumulated over many years, provide a data base for developing an improved understanding of the water quality of lakes. To make these data available to interested parties outside the USGS, the data are published annually in this report series. The locations of water-quality and lake-stage stations in Wisconsin for water year 2012 are shown in figure 1. A water year is the 12-month period from October 1 through September 30. It is designated by the calendar year in which it ends. Thus, the period October 1, 2011 through September 30, 2012, is called “water year 2012.”
The purpose of this report is to provide information about the chemical and physical characteristics of Wisconsin lakes. Data that have been collected at specific lakes, and information to aid in the interpretation of those data, are included in this report. Data collected include measurements of in-lake water quality and lake stage. Time series of Secchi depths, surface total phosphorus and chlorophyll a concentrations collected during non-frozen periods are included for all lakes. Graphs of vertical profiles of temperature, dissolved oxygen, pH, and specific conductance are included for sites where these parameters were measured. Descriptive information for each lake includes: location of the lake, area of the lake’s watershed, period for which data are available, revisions to previously published records, and pertinent remarks. Additional data, such as streamflow and water quality in tributary and outlet streams of some of the lakes, are published online at http://nwis.waterdata.usgs.gov/wi/nwis.
Water-resources data, including stage and discharge data at most streamflow-gaging stations, are available online. The Wisconsin Water Science Center’s home page is at https://www.usgs.gov/centers/wisconsin-water-science-center. Information on the Wisconsin Water Science Center’s Lakes Program is found at http://wi.water.usgs.gov/lakes/index.html and http://wi.water.usgs.gov/projects/index.html.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161050","collaboration":"Prepared in cooperation with the State of Wisconsin and with other agencies","usgsCitation":"Manteufel, S.B., and Robertson, D.M., 2017, Water-quality and lake-stage data for Wisconsin lakes, water years 2012–2013: U.S. Geological Survey Open-File Report 2016–1050, 320 p., https://doi.org/10.3133/ofr20161050.","productDescription":"vi, 153 p.","numberOfPages":"323","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-066231","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":341495,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/preview/ofr20161131","text":"Open-File Report 2016–1131","linkHelpText":"- Water-Quality and Lake-Stage Data for Wisconsin Lakes, Water Year 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\"}}]}","contact":"Director, Wisconsin Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
The U.S. Geological Survey (USGS), in cooperation with local and other agencies, collects data at selected lakes throughout Wisconsin. These data, accumulated over many years, provide a database for developing an improved understanding of the water quality of lakes. To make these data available to interested parties outside the USGS, the data are published annually in this report series. The locations of water-quality and lake-stage stations in Wisconsin for water year 2014 are shown in figure 1. A water year is the 12-month period from October 1 through September 30. It is designated by the calendar year in which it ends. Thus, the period
October 1, 2013, through September 30, 2014, is called “water year 2014.”
The purpose of this report is to provide information about the chemical and physical characteristics of Wisconsin lakes. Data that have been collected at specific lakes, and information to aid in the interpretation of those data, are included in this report. Data collected include measurements of in-lake water quality and lake stage. Time series of Secchi depths, surface total phosphorus, and chlorophyll a concentrations collected during nonfrozen periods are included for many lakes. Graphs of vertical profiles of temperature, dissolved oxygen, pH, and specific conductance are included for sites where these parameters were measured. Descriptive information for each lake includes the location of the lake, area of the lake’s watershed, period for which data are available, revisions to previously published records, and pertinent remarks. Additional data, such as streamflow and water quality in tributary and outlet streams of some of the lakes, are published online at http://nwis.waterdata.usgs.gov/wi/nwis.
Water-resources data, including stage and discharge data at most streamflow-gaging stations, are available online. The Wisconsin Water Science Center’s home page is at https://www.usgs.gov/centers/wisconsin-water-science-center. Information about the Wisconsin Water Science Center’s Lakes Program is found at http://wi.water.usgs.gov/lakes/index.html and http://wi.water.usgs.gov/projects/index.html.
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\"}}]}","contact":"Director, Wisconsin Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
U.S. Geological Survey scientists and technical support staff measured oceanographic, waterquality, seabed-elevation-change, and meteorological parameters in Chincoteague Bay, Maryland and Virginia, during the period of August 13, 2014, to July 14, 2015, as part of the Estuarine Physical Response to Storms project (GS2–2D) supported by the Department of the Interior Hurricane Sandy recovery program. These measurements provide time series data that quantify the response and can be used to better understand the resilience of this back-barrier estuarine system to storms. The Assateague Island National Seashore (National Park Service) and the Chincoteague National Wildlife Refuge (U.S. Fish and Wildlife Service) are on the east side of Chincoteague Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171032","usgsCitation":"Suttles, S.E., Ganju, N.K, Brosnahan, S.M., Montgomery, E.T., Dickhudt, P.J., Beudin, Alexis, Nowacki, D.J., and Martini, M.A., 2017, Summary of oceanographic and water-quality measurements in Chincoteague Bay, Maryland and Virginia, 2014–15: U.S. Geological Survey Open-File Report 2017–1032, 95 p., https://doi.org/10.3133/ofr20171032.","productDescription":"xii, 95 p.","numberOfPages":"112","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-079627","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":341661,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DF6PBV","text":"USGS data release","description":"USGS data release","linkHelpText":"Oceanographic and water-quality measurements in Chincoteague Bay, Maryland/Virginia, 2014–2015 "},{"id":341660,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1032/ofr20171032.pdf","text":"Report","size":"25.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1032"},{"id":338262,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1032/coverthb.jpg"},{"id":341663,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7WD3XSF","text":"USGS data release","description":"USGS data release","linkHelpText":"Water samples in support of oceanographic and water-quality measurements in Chincoteague Bay, Maryland and Virginia, 2014–15, U.S. Geological Survey Field Activity 2014-048-FA"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Chincoteague Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.05996704101562,\n 38.34273329203372\n ],\n [\n -75.11489868164062,\n 38.36857886877816\n ],\n [\n -75.23300170898438,\n 38.28885871419223\n ],\n [\n -75.35110473632812,\n 38.13455657705411\n ],\n [\n -75.52001953125,\n 37.90194871393947\n ],\n [\n -75.38681030273436,\n 37.826056694926535\n ],\n [\n -75.16021728515624,\n 38.08160859009049\n ],\n [\n -75.05996704101562,\n 38.34273329203372\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
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Earthquakes pose a threat to the safety of over 143 million people living in the United States. Earthquake impacts can be significantly reduced if communities understand their risk and take proactive steps to mitigate that risk. The Advanced National Seismic System (ANSS) is a cooperative effort to collect and analyze seismic and geodetic data on earthquakes, issue timely and reliable notifications of their occurrence and impacts, and provide data for earthquake research and the hazard and risk assessments that are the foundation for creating an earthquakeresilient nation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1429","isbn":"978-1-4113-4136-4","usgsCitation":"U.S. Geological Survey, 2017, Advanced National Seismic System—Current status, development opportunities, and priorities for 2017–2027 (ver.1.1, July 2017): U.S. Geological Survey Circular 1429, 32 p., https://doi.org/10.3133/cir1429.","productDescription":"vii, 32 p.","numberOfPages":"44","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-079172","costCenters":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":341183,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1429/circ1429.pdf","text":"Report","size":"9.53 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1429"},{"id":341182,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1429/coverthb2.jpg"},{"id":345080,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/circ/1429/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"}}],"edition":"Version 1.0: Originally posted May 25, 2017; Version 1.1: July 18, 2017","contact":"Earthquake Hazards Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Mail Stop 905
Reston, VA 20192
Hoover Reservoir, an important drinking water supply for the City of Columbus, Ohio, has been the source of a series of taste and odor problems in treated drinking water during the past few years. These taste and odor problems were caused by the compounds geosmin and 2-methylisoborneol, which are thought to have been related to cyanobacteria blooms. In an effort to reduce the phosphorus available for cyanobacteria blooms at fall turnover, the City of Columbus began experimenting with the dam’s selective withdrawal system to remove excess phosphorus in the hypolimnion, which is released from bottom sediments during summer anoxic conditions.
The U.S. Geological Survey completed two synoptic survey campaigns to assess distributions of water quality and water velocity in the lower part of Hoover Reservoir to provide information on the changes to reservoir dynamics caused by changing dam operations. One campaign (campaign 1) was done while water was being withdrawn from the reservoir through the dam’s middle gate and the other (campaign 2) while water was being withdrawn through the dam’s lower gate. Velocities were measured using an acoustic Doppler current profiler, and water-quality parameters were measured using an autonomous underwater vehicle equipped with water-quality sensors. Along with the water-quality and water-velocity data, meteorological, inflow and outflow discharges, and independent water-quality data were compiled to monitor changes in other parameters that affect reservoir behavior. Monthly nutrient data, collected by the City of Columbus, were also analyzed for trends in concentration during periods of expected stratification.
Based on the results of the two campaigns, when compared to withdrawing water through the middle gate, withdrawing water through the lower gate seemed to increase shear-driven mixing across the thermocline, which resulted in an increase in the depth of the epilimnion throughout the lower part of Hoover Reservoir. The observations from this study, if repeatable and driven primarily by changes in gate operations, can inform nutrient management strategies for Hoover Reservoir. Increased mixing across the thermocline may potentially supply nutrients from the hypolimnion to algae in the epilimnion. Although operation of the lower gate has the potential to export nutrients from the hypolimnion (where the concentrations of nutrients have typically been higher during summer months) through two mechanisms (direct withdrawal and mixing into the epilimnion), supply of nutrients to the epilimnion through enhanced mixing could lead to a short-term increase in algal populations. Therefore, further study is recommended to (1) test the repeatability of the results of gate changes on water-quality distributions and circulation patterns in lower Hoover Reservoir, (2) identify the immediate effect of gate changes on nutrient concentrations in the water column, and (3) identify the best management practices to reduce the nutrient storage in the hypolimnion of Hoover Reservoir without increasing the potential for nutrient transport to the highly productive epilimnion.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175027","usgsCitation":"Vonins, B.L., and Jackson, P.R., 2017, Response of currents and water quality to changes in dam operations in Hoover Reservoir, Columbus, Ohio, August 24–28, 2015: U.S. Geological Survey Scientific Investigations Report 2017–5027, 62 p., https://doi.org/10.3133/sir20175027.","productDescription":"Report: vii, 61 p.; Data Release","numberOfPages":"74","onlineOnly":"Y","ipdsId":"IP-080214","costCenters":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"links":[{"id":438332,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75X271K","text":"USGS data release","linkHelpText":"Survey of velocity and bathymetery in Hoover Reservoir, ADCP source data, Columbus, OH (August, 2015)"},{"id":341790,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F70863D8","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Three-dimensional point measurements of basic water-quality parameters in Hoover Reservoir near Westerville, Ohio, August 25 and 27, 2015"},{"id":341771,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F75X271K","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Survey of velocity and bathymetry in Hoover Reservoir, ADCP source data, Columbus, OH (August, 2015)"},{"id":341700,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5027/sir20175027.pdf","text":"Report","size":"55.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5027"},{"id":341699,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5027/coverthb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Hoover Dam, Hoover Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.91999816894531,\n 39.989221102071994\n ],\n [\n -82.83004760742188,\n 39.989221102071994\n ],\n [\n -82.83004760742188,\n 40.25647271628502\n ],\n [\n -82.91999816894531,\n 40.25647271628502\n ],\n [\n -82.91999816894531,\n 39.989221102071994\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio Water Science Center
U.S. Geological Survey
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Urban growth often influences the production of ecosystem services. The impacts of urbanization on landscapes can subsequently affect landowners’ perceptions, values and decisions regarding their land. Within land-use and land-change research, very few models of dynamic landscape-scale processes like urbanization incorporate empirically-grounded landowner decision-making processes. Very little attention has focused on the heterogeneous decision-making processes that aggregate to influence broader-scale patterns of urbanization. We examine the land-use tradeoffs faced by individual landowners in one of the United States’ most rapidly urbanizing regions − the urban area surrounding Charlotte, North Carolina. We focus on the land-use decisions of non-industrial private forest owners located across the region’s development gradient. A discrete choice experiment is used to determine the critical factors influencing individual forest owners’ intent to sell their undeveloped properties across a series of experimentally varied scenarios of urban growth. Data are analyzed using a hierarchical Bayesian approach. The estimates derived from the survey data are used to modify a spatially-explicit trend-based urban development potential model, derived from remotely-sensed imagery and observed changes in the region’s socioeconomic and infrastructural characteristics between 2000 and 2011. This modeling approach combines the theoretical underpinnings of behavioral economics with spatiotemporal data describing a region’s historical development patterns. By integrating empirical social preference data into spatially-explicit urban growth models, we begin to more realistically capture processes as well as patterns that drive the location, magnitude and rates of urban growth.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.landurbplan.2017.03.004","usgsCitation":"Smith, J.W., Smart, L.S., Dorning, M., Dupey, L.N., Meley, A., and Meentemeyer, R.K., 2017, Bayesian methods to estimate urban growth potential: Landscape and Urban Planning, v. 163, p. 1-16, https://doi.org/10.1016/j.landurbplan.2017.03.004.","productDescription":"17 p.","startPage":"1","endPage":"16","ipdsId":"IP-076460","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":469829,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.landurbplan.2017.03.004","text":"Publisher Index Page"},{"id":341664,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"163","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59269bb4e4b0b7ff9fb4895d","contributors":{"authors":[{"text":"Smith, Jordan W.","contributorId":177326,"corporation":false,"usgs":false,"family":"Smith","given":"Jordan","email":"","middleInitial":"W.","affiliations":[{"id":12682,"text":"Utah State University, Logan, UT","active":true,"usgs":false}],"preferred":false,"id":695971,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smart, Lindsey S.","contributorId":192250,"corporation":false,"usgs":false,"family":"Smart","given":"Lindsey","email":"","middleInitial":"S.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":695972,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dorning, Monica 0000-0002-7576-1256 mdorning@usgs.gov","orcid":"https://orcid.org/0000-0002-7576-1256","contributorId":191772,"corporation":false,"usgs":true,"family":"Dorning","given":"Monica","email":"mdorning@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":695970,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dupey, Lauren Nicole","contributorId":192251,"corporation":false,"usgs":false,"family":"Dupey","given":"Lauren","email":"","middleInitial":"Nicole","affiliations":[{"id":12682,"text":"Utah State University, Logan, UT","active":true,"usgs":false}],"preferred":false,"id":695973,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meley, Andreanne","contributorId":192252,"corporation":false,"usgs":false,"family":"Meley","given":"Andreanne","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":695974,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Meentemeyer, Ross K.","contributorId":179341,"corporation":false,"usgs":false,"family":"Meentemeyer","given":"Ross","email":"","middleInitial":"K.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":695975,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70187875,"text":"70187875 - 2017 - Variation in species-level plant functional traits over wetland indicator status categories","interactions":[],"lastModifiedDate":"2017-06-14T11:56:48","indexId":"70187875","displayToPublicDate":"2017-05-24T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Variation in species-level plant functional traits over wetland indicator status categories","docAbstract":"Wetland indicator status (WIS) describes the habitat affinity of plant species and is used in wetland delineations and resource inventories. Understanding how species-level functional traits vary across WIS categories may improve designations, elucidate mechanisms of adaptation, and explain habitat optima and niche. We investigated differences in species-level traits of riparian flora across WIS categories, extending their application to indicate hydrologic habitat. We measured or compiled data on specific leaf area (SLA), stem specific gravity (SSG), seed mass, and mature height of 110 plant species that occur along the Colorado River in Grand Canyon, Arizona. Additionally, we measured leaf δ13C, δ15N, % carbon, % nitrogen, and C/N ratio of 56 species with C3 photosynthesis. We asked the following: (i) How do species-level traits vary over WIS categories? (ii) Does the pattern differ between herbaceous and woody species? (iii) How well do multivariate traits define WIS categories? (iv) Which traits are correlated? The largest trait differences among WIS categories for herbaceous species occurred for SSG, seed mass, % leaf carbon and height, and for woody species occurred for height, SSG, and δ13C. SSG increased and height decreased with habitat aridity for both woody and herbaceous species. The δ13C and hence water use efficiency of woody species increased with habitat aridity. Water use efficiency of herbaceous species increased with habitat aridity via greater occurrence of C4 grasses. Multivariate trait assemblages differed among WIS categories. Over all species, SLA was correlated with height, δ13C, % leaf N, and C/N; height was correlated with SSG and % leaf C; SSG was correlated with % leaf C. Adaptations of both herbaceous and woody riparian species to wet, frequently inundated habitats include low-density stem tissue. Adaptations to drier habitats in the riparian zone include short, high-density cavitation-resistant stem tissue, and high water use efficiency. The results enhance understanding about using traits to describe plant habitat in riparian systems.
","language":"English","publisher":"Blackwell Pub. Ltd","doi":"10.1002/ece3.2975","usgsCitation":"McCoy-Sulentic, M.E., Kolb, T.E., Merritt, D.M., Palmquist, E.C., Ralston, B.E., and Sarr, D.A., 2017, Variation in species-level plant functional traits over wetland indicator status categories: Ecology and Evolution, v. 7, no. 11, p. 3732-3744, https://doi.org/10.1002/ece3.2975.","productDescription":"13 p.","startPage":"3732","endPage":"3744","ipdsId":"IP-083993","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":469833,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.2975","text":"Publisher Index Page"},{"id":438333,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BV7DTQ","text":"USGS data release","linkHelpText":"Plant functional traits, Colorado River, Grand Canyon, 2012-2014Data"},{"id":341617,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon, Marble Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.4503173828125,\n 35.73090666520053\n ],\n [\n -111.53045654296874,\n 35.73090666520053\n ],\n [\n -111.53045654296874,\n 36.90597988519294\n ],\n [\n -113.4503173828125,\n 36.90597988519294\n ],\n [\n -113.4503173828125,\n 35.73090666520053\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-17","publicationStatus":"PW","scienceBaseUri":"59269bb5e4b0b7ff9fb48963","contributors":{"authors":[{"text":"McCoy-Sulentic, Miles E.","contributorId":192228,"corporation":false,"usgs":false,"family":"McCoy-Sulentic","given":"Miles","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":695858,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kolb, Thomas E.","contributorId":189073,"corporation":false,"usgs":false,"family":"Kolb","given":"Thomas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":695859,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Merritt, David M.","contributorId":192229,"corporation":false,"usgs":false,"family":"Merritt","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":24595,"text":"USDA Forest Service, Fort Collins CO","active":true,"usgs":false}],"preferred":false,"id":695860,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Palmquist, Emily C. 0000-0003-1069-2154 epalmquist@usgs.gov","orcid":"https://orcid.org/0000-0003-1069-2154","contributorId":5669,"corporation":false,"usgs":true,"family":"Palmquist","given":"Emily","email":"epalmquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":695857,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ralston, Barbara E. 0000-0001-9991-8994 bralston@usgs.gov","orcid":"https://orcid.org/0000-0001-9991-8994","contributorId":606,"corporation":false,"usgs":true,"family":"Ralston","given":"Barbara","email":"bralston@usgs.gov","middleInitial":"E.","affiliations":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"preferred":false,"id":695861,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sarr, Daniel A. dsarr@usgs.gov","contributorId":191593,"corporation":false,"usgs":false,"family":"Sarr","given":"Daniel","email":"dsarr@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":false,"id":695862,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}