{"pageNumber":"529","pageRowStart":"13200","pageSize":"25","recordCount":46670,"records":[{"id":70101650,"text":"70101650 - 2014 - A review of environmental impacts of salts from produced waters on aquatic resources","interactions":[],"lastModifiedDate":"2018-09-04T16:35:40","indexId":"70101650","displayToPublicDate":"2014-04-10T10:31:34","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"A review of environmental impacts of salts from produced waters on aquatic resources","docAbstract":"Salts are frequently a major constituent of waste waters produced during oil and gas production. These produced waters or brines must be treated and/or disposed and provide a daily challenge for operators and resource managers. Some elements of salts are regulated with water quality criteria established for the protection of aquatic wildlife, e.g. chloride (Cl<sup>−</sup>), which has an acute standard of 860 mg/L and a chronic standard of 230 mg/L. However, data for establishing such standards has only recently been studied for other components of produced water, such as bicarbonate (HCO<sub>3</sub><sup>−</sup>), which has acute median lethal concentrations (LC50s) ranging from 699 to > 8000 mg/L and effects on chronic toxicity from 430 to 657 mg/L. While Cl− is an ion of considerable importance in multiple geographical regions, knowledge about the effects of hardness (calcium and magnesium) on its toxicity and about mechanisms of toxicity is not well understood. A multiple-approach design that combines studies of both individuals and populations, conducted both in the laboratory and the field, was used to study toxic effects of bicarbonate (as NaHCO<sub>3</sub>). This approach allowed interpretations about mechanisms related to growth effects at the individual level that could affect populations in the wild. However, additional mechanistic data for HCO<sub>3</sub><sup>−</sup>, related to the interactions of calcium (Ca<sup>2 +</sup>) precipitation at the microenvironment of the gill would dramatically increase the scientific knowledge base about how NaHCO<sub>3</sub> might affect aquatic life. Studies of the effects of mixtures of multiple salts present in produced waters and more chronic effect studies would give a better picture of the overall potential toxicity of these ions. Organic constituents in hydraulic fracturing fluids, flowback waters, etc. are a concern because of their carcinogenic properties and this paper is not meant to minimize the importance of maintaining vigilance with respect to potential organic contamination.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"International Journal of Coal Geology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2013.12.006","usgsCitation":"Farag, A., and Harper, D., 2014, A review of environmental impacts of salts from produced waters on aquatic resources: International Journal of Coal Geology, v. 126, p. 157-161, https://doi.org/10.1016/j.coal.2013.12.006.","productDescription":"5 p.","startPage":"157","endPage":"161","ipdsId":"IP-049236","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":286284,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":286281,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.coal.2013.12.006"}],"volume":"126","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53516ef9e4b05569d8059f34","contributors":{"authors":[{"text":"Farag, Aïda M.","contributorId":85880,"corporation":false,"usgs":true,"family":"Farag","given":"Aïda M.","affiliations":[],"preferred":false,"id":492720,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harper, David D.","contributorId":102946,"corporation":false,"usgs":true,"family":"Harper","given":"David D.","affiliations":[],"preferred":false,"id":492721,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70059913,"text":"70059913 - 2014 - Three-dimensional distribution of igneous rocks near the Pebble porphyry Cu-Au-Mo deposit in southwestern Alaska: constraints from regional-scale aeromagnetic data","interactions":[],"lastModifiedDate":"2014-04-10T10:34:00","indexId":"70059913","displayToPublicDate":"2014-04-10T10:27:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1808,"text":"Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Three-dimensional distribution of igneous rocks near the Pebble porphyry Cu-Au-Mo deposit in southwestern Alaska: constraints from regional-scale aeromagnetic data","docAbstract":"Aeromagnetic data helped us to understand the 3D distribution of plutonic rocks near the Pebble porphyry copper deposit in southwestern Alaska, USA. Magnetic susceptibility measurements showed that rocks in the Pebble district are more magnetic than rocks of comparable compositions in the Pike Creek–Stuyahok Hills volcano-plutonic complex. The reduced-to-pole transformation of the aeromagnetic data demonstrated that the older rocks in the Pebble district produce strong magnetic anomaly highs. The tilt derivative transformation highlighted northeast-trending lineaments attributed to Tertiary volcanic rocks. Multiscale edge detection delineated near-surface magnetic sources that are mostly outward dipping and coalesce at depth in the Pebble district. The total horizontal gradient of the 10-km upward-continued magnetic data showed an oval, deep magnetic contact along which porphyry deposits occur. Forward and inverse magnetic modeling showed that the magnetic rocks in the Pebble district extend to depths greater than 9 km. Magnetic inversion was constrained by a near-surface, 3D geologic model that is attributed with measured magnetic susceptibilities from various rock types in the region. The inversion results indicated that several near-surface magnetic sources with moderate susceptibilities converge with depth into magnetic bodies with higher susceptibilities. This deep magnetic source appeared to rise toward the surface in several areas. An isosurface value of 0.02 SI was used to depict the magnetic contact between outcropping granodiorite and nonmagnetic sedimentary host rocks. The contact was shown to be outward dipping. At depths around 5 km, nearly the entire model exceeded the isosurface value indicating the limits of nonmagnetic host material. The inversion results showed the presence of a relatively deep, northeast-trending magnetic low that parallels lineaments mapped by the tilt derivative. This deep low represents a strand of the Lake Clark fault.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Geophysics","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Society of Exploration Geophysicists","doi":"10.1190/geo2013-0326.1","usgsCitation":"Anderson, E.D., Zhou, W., Li, Y., Hitzman, M., Monecke, T., Lang, J.R., and Kelley, K., 2014, Three-dimensional distribution of igneous rocks near the Pebble porphyry Cu-Au-Mo deposit in southwestern Alaska: constraints from regional-scale aeromagnetic data: Geophysics, v. 79, no. 2, p. B63-B79, https://doi.org/10.1190/geo2013-0326.1.","productDescription":"17 p.","startPage":"B63","endPage":"B79","numberOfPages":"17","ipdsId":"IP-051177","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":286163,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1190/geo2013-0326.1"},{"id":286168,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -160.0049,58.8251 ], [ -160.0049,61.122 ], [ -151.9958,61.122 ], [ -151.9958,58.8251 ], [ -160.0049,58.8251 ] ] ] } } ] }","volume":"79","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53517069e4b05569d805a40a","contributors":{"authors":[{"text":"Anderson, Eric D. 0000-0002-0138-6166 ericanderson@usgs.gov","orcid":"https://orcid.org/0000-0002-0138-6166","contributorId":1733,"corporation":false,"usgs":true,"family":"Anderson","given":"Eric","email":"ericanderson@usgs.gov","middleInitial":"D.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":487844,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhou, Wei","contributorId":82221,"corporation":false,"usgs":true,"family":"Zhou","given":"Wei","email":"","affiliations":[],"preferred":false,"id":487850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Yaoguo","contributorId":80184,"corporation":false,"usgs":true,"family":"Li","given":"Yaoguo","email":"","affiliations":[],"preferred":false,"id":487849,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hitzman, Murray W.","contributorId":14682,"corporation":false,"usgs":true,"family":"Hitzman","given":"Murray W.","affiliations":[],"preferred":false,"id":487845,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Monecke, Thomas","contributorId":50423,"corporation":false,"usgs":true,"family":"Monecke","given":"Thomas","affiliations":[],"preferred":false,"id":487847,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lang, James R.","contributorId":39679,"corporation":false,"usgs":true,"family":"Lang","given":"James","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":487846,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kelley, Karen D. 0000-0002-3232-5809","orcid":"https://orcid.org/0000-0002-3232-5809","contributorId":57817,"corporation":false,"usgs":true,"family":"Kelley","given":"Karen D.","affiliations":[],"preferred":false,"id":487848,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70048692,"text":"70048692 - 2014 - The long-term trends (1982-2006) in vegetation greenness of the alpine ecosystem in the Qinghai-Tibetan Plateau","interactions":[],"lastModifiedDate":"2014-08-29T14:49:00","indexId":"70048692","displayToPublicDate":"2014-04-10T10:12:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1534,"text":"Environmental Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"The long-term trends (1982-2006) in vegetation greenness of the alpine ecosystem in the Qinghai-Tibetan Plateau","docAbstract":"The increased rate of annual temperature in the Qinghai-Tibetan Plateau exceeded all other areas of the same latitude in recent decades. The influence of the warming climate on the alpine ecosystem of the plateau was distinct. An analysis of alpine vegetation under changes in climatic conditions was conducted in this study. This was done through an examination of vegetation greenness and its relationship with climate variability using the Advanced Very High Resolution Radiometer satellite imagery and climate datasets. Vegetation in the plateau experienced a positive trend in greenness, with 18.0 % of the vegetated areas exhibiting significantly positive trends, which were primarily located in the eastern and southwestern parts of the plateau. In grasslands, 25.8 % of meadows and 14.1 % of steppes exhibited significant upward trends. In contrast, the broadleaf forests experienced a trend of degradation. Temperature, particularly summer temperature, was the primary factor promoting the vegetation growth in the plateau. The wetter and warmer climate in the east contributed to the favorable conditions for vegetation. The alpine meadow was mostly sensitive to temperature, while the steppes were sensitive to both temperature and precipitation. Although a warming climate was expected to be beneficial to vegetation growth in the alpine region, the rising temperature coupled with reduced precipitation in the south did not favor vegetation growth due to low humidity and poor soil moisture conditions.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Environmental Earth Sciences","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1007/s12665-014-3092-1","usgsCitation":"Zhang, L., Guo, H., Wang, C., Ji, L., Li, J., Wang, K., and Dai, L., 2014, The long-term trends (1982-2006) in vegetation greenness of the alpine ecosystem in the Qinghai-Tibetan Plateau: Environmental Earth Sciences, v. 72, no. 6, p. 1827-1841, https://doi.org/10.1007/s12665-014-3092-1.","productDescription":"15 p.","startPage":"1827","endPage":"1841","numberOfPages":"15","ipdsId":"IP-045105","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":286156,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":286151,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s12665-014-3092-1"}],"country":"China","otherGeospatial":"Qinghai-tibetan Plateau","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 73.31,25.0 ], [ 73.31,41.79 ], [ 105.06,41.79 ], [ 105.06,25.0 ], [ 73.31,25.0 ] ] ] } } ] }","volume":"72","issue":"6","noUsgsAuthors":false,"publicationDate":"2014-02-12","publicationStatus":"PW","scienceBaseUri":"53517069e4b05569d805a400","contributors":{"authors":[{"text":"Zhang, Li","contributorId":98139,"corporation":false,"usgs":true,"family":"Zhang","given":"Li","affiliations":[],"preferred":false,"id":485440,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guo, Huadong","contributorId":21056,"corporation":false,"usgs":true,"family":"Guo","given":"Huadong","email":"","affiliations":[],"preferred":false,"id":485438,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Cuizhen","contributorId":16312,"corporation":false,"usgs":true,"family":"Wang","given":"Cuizhen","email":"","affiliations":[],"preferred":false,"id":485437,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ji, Lei 0000-0002-6133-1036 lji@usgs.gov","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":2832,"corporation":false,"usgs":true,"family":"Ji","given":"Lei","email":"lji@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":485435,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Li, Jing","contributorId":9166,"corporation":false,"usgs":true,"family":"Li","given":"Jing","email":"","affiliations":[],"preferred":false,"id":485436,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wang, Kun","contributorId":51648,"corporation":false,"usgs":true,"family":"Wang","given":"Kun","email":"","affiliations":[],"preferred":false,"id":485439,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dai, Lin","contributorId":104811,"corporation":false,"usgs":true,"family":"Dai","given":"Lin","email":"","affiliations":[],"preferred":false,"id":485441,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70059037,"text":"70059037 - 2014 - Testing the accuracy of a 1-D volcanic plume model in estimating mass eruption rate","interactions":[],"lastModifiedDate":"2019-03-11T10:56:51","indexId":"70059037","displayToPublicDate":"2014-04-10T09:23:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2316,"text":"Journal of Geophysical Research D: Atmospheres","active":true,"publicationSubtype":{"id":10}},"title":"Testing the accuracy of a 1-D volcanic plume model in estimating mass eruption rate","docAbstract":"During volcanic eruptions, empirical relationships are used to estimate mass eruption rate from plume height. Although simple, such relationships can be inaccurate and can underestimate rates in windy conditions. One-dimensional plume models can incorporate atmospheric conditions and give potentially more accurate estimates. Here I present a 1-D model for plumes in crosswind and simulate 25 historical eruptions where plume height <i>H</i><sub>obs</sub> was well observed and mass eruption rate <i>M</i><sub>obs</sub> could be calculated from mapped deposit mass and observed duration. The simulations considered wind, temperature, and phase changes of water. Atmospheric conditions were obtained from the National Center for Atmospheric Research Reanalysis 2.5° model. Simulations calculate the minimum, maximum, and average values (<i>M</i><sub>min</sub>, <i>M</i><sub>max</sub>, and <i>M</i><sub>avg</sub>) that fit the plume height. Eruption rates were also estimated from the empirical formula <i>M</i><sub>empir</sub> = 140<i>H</i><sub>obs</sub><i><sup>4.14</sup></i> (<i>M</i><sub>empir</sub> is in kilogram per second, <i>H</i><sub>obs</sub> is in kilometer). For these eruptions, the standard error of the residual in log space is about 0.53 for <i>M</i><sub>avg</sub> and 0.50 for <i>M</i><sub>empir</sub>. Thus, for this data set, the model is slightly less accurate at predicting <i>M</i><sub>obs</sub> than the empirical curve. The inability of this model to improve eruption rate estimates may lie in the limited accuracy of even well-observed plume heights, inaccurate model formulation, or the fact that most eruptions examined were not highly influenced by wind. For the low, wind-blown plume of 14–18 April 2010 at Eyjafjallajökull, where an accurate plume height time series is available, modeled rates do agree better with <i>M</i><sub>obs</sub> than <i>M</i><sub>empir</sub>.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Geophysical Research D: Atmospheres","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Geophysical Union","doi":"10.1002/2013JD020604","usgsCitation":"Mastin, L.G., 2014, Testing the accuracy of a 1-D volcanic plume model in estimating mass eruption rate: Journal of Geophysical Research D: Atmospheres, v. 119, no. 5, p. 2474-2495, https://doi.org/10.1002/2013JD020604.","productDescription":"22 p.","startPage":"2474","endPage":"2495","numberOfPages":"22","ipdsId":"IP-046214","costCenters":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":473059,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2013jd020604","text":"Publisher Index Page"},{"id":286120,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"119","issue":"5","noUsgsAuthors":false,"publicationDate":"2014-03-07","publicationStatus":"PW","scienceBaseUri":"53517066e4b05569d805a3dd","contributors":{"authors":[{"text":"Mastin, Larry G. 0000-0002-4795-1992 lgmastin@usgs.gov","orcid":"https://orcid.org/0000-0002-4795-1992","contributorId":555,"corporation":false,"usgs":true,"family":"Mastin","given":"Larry","email":"lgmastin@usgs.gov","middleInitial":"G.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":487443,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70188049,"text":"70188049 - 2014 - Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States","interactions":[],"lastModifiedDate":"2017-05-31T16:11:38","indexId":"70188049","displayToPublicDate":"2014-04-10T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States","docAbstract":"<p><span>Accurately quantifying the spatial and temporal variability of net primary production (NPP) for croplands is essential to understand regional cropland carbon dynamics. We compared three NPP estimates for croplands in the Midwestern United States: inventory-based estimates using crop yield data from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS); estimates from the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) NPP product; and estimates from the General Ensemble biogeochemical Modeling System (GEMS) process-based model. The three methods estimated mean NPP in the range of 469–687&nbsp;g&nbsp;C&nbsp;m</span><sup>−2</sup><span>&nbsp;yr</span><sup>−1</sup><span>and total NPP in the range of 318–490&nbsp;Tg&nbsp;C&nbsp;yr</span><sup>−1</sup><span> for croplands in the Midwest in 2007 and 2008. The NPP estimates from crop yield data and the GEMS model showed the mean NPP for croplands was over 650&nbsp;g&nbsp;C&nbsp;m</span><sup>−2</sup><span>&nbsp;yr</span><sup>−1</sup><span> while the MODIS NPP product estimated the mean NPP was less than 500&nbsp;g&nbsp;C&nbsp;m</span><sup>−2</sup><span>&nbsp;yr</span><sup>−1</sup><span>. MODIS NPP also showed very different spatial variability of the cropland NPP from the other two methods. We found these differences were mainly caused by the difference in the land cover data and the crop specific information used in the methods. Our study demonstrated that the detailed mapping of the temporal and spatial change of crop species is critical for estimating the spatial and temporal variability of cropland NPP. We suggest that high resolution land cover data with species–specific crop information should be used in satellite-based and process-based models to improve carbon estimates for croplands.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2014.01.012","usgsCitation":"Li, Z., Liu, S., Tan, Z., Bliss, N.B., Young, C.J., West, T.O., and Ogle, S.M., 2014, Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States: Ecological Modelling, v. 277, p. 1-12, https://doi.org/10.1016/j.ecolmodel.2014.01.012.","productDescription":"12 p.","startPage":"1","endPage":"12","ipdsId":"IP-053484","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":341842,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, 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sliu@usgs.gov","orcid":"https://orcid.org/0000-0002-6027-3479","contributorId":147403,"corporation":false,"usgs":true,"family":"Liu","given":"Shuguang","email":"sliu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696321,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tan, Zhengxi 0000-0002-4136-0921 ztan@usgs.gov","orcid":"https://orcid.org/0000-0002-4136-0921","contributorId":2945,"corporation":false,"usgs":true,"family":"Tan","given":"Zhengxi","email":"ztan@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696357,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bliss, Norman B. 0000-0003-2409-5211 bliss@usgs.gov","orcid":"https://orcid.org/0000-0003-2409-5211","contributorId":1921,"corporation":false,"usgs":true,"family":"Bliss","given":"Norman","email":"bliss@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":696358,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Young, Claudia J. 0000-0002-0859-7206 cyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-0859-7206","contributorId":2770,"corporation":false,"usgs":true,"family":"Young","given":"Claudia","email":"cyoung@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":696359,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"West, Tristram O.","contributorId":39230,"corporation":false,"usgs":true,"family":"West","given":"Tristram","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":696360,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ogle, Stephen M.","contributorId":187520,"corporation":false,"usgs":false,"family":"Ogle","given":"Stephen","email":"","middleInitial":"M.","affiliations":[{"id":6935,"text":"Natural Resources Ecology Laboratory, Colorado State University","active":true,"usgs":false}],"preferred":false,"id":696361,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70073915,"text":"ds821 - 2014 - Large scale Wyoming transportation data: a resource planning tool","interactions":[],"lastModifiedDate":"2017-12-27T15:01:42","indexId":"ds821","displayToPublicDate":"2014-04-09T13:52:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"821","title":"Large scale Wyoming transportation data: a resource planning tool","docAbstract":"The U.S. Geological Survey Fort Collins Science Center created statewide roads data for the Bureau of Land Management Wyoming State Office using 2009 aerial photography from the National Agriculture Imagery Program. The updated roads data resolves known concerns of omission, commission, and inconsistent representation of map scale, attribution, and ground reference dates which were present in the original source data. To ensure a systematic and repeatable approach of capturing roads on the landscape using on-screen digitizing from true color National Agriculture Imagery Program imagery, we developed a photogrammetry key and quality assurance/quality control protocols. Therefore, the updated statewide roads data will support the Bureau of Land Management’s resource management requirements with a standardized map product representing 2009 ground conditions. The updated Geographic Information System roads data set product, represented at 1:4,000 and +/- 10 meters spatial accuracy, contains 425,275 kilometers within eight attribute classes. The quality control of these products indicated a 97.7 percent accuracy of aspatial information and 98.0 percent accuracy of spatial locations. Approximately 48 percent of the updated roads data was corrected for spatial errors of greater than 1 meter relative to the pre-existing road data. Twenty-six percent of the updated roads involved correcting spatial errors of greater than 5 meters and 17 percent of the updated roads involved correcting spatial errors of greater than 9 meters. The Bureau of Land Management, other land managers, and researchers can use these new statewide roads data set products to support important studies and management decisions regarding land use changes, transportation and planning needs, transportation safety, wildlife applications, and other studies.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds821","issn":"2327-638X","collaboration":"Prepared in cooperation with Resource Ecology Laboratory, Colorado State University","usgsCitation":"O'Donnell, M., Fancher, T., Freeman, A.T., Ziegler, A.E., Bowen, Z.H., and Aldridge, C.L., 2014, Large scale Wyoming transportation data: a resource planning tool: U.S. Geological Survey Data Series 821, Report: v, 21 p.; Downloads directory, https://doi.org/10.3133/ds821.","productDescription":"Report: v, 21 p.; Downloads directory","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-049829","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":286026,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds821.jpg"},{"id":286024,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/821/pdf/ds821.pdf"},{"id":286023,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/821/"},{"id":286025,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/821/downloads/"}],"country":"United States","state":"Wyoming","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -111.0,41.0 ], [ -111.0,45.0 ], [ -104.0,45.0 ], [ -104.0,41.0 ], [ -111.0,41.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53517051e4b05569d805a301","contributors":{"authors":[{"text":"O'Donnell, Michael S.","contributorId":40667,"corporation":false,"usgs":true,"family":"O'Donnell","given":"Michael S.","affiliations":[],"preferred":false,"id":489206,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fancher, Tammy S.","contributorId":17689,"corporation":false,"usgs":true,"family":"Fancher","given":"Tammy S.","affiliations":[],"preferred":false,"id":489205,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":489203,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ziegler, Abra E. aeziegler@usgs.gov","contributorId":5294,"corporation":false,"usgs":true,"family":"Ziegler","given":"Abra","email":"aeziegler@usgs.gov","middleInitial":"E.","affiliations":[],"preferred":true,"id":489204,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bowen, Zachary H. 0000-0002-8656-1831 bowenz@usgs.gov","orcid":"https://orcid.org/0000-0002-8656-1831","contributorId":821,"corporation":false,"usgs":true,"family":"Bowen","given":"Zachary","email":"bowenz@usgs.gov","middleInitial":"H.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":489202,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":489207,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70101080,"text":"70101080 - 2014 - From headwaters to coast: Influence of human activities on water quality of the Potomac River Estuary","interactions":[],"lastModifiedDate":"2019-12-02T07:05:42","indexId":"70101080","displayToPublicDate":"2014-04-09T13:26:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":866,"text":"Aquatic Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"From headwaters to coast: Influence of human activities on water quality of the Potomac River Estuary","docAbstract":"The natural aging process of Chesapeake Bay and its tributary estuaries has been accelerated by human activities around the shoreline and within the watershed, increasing sediment and nutrient loads delivered to the bay. Riverine nutrients cause algal growth in the bay leading to reductions in light penetration with consequent declines in sea grass growth, smothering of bottom-dwelling organisms, and decreases in bottom-water dissolved oxygen as algal blooms decay. Historically, bay waters were filtered by oysters, but declines in oyster populations from overfishing and disease have led to higher concentrations of fine-sediment particles and phytoplankton in the water column. Assessments of water and biological resource quality in Chesapeake Bay and tributaries, such as the Potomac River, show a continual degraded state. In this paper, we pay tribute to Owen Bricker’s comprehensive, holistic scientific perspective using an approach that examines the connection between watershed and estuary. We evaluated nitrogen inputs from Potomac River headwaters, nutrient-related conditions within the estuary, and considered the use of shellfish aquaculture as an in-the-water nutrient management measure. Data from headwaters, nontidal, and estuarine portions of the Potomac River watershed and estuary were analyzed to examine the contribution from different parts of the watershed to total nitrogen loads to the estuary. An eutrophication model was applied to these data to evaluate eutrophication status and changes since the early 1990s and for comparison to regional and national conditions. A farm-scale aquaculture model was applied and results scaled to the estuary to determine the potential for shellfish (oyster) aquaculture to mediate eutrophication impacts. Results showed that (1) the contribution to nitrogen loads from headwater streams is small (about 2 %) of total inputs to the Potomac River Estuary; (2) eutrophic conditions in the Potomac River Estuary have improved in the upper estuary since the early 1990s, but have worsened in the lower estuary. The overall system-wide eutrophication impact is high, despite a decrease in nitrogen loads from the upper basin and declining surface water nitrate nitrogen concentrations over that period; (3) eutrophic conditions in the Potomac River Estuary are representative of Chesapeake Bay region and other US estuaries; moderate to high levels of nutrient-related degradation occur in about 65 % of US estuaries, particularly river-dominated low-flow systems such as the Potomac River Estuary; and (4) shellfish (oyster) aquaculture could remove eutrophication impacts directly from the estuary through harvest but should be considered a complement—not a substitute—for land-based measures. The total nitrogen load could be removed if 40 % of the Potomac River Estuary bottom was in shellfish cultivation; a combination of aquaculture and restoration of oyster reefs may provide larger benefits.","language":"English","publisher":"Springer","doi":"10.1007/s10498-014-9226-y","issn":"13806165","usgsCitation":"Bricker, S.B., Rice, K.C., and Bricker, O.P., 2014, From headwaters to coast: Influence of human activities on water quality of the Potomac River Estuary: Aquatic Geochemistry, v. 20, no. 2, p. 291-323, https://doi.org/10.1007/s10498-014-9226-y.","productDescription":"33 p.","startPage":"291","endPage":"323","ipdsId":"IP-046228","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":286015,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":333173,"rank":2,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/70115891","text":"Response to comment"}],"country":"United States","state":"Maryland, Pennsylvania, Virginia, West Virginia","otherGeospatial":"Potomac River Estuary","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -80.4772,37.8139 ], [ -80.4772,40.788 ], [ -75.9119,40.788 ], [ -75.9119,37.8139 ], [ -80.4772,37.8139 ] ] ] } } ] }","volume":"20","issue":"2","noUsgsAuthors":false,"publicationDate":"2014-02-26","publicationStatus":"PW","scienceBaseUri":"5351703de4b05569d805a20c","contributors":{"authors":[{"text":"Bricker, Suzanne B.","contributorId":64555,"corporation":false,"usgs":false,"family":"Bricker","given":"Suzanne","email":"","middleInitial":"B.","affiliations":[{"id":12448,"text":"U.S. National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":492591,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rice, Karen C. 0000-0002-9356-5443 kcrice@usgs.gov","orcid":"https://orcid.org/0000-0002-9356-5443","contributorId":1998,"corporation":false,"usgs":true,"family":"Rice","given":"Karen","email":"kcrice@usgs.gov","middleInitial":"C.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":false,"id":492589,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bricker, Owen P. III","contributorId":34432,"corporation":false,"usgs":true,"family":"Bricker","given":"Owen","suffix":"III","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":492590,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70099978,"text":"fs20143024 - 2014 - Groundwater studies: principal aquifer surveys","interactions":[],"lastModifiedDate":"2017-01-23T09:59:01","indexId":"fs20143024","displayToPublicDate":"2014-04-09T13:24:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-3024","title":"Groundwater studies: principal aquifer surveys","docAbstract":"<p>In 1991, the U.S. Congress established the National Water-Quality Assessment (NAWQA) program within the U.S. Geological Survey (USGS) to develop nationally consistent long-term datasets and provide information about the quality of the Nation’s streams and groundwater. The USGS uses objective and reliable data, water-quality models, and systematic scientific studies to assess current water-quality conditions, to identify changes in water quality over time, and to determine how natural factors and human activities affect the quality of streams and groundwater. NAWQA is the only non-regulatory Federal program to perform these types of studies; participation is voluntary.</p>\n\n<br>\n\n<p>In the third decade (Cycle 3) of the NAWQA program (2013–2023), the USGS will evaluate the quality and availability of groundwater for drinking supply, improve our understanding of where and why water quality is degraded, and assess how groundwater quality could respond to changes in climate and land use. These goals will be addressed through the implementation of a new monitoring component in Cycle 3: Principal Aquifer Surveys.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20143024","collaboration":"National Water-Quality Assessment (NAWQA) Program","usgsCitation":"Burow, K.R., and Belitz, K., 2014, Groundwater studies: principal aquifer surveys: U.S. Geological Survey Fact Sheet 2014-3024, 2 p., https://doi.org/10.3133/fs20143024.","productDescription":"2 p.","numberOfPages":"2","ipdsId":"IP-049808","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":286011,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs20143024.jpg"},{"id":286008,"type":{"id":15,"text":"Index 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,{"id":70098028,"text":"fs20143021 - 2014 - Decision support system development at the Upper Midwest Environmental Sciences Center","interactions":[],"lastModifiedDate":"2023-01-20T16:11:10.614395","indexId":"fs20143021","displayToPublicDate":"2014-04-09T10:13:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-3021","title":"Decision support system development at the Upper Midwest Environmental Sciences Center","docAbstract":"A Decision Support System (DSS) can be defined in many ways. The working definition used by the U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) is, “A spatially based computer application or data that assists a researcher or manager in making decisions.” This is quite a broad definition—and it needs to be, because the possibilities for types of DSSs are limited only by the user group and the developer’s imagination. There is no one DSS; the types of DSSs are as diverse as the problems they help solve. This diversity requires that DSSs be built in a variety of ways, using the most appropriate methods and tools for the individual application. The skills of potential DSS users vary widely as well, further necessitating multiple approaches to DSS development. Some small, highly trained user groups may want a powerful modeling tool with extensive functionality at the expense of ease of use. Other user groups less familiar with geographic information system (GIS) and spatial data may want an easy-to-use application for a nontechnical audience. UMESC has been developing DSSs for almost 20 years. Our DSS developers offer our partners a wide variety of technical skills and development options, ranging from the most simple Web page or small application to complex modeling application development.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20143021","usgsCitation":"Fox, T.J., Nelson, J., and Rohweder, J., 2014, Decision support system development at the Upper Midwest Environmental Sciences Center: U.S. Geological Survey Fact Sheet 2014-3021, 2 p., https://doi.org/10.3133/fs20143021.","productDescription":"2 p.","ipdsId":"IP-049834","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":285944,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs20143021.jpg"},{"id":285941,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2014/3021/"},{"id":285940,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2014/3021/pdf/fs2014-3021.pdf"}],"country":"United States","otherGeospatial":"Upper Midwest","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -91.27417,43.746041 ], [ -91.27417,43.898447 ], [ -91.157089,43.898447 ], [ -91.157089,43.746041 ], [ -91.27417,43.746041 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53517032e4b05569d805a1b1","contributors":{"authors":[{"text":"Fox, Timothy J. 0000-0002-6167-3001 tfox@usgs.gov","orcid":"https://orcid.org/0000-0002-6167-3001","contributorId":1701,"corporation":false,"usgs":true,"family":"Fox","given":"Timothy","email":"tfox@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":491543,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nelson, J. C. 0000-0002-7105-0107 jcnelson@usgs.gov","orcid":"https://orcid.org/0000-0002-7105-0107","contributorId":459,"corporation":false,"usgs":true,"family":"Nelson","given":"J. C.","email":"jcnelson@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":false,"id":491542,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rohweder, Jason J.","contributorId":25629,"corporation":false,"usgs":true,"family":"Rohweder","given":"Jason J.","affiliations":[],"preferred":false,"id":491544,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70101379,"text":"70101379 - 2014 - Water use characteristics of black mangrove (Avicennia germinans) communities along an ecotone with marsh at a northern geographical limit","interactions":[],"lastModifiedDate":"2014-04-11T10:17:26","indexId":"70101379","displayToPublicDate":"2014-04-09T10:03:07","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Water use characteristics of black mangrove (Avicennia germinans) communities along an ecotone with marsh at a northern geographical limit","docAbstract":"Mangroves are expanding into warm temperate-zone salt marsh communities in several locations globally. Although scientists have discovered that expansion might have modest effects on ecosystem functioning, water use characteristics have not been assessed relative to this transition. We measured early growing season sapflow (J<sub>s</sub>) and leaf transpiration (T<sub>r</sub>) in Avicennia germinans at a latitudinal limit along the northern Gulf of Mexico (Louisiana, United States) under both flooded and drained states and used these data to scale vegetation water use responses in comparison with Spartina alterniflora. We discovered strong convergence when using either J<sub>s</sub> or T<sub>r</sub> for determining individual tree water use, indicating tight connection between transpiration and xylem water movement in small Avicennia trees. When T<sub>r</sub> data were combined with leaf area indices for the region with the use of three separate approaches, we determined that Avicennia stands use approximately 1·0–1·3 mm d<sup>–1</sup> less water than Spartina marsh. Differences were only significant with the use of two of the three approaches, but are suggestive of net conservation of water as Avicennia expands into Spartina marshes at this location. Average J<sub>s</sub> for Avicennia trees was not influenced by flooding, but maximum J<sub>s</sub> was greater when sites were flooded. Avicennia and Spartina closest to open water (shoreline) used more water than interior locations of the same assemblages by an average of 1·3 mm d<sup>−1</sup>. Lower water use by Avicennia may indicate a greater overall resilience to drought relative to Spartina, such that aperiodic drought may interact with warmer winter temperatures to facilitate expansion of Avicennia in some years.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecohydrology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley Online Library","doi":"10.1002/eco.1353","usgsCitation":"Krauss, K.W., McKee, K.L., and Hester, M.W., 2014, Water use characteristics of black mangrove (Avicennia germinans) communities along an ecotone with marsh at a northern geographical limit: Ecohydrology, v. 7, no. 2, p. 354-365, https://doi.org/10.1002/eco.1353.","startPage":"354","endPage":"365","ipdsId":"IP-038229","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"links":[{"id":286249,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":286246,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/eco.1353"}],"country":"United States","state":"Louisiana","otherGeospatial":"Gulf Of Mexico","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -91,28.5 ], [ -91,8.333333333333334E-4 ], [ -89,8.333333333333334E-4 ], [ -89,28.5 ], [ -91,28.5 ] ] ] } } ] }","volume":"7","issue":"2","edition":"12 p.","noUsgsAuthors":false,"publicationDate":"2012-12-05","publicationStatus":"PW","scienceBaseUri":"5351706ee4b05569d805a44a","contributors":{"authors":[{"text":"Krauss, Ken W. 0000-0003-2195-0729 kraussk@usgs.gov","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":2017,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","email":"kraussk@usgs.gov","middleInitial":"W.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":492679,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKee, Karen L. 0000-0001-7042-670X","orcid":"https://orcid.org/0000-0001-7042-670X","contributorId":8927,"corporation":false,"usgs":true,"family":"McKee","given":"Karen","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":492680,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hester, Mark W.","contributorId":9566,"corporation":false,"usgs":true,"family":"Hester","given":"Mark","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":492681,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70100875,"text":"ofr20141073 - 2014 - Laharz_py: GIS tools for automated mapping of lahar inundation hazard zones","interactions":[],"lastModifiedDate":"2014-04-09T10:25:22","indexId":"ofr20141073","displayToPublicDate":"2014-04-09T09:25:00","publicationYear":"2014","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":"2014-1073","title":"Laharz_py: GIS tools for automated mapping of lahar inundation hazard zones","docAbstract":"Laharz_py is written in the Python programming language as a suite of tools for use in ArcMap Geographic Information System (GIS). Primarily, Laharz_py is a computational model that uses statistical descriptions of areas inundated by past mass-flow events to forecast areas likely to be inundated by hypothetical future events. The forecasts use physically motivated and statistically calibrated power-law equations that each has a form A = cV<sup>2/3</sup>, relating mass-flow volume (V) to planimetric or cross-sectional areas (A) inundated by an average flow as it descends a given drainage. Calibration of the equations utilizes logarithmic transformation and linear regression to determine the best-fit values of c. The software uses values of V, an algorithm for idenitifying mass-flow source locations, and digital elevation models of topography to portray forecast hazard zones for lahars, debris flows, or rock avalanches on maps. Laharz_py offers two methods to construct areas of potential inundation for lahars: (1) Selection of a range of plausible V values results in a set of nested hazard zones showing areas likely to be inundated by a range of hypothetical flows; and (2) The user selects a single volume and a confidence interval for the prediction. In either case, Laharz_py calculates the mean expected A and B value from each user-selected value of V. However, for the second case, a single value of V yields two additional results representing the upper and lower values of the confidence interval of prediction. Calculation of these two bounding predictions require the statistically calibrated prediction equations, a user-specified level of confidence, and t-distribution statistics to calculate the standard error of regression, standard error of the mean, and standard error of prediction. The portrayal of results from these two methods on maps compares the range of inundation areas due to prediction uncertainties with uncertainties in selection of V values. The Open-File Report document contains an explanation of how to install and use the software. The Laharz_py software includes an example data set for Mount Rainier, Washington. The second part of the documentation describes how to use all of the Laharz_py tools in an example dataset at Mount Rainier, Washington.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141073","usgsCitation":"Schilling, S.P., 2014, Laharz_py: GIS tools for automated mapping of lahar inundation hazard zones: U.S. Geological Survey Open-File Report 2014-1073, Report: iv, 78 p.; Laharz_py example ZIP, https://doi.org/10.3133/ofr20141073.","productDescription":"Report: iv, 78 p.; Laharz_py example ZIP","numberOfPages":"82","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-043956","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":285932,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20141073.PNG"},{"id":285930,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1073/pdf/ofr2014-1073.pdf"},{"id":285912,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2014/1073/"},{"id":285931,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2014/1073/downloads/laharz_py_example.zip"}],"country":"United States","state":"Washington","otherGeospatial":"Mount St. Helens","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.258,46.160 ], [ -122.258,46.222 ], [ -122.130,46.222 ], [ -122.130,46.160 ], [ -122.258,46.160 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53517051e4b05569d805a2f8","contributors":{"authors":[{"text":"Schilling, Steve P. sschilli@usgs.gov","contributorId":634,"corporation":false,"usgs":true,"family":"Schilling","given":"Steve","email":"sschilli@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":492440,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70099612,"text":"70099612 - 2014 - Adverse moisture events predict seasonal abundance of Lyme disease vector ticks (Ixodes scapularis)","interactions":[],"lastModifiedDate":"2017-06-14T14:37:02","indexId":"70099612","displayToPublicDate":"2014-04-08T10:04:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3010,"text":"Parasites & Vectors","printIssn":"1756-3305","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Adverse moisture events predict seasonal abundance of Lyme disease vector ticks (<i>Ixodes scapularis</i>)","title":"Adverse moisture events predict seasonal abundance of Lyme disease vector ticks (Ixodes scapularis)","docAbstract":"<p><strong>Background</strong>: Lyme borreliosis (LB) is the most commonly reported vector-borne disease in north temperate regions worldwide, affecting an estimated 300,000 people annually in the United States alone. The incidence of LB is correlated with human exposure to its vector, the blacklegged tick (<i>Ixodes scapularis</i>). To date, attempts to model tick encounter risk based on environmental parameters have been equivocal. Previous studies have not considered (1) the differences between relative humidity (RH) in leaf litter and at weather stations, (2) the RH threshold that affects nymphal blacklegged tick survival, and (3) the time required below the threshold to induce mortality. We clarify the association between environmental moisture and tick survival by presenting a significant relationship between the total number of tick adverse moisture events (TAMEs - calculated as microclimatic periods below a RH threshold) and tick abundance each year.</p><p><strong>Methods</strong>: We used a 14-year continuous statewide tick surveillance database and corresponding weather data from Rhode Island (RI), USA, to assess the effects of TAMEs on nymphal populations of <i>I. scapularis</i>. These TAMEs were defined as extended periods of time (&gt;8 h below 82% RH in leaf litter). We fit a sigmoid curve comparing weather station data to those collected by loggers placed in tick habitats to estimate RH experienced by nymphal ticks, and compiled the number of historical TAMEs during the 14-year record.</p><p><strong>Results</strong>: The total number of TAMEs in June of each year was negatively related to total seasonal nymphal tick densities, suggesting that sub-threshold humidity episodes &gt;8 h in duration naturally lowered nymphal blacklegged tick abundance. Furthermore, TAMEs were positively related to the ratio of tick abundance early in the season when compared to late season, suggesting that lower than average tick abundance for a given year resulted from tick mortality and not from other factors.</p><p><strong>Conclusions</strong>: Our results clarify the mechanism by which environmental moisture affects blacklegged tick populations, and offers the possibility to more accurately predict tick abundance and human LB incidence. We describe a method to forecast LB risk in endemic regions and identify the predictive role of microclimatic moisture conditions on tick encounter risk.</p>","language":"English","publisher":"BioMed Central","doi":"10.1186/1756-3305-7-181","usgsCitation":"Berger, K.A., Ginsberg, H.S., Dugas, K.D., Hamel, L.H., and Mather, T., 2014, Adverse moisture events predict seasonal abundance of Lyme disease vector ticks (Ixodes scapularis): Parasites & Vectors, v. 7, no. 181, 8 p., https://doi.org/10.1186/1756-3305-7-181.","productDescription":"8 p.","ipdsId":"IP-055702","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":473063,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/1756-3305-7-181","text":"Publisher Index Page"},{"id":288056,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288055,"rank":1,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1186/1756-3305-7-181"}],"country":"United States","state":"Rhode Island","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -71.8923,41.1467 ], [ -71.8923,42.0188 ], [ -71.1205,42.0188 ], [ -71.1205,41.1467 ], [ -71.8923,41.1467 ] ] ] } } ] }","volume":"7","issue":"181","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53903fe1e4b04eea98bf84df","contributors":{"authors":[{"text":"Berger, Kathryn A.","contributorId":22693,"corporation":false,"usgs":true,"family":"Berger","given":"Kathryn","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":491984,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ginsberg, Howard S. 0000-0002-4933-2466 hginsberg@usgs.gov","orcid":"https://orcid.org/0000-0002-4933-2466","contributorId":3204,"corporation":false,"usgs":true,"family":"Ginsberg","given":"Howard","email":"hginsberg@usgs.gov","middleInitial":"S.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":491983,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dugas, Katherine D.","contributorId":46878,"corporation":false,"usgs":true,"family":"Dugas","given":"Katherine","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":491986,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hamel, Lutz H.","contributorId":41747,"corporation":false,"usgs":true,"family":"Hamel","given":"Lutz","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":491985,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mather, Thomas N.","contributorId":67419,"corporation":false,"usgs":true,"family":"Mather","given":"Thomas N.","affiliations":[],"preferred":false,"id":491987,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70099600,"text":"sir20145037 - 2014 - Statistics for stochastic modeling of volume reduction, hydrograph extension, and water-quality treatment by structural stormwater runoff best management practices (BMPs)","interactions":[],"lastModifiedDate":"2014-04-07T14:30:37","indexId":"sir20145037","displayToPublicDate":"2014-04-07T14:25:00","publicationYear":"2014","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":"2014-5037","title":"Statistics for stochastic modeling of volume reduction, hydrograph extension, and water-quality treatment by structural stormwater runoff best management practices (BMPs)","docAbstract":"<p>The U.S. Geological Survey (USGS) developed the Stochastic Empirical Loading and Dilution Model (SELDM) in cooperation with the Federal Highway Administration (FHWA) to indicate the risk for stormwater concentrations, flows, and loads to be above user-selected water-quality goals and the potential effectiveness of mitigation measures to reduce such risks. SELDM models the potential effect of mitigation measures by using Monte Carlo methods with statistics that approximate the net effects of structural and nonstructural best management practices (BMPs). In this report, structural BMPs are defined as the components of the drainage pathway between the source of runoff and a stormwater discharge location that affect the volume, timing, or quality of runoff. SELDM uses a simple stochastic statistical model of BMP performance to develop planning-level estimates of runoff-event characteristics. This statistical approach can be used to represent a single BMP or an assemblage of BMPs. The SELDM BMP-treatment module has provisions for stochastic modeling of three stormwater treatments: volume reduction, hydrograph extension, and water-quality treatment. In SELDM, these three treatment variables are modeled by using the trapezoidal distribution and the rank correlation with the associated highway-runoff variables. This report describes methods for calculating the trapezoidal-distribution statistics and rank correlation coefficients for stochastic modeling of volume reduction, hydrograph extension, and water-quality treatment by structural stormwater BMPs and provides the calculated values for these variables. This report also provides robust methods for estimating the minimum irreducible concentration (MIC), which is the lowest expected effluent concentration from a particular BMP site or a class of BMPs. These statistics are different from the statistics commonly used to characterize or compare BMPs. They are designed to provide a stochastic transfer function to approximate the quantity, duration, and quality of BMP effluent given the associated inflow values for a population of storm events. A database application and several spreadsheet tools are included in the digital media accompanying this report for further documentation of methods and for future use.</p>\n<br>\n<p>In this study, analyses were done with data extracted from a modified copy of the January 2012 version of International Stormwater Best Management Practices Database, designated herein as the January 2012a version. Statistics for volume reduction, hydrograph extension, and water-quality treatment were developed with selected data. Sufficient data were available to estimate statistics for 5 to 10 BMP categories by using data from 40 to more than 165 monitoring sites. Water-quality treatment statistics were developed for 13 runoff-quality constituents commonly measured in highway and urban runoff studies including turbidity, sediment and solids; nutrients; total metals; organic carbon; and fecal coliforms. The medians of the best-fit statistics for each category were selected to construct generalized cumulative distribution functions for the three treatment variables. For volume reduction and hydrograph extension, interpretation of available data indicates that selection of a Spearman’s rho value that is the average of the median and maximum values for the BMP category may help generate realistic simulation results in SELDM. The median rho value may be selected to help generate realistic simulation results for water-quality treatment variables.</p>\n<br>\n<p>MIC statistics were developed for 12 runoff-quality constituents commonly measured in highway and urban runoff studies by using data from 11 BMP categories and more than 167 monitoring sites. Four statistical techniques were applied for estimating MIC values with monitoring data from each site. These techniques produce a range of lower-bound estimates for each site. Four MIC estimators are proposed as alternatives for selecting a value from among the estimates from multiple sites. Correlation analysis indicates that the MIC estimates from multiple sites were weakly correlated with the geometric mean of inflow values, which indicates that there may be a qualitative or semiquantitative link between the inflow quality and the MIC. Correlations probably are weak because the MIC is influenced by the inflow water quality and the capability of each individual BMP site to reduce inflow concentrations.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145037","issn":"2328-0328","collaboration":"Prepared in cooperation with the U.S. Department of Transportation Federal Highway Administration Office of Project Development and Environmental Review","usgsCitation":"Granato, G., 2014, Statistics for stochastic modeling of volume reduction, hydrograph extension, and water-quality treatment by structural stormwater runoff best management practices (BMPs): U.S. Geological Survey Scientific Investigations Report 2014-5037, Report: vii, 37 p.; Digital media, https://doi.org/10.3133/sir20145037.","productDescription":"Report: vii, 37 p.; Digital media","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-053232","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":285854,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145037.jpg"},{"id":285853,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2014/5037/sir2014-5037.zip"},{"id":285851,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5037/pdf/sir2014-5037.pdf"},{"id":284444,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5037/"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53517065e4b05569d805a3cf","contributors":{"authors":[{"text":"Granato, Gregory E. 0000-0002-2561-9913 ggranato@usgs.gov","orcid":"https://orcid.org/0000-0002-2561-9913","contributorId":1692,"corporation":false,"usgs":true,"family":"Granato","given":"Gregory E.","email":"ggranato@usgs.gov","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":false,"id":491974,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70095800,"text":"ofr20141050 - 2014 - Projecting climate effects on birds and reptiles of the Southwestern United States","interactions":[],"lastModifiedDate":"2017-11-25T13:45:42","indexId":"ofr20141050","displayToPublicDate":"2014-04-07T09:06:00","publicationYear":"2014","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":"2014-1050","title":"Projecting climate effects on birds and reptiles of the Southwestern United States","docAbstract":"<p>We modeled the current and future breeding ranges of seven bird and five reptile species in the Southwestern United States with sets of landscape, biotic (plant), and climatic global circulation model (GCM) variables.</p>\n<br>\n<p>For modeling purposes, we used PRISM data to characterize the climate of the Western United States between 1980 and 2009 (baseline for birds) and between 1940 and 2009 (baseline for reptiles). In contrast, we used a pre-selected set of GCMs that are known to be good predictors of southwestern climate (five individual and one ensemble GCM), for the A1B emission scenario, to characterize future climatic conditions in three time periods (2010–39; 2040–69; and, 2070–99).</p>\n<br>\n<p>Our modeling approach relied on conceptual models for each target species to inform selection of candidate explanatory variables and to interpret the ecological meaning of developed probabilistic distribution models. We employed logistic regression and maximum entropy modeling techniques to create a set of probabilistic models for each target species.</p>\n<br>\n<p>We considered climatic, landscape, and plant variables when developing and testing our probabilistic models. Climatic variables included the maximum and minimum mean monthly and seasonal temperature and precipitation for three time periods. Landscape features included terrain ruggedness and insolation. We also considered plant species distributions as candidate explanatory variables where prior ecological knowledge implicated a strong association between a plant and animal species.</p>\n<br>\n<p>Projected changes in range varied widely among species, from major losses to major gains.</p>\n<br>\n<p>Breeding bird ranges exhibited greater expansions and contractions than did reptile species.</p>\n<br>\n<p>We project range losses for Williamson’s sapsucker and pygmy nuthatch of a magnitude that could move these two species close to extinction within the next century. Although both species currently have a relatively limited distribution, they can be locally common, and neither are presently considered candidates for prospective endangerment.</p>\n<br>\n<p>We project range losses of over 40 percent, from its current extent of occurrence, for the plateau striped whiptail, Arizona black rattlesnake, and common lesser earless lizard. Currently, these reptile species are thought to be common or at least locally abundant throughout their ranges.</p>\n<br>\n<p>The total contribution of plants in each distribution model was very small, but models that contained at least one plant always outperformed models with only physical variables (climatic or landscape). The magnitude of change in projected range increased further into the future, especially when a plant was in the model.</p>\n<br>\n<p>Among bird species, those that had the strongest association with a landscape feature during the breeding season, such as terrain ruggedness and insolation, exhibited the smallest contractions in projected breeding range in the future. In contrast, bird species that had weak associations with landscape features, but strong climatic associations, suffered the greatest breeding range contractions. Thus, landscape effects appeared to buffer some of the negative effects of climate change for some species.</p>\n<br>\n<p>Among bird species, magnitude of change in projected breeding range was positively related to the annual average temperature of their baseline distribution, thus species with the warmest breeding ranges exhibited the greatest changes in future breeding ranges. This pattern was not evident for reptiles, but might exist if additional species were included in the model.</p>\n<br>\n<p>Our results provide managers with a series of projected range maps that will enable scientists, concerned citizens, and wildlife managers to identify what the potential effects of climate change will be on bird and reptile distributions in the Western United States. We hope that our results can be used in proactive ways to mitigate some of the potential effects of climate change on selected species.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141050","issn":"2331-1258","usgsCitation":"van Riper, C., Hatten, J.R., Giermakowski, J.T., Mattson, D., Holmes, J., Johnson, M.J., Nowak, E., Ironside, K., Peters, M., Heinrich, P., Cole, K., Truettner, C., and Schwalbe, C.R., 2014, Projecting climate effects on birds and reptiles of the Southwestern United States: U.S. Geological Survey Open-File Report 2014-1050, x, 100 p., https://doi.org/10.3133/ofr20141050.","productDescription":"x, 100 p.","numberOfPages":"112","onlineOnly":"Y","ipdsId":"IP-040401","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":285758,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20141050.jpg"},{"id":285757,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1050/pdf/ofr2014-1050.pdf"},{"id":285756,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2014/1050/"}],"country":"United States","otherGeospatial":"Colorado Plateau;Sonoran Desert","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.88,29.35 ], [ -124.88,49.0 ], [ -102.04,49.0 ], [ -102.04,29.35 ], [ -124.88,29.35 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5351705ce4b05569d805a37b","contributors":{"authors":[{"text":"van Riper, Charles III 0000-0003-1084-5843 charles_van_riper@usgs.gov","orcid":"https://orcid.org/0000-0003-1084-5843","contributorId":169488,"corporation":false,"usgs":true,"family":"van Riper","given":"Charles","suffix":"III","email":"charles_van_riper@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":491454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatten, James R. 0000-0003-4676-8093 jhatten@usgs.gov","orcid":"https://orcid.org/0000-0003-4676-8093","contributorId":3431,"corporation":false,"usgs":true,"family":"Hatten","given":"James","email":"jhatten@usgs.gov","middleInitial":"R.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":491446,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Giermakowski, J. Tomasz","contributorId":98630,"corporation":false,"usgs":true,"family":"Giermakowski","given":"J.","email":"","middleInitial":"Tomasz","affiliations":[],"preferred":false,"id":491457,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mattson, David","contributorId":75047,"corporation":false,"usgs":true,"family":"Mattson","given":"David","affiliations":[],"preferred":false,"id":491453,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Holmes, Jennifer A.","contributorId":86437,"corporation":false,"usgs":true,"family":"Holmes","given":"Jennifer A.","affiliations":[],"preferred":false,"id":491455,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Matthew J. mjjohnson@usgs.gov","contributorId":3604,"corporation":false,"usgs":true,"family":"Johnson","given":"Matthew","email":"mjjohnson@usgs.gov","middleInitial":"J.","affiliations":[{"id":27989,"text":"Colorado Plateau Research Station, Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":491447,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nowak, Erika M.","contributorId":14062,"corporation":false,"usgs":true,"family":"Nowak","given":"Erika M.","affiliations":[],"preferred":false,"id":491449,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ironside, Kirsten","contributorId":19808,"corporation":false,"usgs":true,"family":"Ironside","given":"Kirsten","affiliations":[],"preferred":false,"id":491450,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Peters, Michael","contributorId":35643,"corporation":false,"usgs":true,"family":"Peters","given":"Michael","affiliations":[],"preferred":false,"id":491451,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Heinrich, Paul","contributorId":63308,"corporation":false,"usgs":true,"family":"Heinrich","given":"Paul","email":"","affiliations":[],"preferred":false,"id":491452,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Cole, K.L.","contributorId":87507,"corporation":false,"usgs":true,"family":"Cole","given":"K.L.","email":"","affiliations":[],"preferred":false,"id":491456,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Truettner, C.","contributorId":7615,"corporation":false,"usgs":true,"family":"Truettner","given":"C.","affiliations":[],"preferred":false,"id":491448,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Schwalbe, Cecil R. cschwalbe@usgs.gov","contributorId":3077,"corporation":false,"usgs":true,"family":"Schwalbe","given":"Cecil","email":"cschwalbe@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":491445,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}}
,{"id":70100765,"text":"70100765 - 2014 - Identifying marine Important Bird Areas using at-sea survey data","interactions":[],"lastModifiedDate":"2014-04-04T15:51:07","indexId":"70100765","displayToPublicDate":"2014-04-04T15:47:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Identifying marine Important Bird Areas using at-sea survey data","docAbstract":"Effective marine bird conservation requires identification of at-sea locations used by populations for foraging, staging, and migration. Using an extensive database of at-sea survey data spanning over 30 years, we developed a standardized and data-driven spatial method for identifying globally significant marine Important Bird Areas in Alaska. To delineate these areas we developed a six-step process: binning data and accounting for unequal survey effort, filtering input data for persistence of species use, using a moving window analysis to produce maps representing a gradient from low to high abundance, drawing core area boundaries around major concentrations based on abundance thresholds, validating the results, and combining overlapping boundaries into important areas for multiple species. We identified 126 bird core areas which were merged into 59 pelagic sites important to 45 out of 57 species assessed. The final areas included approximately 34–38% of all marine birds in Alaska waters, within just 6% of the total area. We identified globally significant Important Bird Areas spanning 20 degrees of latitude and 56 degrees of longitude, in two different oceans, with climates ranging from temperate to polar. Although our maps did suffer from some data gaps, these gaps did not preclude us from identifying sites that incorporated 13% of the assessed continental waterbird population and 9% of the assessed global seabird population. The application of this technique over a large and productive region worked well for a wide range of birds, exhibiting a variety of foraging strategies and occupying a variety of ecosystem types.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Biological Conservation","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2014.02.039","usgsCitation":"Smith, M.A., Walker, N.J., Free, C.M., Kirchhoff, M.J., Drew, G.S., Warnock, N., and Stenhouse, I.J., 2014, Identifying marine Important Bird Areas using at-sea survey data: Biological Conservation, v. 172, p. 180-189, https://doi.org/10.1016/j.biocon.2014.02.039.","productDescription":"10 p.","startPage":"180","endPage":"189","numberOfPages":"10","ipdsId":"IP-051043","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":285755,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":285754,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.biocon.2014.02.039"}],"country":"United States","state":"Alaska","otherGeospatial":"Beaufort Sea;Chukchi Sea;East Bering Sea;Gulf Of Alaska;West Bering Sea","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 130.5,47.9 ], [ 130.5,74.7 ], [ -167.6,74.7 ], [ -167.6,47.9 ], [ 130.5,47.9 ] ] ] } } ] }","volume":"172","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5351704ee4b05569d805a2db","contributors":{"authors":[{"text":"Smith, Melanie A.","contributorId":31305,"corporation":false,"usgs":true,"family":"Smith","given":"Melanie","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":492431,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walker, Nathan J.","contributorId":90210,"corporation":false,"usgs":true,"family":"Walker","given":"Nathan","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":492435,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Free, Christopher M.","contributorId":40895,"corporation":false,"usgs":true,"family":"Free","given":"Christopher","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":492433,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kirchhoff, Matthew J.","contributorId":31306,"corporation":false,"usgs":true,"family":"Kirchhoff","given":"Matthew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":492432,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Drew, Gary S. 0000-0002-6789-0891 gdrew@usgs.gov","orcid":"https://orcid.org/0000-0002-6789-0891","contributorId":3311,"corporation":false,"usgs":true,"family":"Drew","given":"Gary","email":"gdrew@usgs.gov","middleInitial":"S.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":492429,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Warnock, Nils","contributorId":64534,"corporation":false,"usgs":false,"family":"Warnock","given":"Nils","email":"","affiliations":[],"preferred":false,"id":492434,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stenhouse, Iain J.","contributorId":23434,"corporation":false,"usgs":true,"family":"Stenhouse","given":"Iain","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":492430,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70100419,"text":"ofr20141069 - 2014 - Post-release behavior and movement patterns of Chinook salmon (<i>Oncorhynchus tshawytscha</i>) and coho salmon (<i>Oncorhynchus kisutch</i>) after capture using alternative commercial fish gear, lower Columbia River, Washington and Oregon, 2013","interactions":[],"lastModifiedDate":"2016-04-26T10:16:48","indexId":"ofr20141069","displayToPublicDate":"2014-04-04T13:16:13","publicationYear":"2014","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":"2014-1069","title":"Post-release behavior and movement patterns of Chinook salmon (<i>Oncorhynchus tshawytscha</i>) and coho salmon (<i>Oncorhynchus kisutch</i>) after capture using alternative commercial fish gear, lower Columbia River, Washington and Oregon, 2013","docAbstract":"<p>Commercial salmon <i>Oncorhynchus </i>spp. fishers traditionally have used gill nets, and more recently tangle nets, to capture adult salmon in the lower Columbia River, Washington and Oregon, but these gear types are not selective and can result in unintentional injury or death to non-target species, which is a problem when wild or Endangered Species Act-listed salmon are present. Gill and tangle nets capture fish through physical retention. Gill nets have mesh sizes that are slightly larger than the diameter of the head of the target species so that a fish moving through the net becomes entangled behind its operculum. Tangle nets have mesh sizes that are smaller than the diameter of the head of the target species so that a fish becomes entangled by its teeth or jaw. The Washington Department of Fish and Wildlife (WDFW) has been evaluating Merwin traps, beach seines, and purse seines during the past decade to determine if these are viable alternative commercial fishing gear types that would reduce negative effects to non-target fish, including wild salmon. As opposed to gill and tangle nets, these alternative gear types capture fish without physical restraint. The nets encircle the area where a fish or school of fish is located and eliminate the ability of those fish to escape. Because fish are not physically restrained by the gear, it is believed that the likelihood of injury and death would be reduced, allowing the safe release of non-target fish.</p>\n<p>In 2011 and 2012, WDFW conducted post-release mortality studies of steelhead (<i>Oncorhynchus mykiss</i>), Chinook salmon (<i>Oncorhynchus tshawytscha</i>)<i>, </i>and coho salmon (<i>Oncorhynchus kisutch</i>) that were captured using beach or purse seines. These studies were comprised of two groups of fish tagged with passive integrated transponder tags (PIT tags): (1) treatment fish that were captured by one of the gear types 9&ndash;25 river kilometers (rkm) downstream of Bonneville Dam (rkm 234); and (2) control fish that were captured at the Adult Fish Facility near the Washington shore fish ladder at Bonneville Dam, and then transported and released 8 rkm downstream of the Bonneville Dam. Fish were confirmed to have survived if they moved upstream and were detected on PIT-tag antennas at or upstream of Bonneville Dam, were recovered at hatcheries or at the dam, or were captured by commercial or sport fishers. Post-release survival estimates were higher for steelhead (89&ndash;98 percent) than for Chinook salmon and coho salmon (50&ndash;90 percent; Washington Department of Fish and Wildlife, unpub. data, 2014). However, some Chinook salmon and coho salmon return to hatcheries, or spawn in the mainstem Columbia River and in tributaries downstream of Bonneville Dam. The proportion of Chinook salmon and coho salmon in the treatment group that were destined for areas downstream of Bonneville Dam likely was higher than in the control group because the control fish were collected as they were attempting to pass the dam. If this assertion was true, mortality would have been overestimated in these studies, so WDFW developed a study plan to determine the post-release movements and intended location of Chinook salmon and coho salmon collected with beach and purse seines in the lower Columbia River.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141069","collaboration":"Prepared in cooperation with the Washington Department of Fish and Wildlife","usgsCitation":"Liedtke, T.L., Kock, T.J., Evans, S.D., Hansen, G.S., and Rondorf, D.W., 2014, Post-release behavior and movement patterns of Chinook salmon (<i>Oncorhynchus tshawytscha</i>) and coho salmon (<i>Oncorhynchus kisutch</i>) after capture using alternative commercial fish gear, lower Columbia River, Washington and Oregon, 2013: U.S. Geological Survey Open-File Report 2014-1069, vi, 36 p., https://doi.org/10.3133/ofr20141069.","productDescription":"vi, 36 p.","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-054420","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":285721,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20141069.jpg"},{"id":285719,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2014/1069/"},{"id":285720,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1069/pdf/ofr2014-1069.pdf","text":"Report","size":"1.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"country":"United States","state":"Oregon, Washington","otherGeospatial":"Lower Columbia River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.4,45.3 ], [ -122.4,0.0011111111111111111 ], [ -121.5,0.0011111111111111111 ], [ -121.5,45.3 ], [ -122.4,45.3 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5351705ae4b05569d805a36a","contributors":{"authors":[{"text":"Liedtke, Theresa L. 0000-0001-6063-9867 tliedtke@usgs.gov","orcid":"https://orcid.org/0000-0001-6063-9867","contributorId":2999,"corporation":false,"usgs":true,"family":"Liedtke","given":"Theresa","email":"tliedtke@usgs.gov","middleInitial":"L.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":492196,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kock, Tobias J. 0000-0001-8976-0230 tkock@usgs.gov","orcid":"https://orcid.org/0000-0001-8976-0230","contributorId":3038,"corporation":false,"usgs":true,"family":"Kock","given":"Tobias","email":"tkock@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":492197,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evans, Scott D. 0000-0003-0452-7726 sdevans@usgs.gov","orcid":"https://orcid.org/0000-0003-0452-7726","contributorId":4408,"corporation":false,"usgs":true,"family":"Evans","given":"Scott","email":"sdevans@usgs.gov","middleInitial":"D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":492199,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hansen, Gabriel S. 0000-0001-6272-3632 ghansen@usgs.gov","orcid":"https://orcid.org/0000-0001-6272-3632","contributorId":3422,"corporation":false,"usgs":true,"family":"Hansen","given":"Gabriel","email":"ghansen@usgs.gov","middleInitial":"S.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":492198,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rondorf, Dennis W. drondorf@usgs.gov","contributorId":2970,"corporation":false,"usgs":true,"family":"Rondorf","given":"Dennis","email":"drondorf@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":492195,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70099604,"text":"sir20145050 - 2014 - Groundwater availability in the Crouch Branch and McQueen Branch aquifers, Chesterfield County, South Carolina, 1900-2012","interactions":[],"lastModifiedDate":"2024-04-10T10:56:07.508306","indexId":"sir20145050","displayToPublicDate":"2014-04-04T12:36:00","publicationYear":"2014","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":"2014-5050","title":"Groundwater availability in the Crouch Branch and McQueen Branch aquifers, Chesterfield County, South Carolina, 1900-2012","docAbstract":"<p>Chesterfield County is located in the northeastern part of South Carolina along the southern border of North Carolina and is primarily underlain by unconsolidated sediments of Late Cretaceous age and younger of the Atlantic Coastal Plain. Approximately 20 percent of Chesterfield County is in the Piedmont Physiographic Province, and this area of the county is not included in this study. These Atlantic Coastal Plain sediments compose two productive aquifers: the Crouch Branch aquifer that is present at land surface across most of the county and the deeper, semi-confined McQueen Branch aquifer. Most of the potable water supplied to residents of Chesterfield County is produced from the Crouch Branch and McQueen Branch aquifers by a well field located near McBee, South Carolina, in the southwestern part of the county. Overall, groundwater availability is good to very good in most of Chesterfield County, especially the area around and to the south of McBee, South Carolina. The eastern part of Chesterfield County does not have as abundant groundwater resources but resources are generally adequate for domestic purposes.</p>\n<br>\n<p>The primary purpose of this study was to determine groundwater-flow rates, flow directions, and changes in water budgets over time for the Crouch Branch and McQueen Branch aquifers in the Chesterfield County area. This goal was accomplished by using the U.S. Geological Survey finite-difference MODFLOW groundwater-flow code to construct and calibrate a groundwater-flow model of the Atlantic Coastal Plain of Chesterfield County. The model was created with a uniform grid size of 300 by 300 feet to facilitate a more accurate simulation of groundwater-surface-water interactions. The model consists of 617 rows from north to south extending about 35 miles and 884 columns from west to east extending about 50 miles, yielding a total area of about 1,750 square miles. However, the active part of the modeled area, or the part where groundwater flow is simulated, totaled about 1,117 square miles.</p>\n<br>\n<p>Major types of data used as input to the model included groundwater levels, groundwater-use data, and hydrostratigraphic data, along with estimates and measurements of stream base flows made specifically for this study. The groundwater-flow model was calibrated to groundwater-level and stream base-flow conditions from 1900 to 2012 using 39 stress periods. The model was calibrated with an automated parameter-estimation approach using the computer program PEST, and the model used regularized inversion and pilot points. The groundwater-flow model was calibrated using field data that included groundwater levels that had been collected between 1940 and 2012 from 239 wells and base-flow measurements from 44 locations distributed within the study area. To better understand recharge and inter-aquifer interactions, seven wells were equipped with continuous groundwater-level recording equipment during the course of the study, between 2008 and 2012. These water levels were included in the model calibration process. The observed groundwater levels were compared to the simulated ones, and acceptable calibration fits were achieved. Root mean square error for the simulated groundwater levels compared to all observed groundwater levels was 9.3 feet for the Crouch Branch aquifer and 8.6 feet for the McQueen Branch aquifer.</p>\n<br>\n<p>The calibrated groundwater-flow model was then used to calculate groundwater budgets for the entire study area and for two sub-areas. The sub-areas are the Alligator Rural Water and Sewer Company well field near McBee, South Carolina, and the Carolina Sandhills National Wildlife Refuge acquisition boundary area. For the overall model area, recharge rates vary from 56 to 1,679 million gallons per day (Mgal/d) with a mean of 737 Mgal/d over the simulation period (1900–2012). The simulated water budget for the streams and rivers varies from 653 to 1,127 Mgal/d with a mean of 944 Mgal/d. The simulated “storage-in term” ranges from 0 to 565 Mgal/d with a mean of 276 Mgal/d. The simulated “storage-out term” has a range of 0 to 552 Mgal/d with a mean of 77 Mgal/d. Groundwater budgets for the McBee, South Carolina, area and the Carolina Sandhills National Wildlife Refuge acquisition area had similar results.</p>\n<br>\n<p>An analysis of the effects of past and current groundwater withdrawals on base flows in the McBee area indicated a negligible effect of pumping from the Alligator Rural Water and Sewer well field on local stream base flows. Simulate base flows for 2012 for selected streams in and around the McBee area were similar with and without simulated groundwater withdrawals from the well field. Removing all pumping from the model for the entire simulation period (1900–2012) produces a negligible difference in increased base flow for the selected streams. The 2012 flow for Lower Alligator Creek was 5.04 Mgal/d with the wells pumping and 5.08 Mgal/d without the wells pumping; this represents the largest difference in simulated flows for the six streams.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145050","issn":"2328-0328","collaboration":"Prepared in cooperation with the South Carolina Department of Natural Resources","usgsCitation":"Campbell, B.G., and Landmeyer, J., 2014, Groundwater availability in the Crouch Branch and McQueen Branch aquifers, Chesterfield County, South Carolina, 1900-2012: U.S. Geological Survey Scientific Investigations Report 2014-5050, Report: viii, 68 p.; 2 Tables, https://doi.org/10.3133/sir20145050.","productDescription":"Report: viii, 68 p.; 2 Tables","numberOfPages":"80","onlineOnly":"Y","temporalStart":"1900-01-01","temporalEnd":"2012-12-31","ipdsId":"IP-052468","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":285712,"rank":5,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145050.jpg"},{"id":285708,"rank":4,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5050/"},{"id":285709,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5050/pdf/sir2014-5050.pdf"},{"id":285710,"rank":2,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2014/5050/tables/sir2014-5050_table2-1-crouchbranch.xlsx"},{"id":285711,"rank":1,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2014/5050/tables/sir2014-5050_table2-2-mcqueenbranch.xlsx"}],"scale":"100000","projection":"North American Datum of 1983","country":"United States","state":"South Carolina","county":"Chesterfield County","otherGeospatial":"Crouch Branch Aquifer, Mcqueen Branch 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Bruce G. 0000-0003-4800-6674 bcampbel@usgs.gov","orcid":"https://orcid.org/0000-0003-4800-6674","contributorId":995,"corporation":false,"usgs":true,"family":"Campbell","given":"Bruce","email":"bcampbel@usgs.gov","middleInitial":"G.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":491975,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Landmeyer, James 0000-0002-5640-3816 jlandmey@usgs.gov","orcid":"https://orcid.org/0000-0002-5640-3816","contributorId":3257,"corporation":false,"usgs":true,"family":"Landmeyer","given":"James","email":"jlandmey@usgs.gov","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":491976,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70099787,"text":"ofr20141064 - 2014 - Noble gas isotopes in mineral springs within the Cascadia Forearc, Washington and Oregon","interactions":[],"lastModifiedDate":"2024-01-29T22:47:49.297952","indexId":"ofr20141064","displayToPublicDate":"2014-04-04T08:03:00","publicationYear":"2014","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":"2014-1064","title":"Noble gas isotopes in mineral springs within the Cascadia Forearc, Washington and Oregon","docAbstract":"This U.S. Geological Survey report presents laboratory analyses along with field notes for a pilot study to document the relative abundance of noble gases in mineral springs within the Cascadia forearc of Washington and Oregon. Estimates of the depth to the underlying Juan de Fuca oceanic plate beneath the sample sites are derived from the McCrory and others (2012) slab model. Some of these springs have been previously sampled for chemical analyses (Mariner and others, 2006), but none currently have publicly available noble gas data. Helium isotope values as well as the noble gas values and ratios presented below will be used to determine the sources and mixing history of these mineral waters.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141064","usgsCitation":"McCrory, P.A., Constantz, J., and Hunt, A.G., 2014, Noble gas isotopes in mineral springs within the Cascadia Forearc, Washington and Oregon: U.S. Geological Survey Open-File Report 2014-1064, Report: iv, 20 p.; Tables 1-8, https://doi.org/10.3133/ofr20141064.","productDescription":"Report: iv, 20 p.; Tables 1-8","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-052802","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":285666,"rank":10,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2014/1064/"},{"id":285676,"rank":11,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20141064.GIF"},{"id":285675,"rank":1,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2014/1064/downloads/ofr2014-1064_Table8_Wilhoit.xlsx"},{"id":285674,"rank":2,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2014/1064/downloads/ofr2014-1064_Table7_Sodaville.xlsx"},{"id":285673,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2014/1064/downloads/ofr2014-1064_Table6_Cascadia.xlsx"},{"id":285669,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2014/1064/downloads/ofr2014-1064_Table2_Olympic.xlsx"},{"id":285672,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2014/1064/downloads/ofr2014-1064_Table5_Boswell.xlsx"},{"id":285671,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2014/1064/downloads/ofr2014-1064_Table4_Pigeon.xlsx"},{"id":285670,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2014/1064/downloads/ofr2014-1064_Table3_JacksonPrairie.xlsx"},{"id":285668,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2014/1064/downloads/ofr2014-1064_Table1_SolDuc.xlsx"},{"id":285667,"rank":9,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1064/pdf/ofr2014-1064.pdf"}],"projection":"Transverse Mercator projection","datum":"World Geodetic System 1984","country":"United States","state":"Oregon;Washington","otherGeospatial":"Cascadia","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -132.0,39.0 ], [ -132.0,52.0 ], [ -120.0,52.0 ], [ -120.0,39.0 ], [ -132.0,39.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53517057e4b05569d805a345","contributors":{"authors":[{"text":"McCrory, Patricia A. 0000-0003-2471-0018 pmccrory@usgs.gov","orcid":"https://orcid.org/0000-0003-2471-0018","contributorId":2728,"corporation":false,"usgs":true,"family":"McCrory","given":"Patricia","email":"pmccrory@usgs.gov","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":492027,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Constantz, James E. 0000-0002-4062-2096 jconstan@usgs.gov","orcid":"https://orcid.org/0000-0002-4062-2096","contributorId":1962,"corporation":false,"usgs":true,"family":"Constantz","given":"James E.","email":"jconstan@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":492026,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hunt, Andrew G. 0000-0002-3810-8610 ahunt@usgs.gov","orcid":"https://orcid.org/0000-0002-3810-8610","contributorId":1582,"corporation":false,"usgs":true,"family":"Hunt","given":"Andrew","email":"ahunt@usgs.gov","middleInitial":"G.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":492025,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70048943,"text":"ds795 - 2014 - Groundwater-quality data in seven GAMA study units: results from initial sampling, 2004-2005, and resampling, 2007-2008, of wells: California GAMA Program Priority Basin Project","interactions":[],"lastModifiedDate":"2018-06-04T14:41:26","indexId":"ds795","displayToPublicDate":"2014-04-03T16:06:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"795","title":"Groundwater-quality data in seven GAMA study units: results from initial sampling, 2004-2005, and resampling, 2007-2008, of wells: California GAMA Program Priority Basin Project","docAbstract":"<p>The Priority Basin Project (PBP) of the Groundwater Ambient Monitoring and Assessment (GAMA) Program was developed in response to the Groundwater Quality Monitoring Act of 2001 and is being conducted by the U.S. Geological Survey (USGS) in cooperation with the California State Water Resources Control Board (SWRCB). The GAMA-PBP began sampling, primarily public supply wells in May 2004. By the end of February 2006, seven (of what would eventually be 35) study units had been sampled over a wide area of the State. Selected wells in these first seven study units were resampled for water quality from August 2007 to November 2008 as part of an assessment of temporal trends in water quality by the GAMA-PBP.</p>\n<br/>\n<p>The initial sampling was designed to provide a spatially unbiased assessment of the quality of raw groundwater used for public water supplies within the seven study units. In the 7 study units, 462 wells were selected by using a spatially distributed, randomized grid-based method to provide statistical representation of the study area. Wells selected this way are referred to as grid wells or status wells. Approximately 3 years after the initial sampling, 55 of these previously sampled status wells (approximately 10 percent in each study unit) were randomly selected for resampling. The seven resampled study units, the total number of status wells sampled for each study unit, and the number of these wells resampled for trends are as follows, in chronological order of sampling: San Diego Drainages (53 status wells, 7 trend wells), North San Francisco Bay (84, 10), Northern San Joaquin Basin (51, 5), Southern Sacramento Valley (67, 7), San Fernando–San Gabriel (35, 6), Monterey Bay and Salinas Valley Basins (91, 11), and Southeast San Joaquin Valley (83, 9).</p>\n<br/>\n<p>The groundwater samples were analyzed for a large number of synthetic organic constituents (volatile organic compounds [VOCs], pesticides, and pesticide degradates), constituents of special interest (perchlorate, N-nitrosodimethylamine [NDMA], and 1,2,3-trichloropropane [1,2,3-TCP]), and naturally-occurring inorganic constituents (nutrients, major and minor ions, and trace elements). Naturally-occurring isotopes (tritium, carbon-14, and stable isotopes of hydrogen and oxygen in water) also were measured to help identify processes affecting groundwater quality and the sources and ages of the sampled groundwater. Nearly 300 constituents and water-quality indicators were investigated.</p>\n<br/>\n<p>Quality-control samples (blanks, replicates, and samples for matrix spikes) were collected at 24 percent of the 55 status wells resampled for trends, and the results for these samples were used to evaluate the quality of the data for the groundwater samples. Field blanks rarely contained detectable concentrations of any constituent, suggesting that contamination was not a noticeable source of bias in the data for the groundwater samples. Differences between replicate samples were mostly within acceptable ranges, indicating acceptably low variability in analytical results. Matrix-spike recoveries were within the acceptable range (70 to 130 percent) for 75 percent of the compounds for which matrix spikes were collected.</p>\n<br/>\n<p>This study did not attempt to evaluate the quality of water delivered to consumers. After withdrawal, groundwater typically is treated, disinfected, and blended with other waters to maintain acceptable water quality. The benchmarks used in this report apply to treated water that is served to the consumer, not to untreated groundwater. To provide some context for the results, however, concentrations of constituents measured in these groundwater samples were compared with benchmarks established by the U.S. Environmental Protection Agency (USEPA) and California Department of Public Health (CDPH). Comparisons between data collected for this study and benchmarks for drinking water are for illustrative purposes only and are not indicative of compliance or non-compliance with those benchmarks.</p>\n<br/>\n<p>Most constituents that were detected in groundwater samples from the trend wells were found at concentrations less than drinking-water benchmarks. Four VOCs—trichloroethene, tetrachloroethene, 1,2-dibromo-3-chloropropane, and methyl tert-butyl ether—were detected in one or more wells at concentrations greater than their health-based benchmarks, and six VOCs were detected in at least 10 percent of the samples during initial sampling or resampling of the trend wells. No pesticides were detected at concentrations near or greater than their health-based benchmarks. Three pesticide constituents—atrazine, deethylatrazine, and simazine—were detected in more than 10 percent of the trend-well samples during both sampling periods. Perchlorate, a constituent of special interest, was detected more frequently, and at greater concentrations during resampling than during initial sampling, but this may be due to a change in analytical method between the sampling periods, rather than to a change in groundwater quality. Another constituent of special interest, 1,2,3-TCP, was also detected more frequently during resampling than during initial sampling, but this pattern also may not reflect a change in groundwater quality. Samples from several of the wells where 1,2,3-TCP was detected by low-concentration-level analysis during resampling were not analyzed for 1,2,3-TCP using a low-level method during initial sampling. Most detections of nutrients and trace elements in samples from trend wells were less than health-based benchmarks during both sampling periods. Exceptions include nitrate, arsenic, boron, and vanadium, all detected at concentrations greater than their health-based benchmarks in at least one well during both sampling periods, and molybdenum, detected at concentrations greater than its health-based benchmark during resampling only. The isotopic ratios of oxygen and hydrogen in water and tritium and carbon-14 activities generally changed little between sampling periods, suggesting that the predominant sources and ages of groundwater in most trend wells were consistent between the sampling periods.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds795","collaboration":"A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program. Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Kent, R.H., Belitz, K., and Fram, M.S., 2014, Groundwater-quality data in seven GAMA study units: results from initial sampling, 2004-2005, and resampling, 2007-2008, of wells: California GAMA Program Priority Basin Project: U.S. Geological Survey Data Series 795, x, 170 p., https://doi.org/10.3133/ds795.","productDescription":"x, 170 p.","numberOfPages":"184","onlineOnly":"Y","ipdsId":"IP-032958","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":285665,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds795.jpg"},{"id":285663,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/795/"},{"id":285664,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/795/pdf/ds795.pdf"}],"projection":"Albers Equal Area Conic Projection","country":"United States","state":"California","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -125.0,32.0 ], [ -125.0,42.2 ], [ -114.0,42.2 ], [ -114.0,32.0 ], [ -125.0,32.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53517044e4b05569d805a243","contributors":{"authors":[{"text":"Kent, Robert H. 0000-0003-4174-9467 rhkent@usgs.gov","orcid":"https://orcid.org/0000-0003-4174-9467","contributorId":175257,"corporation":false,"usgs":true,"family":"Kent","given":"Robert","email":"rhkent@usgs.gov","middleInitial":"H.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485827,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belitz, Kenneth 0000-0003-4481-2345 kbelitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":442,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","email":"kbelitz@usgs.gov","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":485825,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fram, Miranda S. 0000-0002-6337-059X mfram@usgs.gov","orcid":"https://orcid.org/0000-0002-6337-059X","contributorId":1156,"corporation":false,"usgs":true,"family":"Fram","given":"Miranda","email":"mfram@usgs.gov","middleInitial":"S.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485826,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70100632,"text":"70100632 - 2014 - Testing metapopulation concepts: effects of patch characteristics and neighborhood occupancy on the dynamics of an endangered lagomorph","interactions":[],"lastModifiedDate":"2014-05-16T16:12:51","indexId":"70100632","displayToPublicDate":"2014-04-03T11:53:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2939,"text":"Oikos","active":true,"publicationSubtype":{"id":10}},"title":"Testing metapopulation concepts: effects of patch characteristics and neighborhood occupancy on the dynamics of an endangered lagomorph","docAbstract":"Metapopulation ecology is a field that is richer in theory than in empirical results. Many existing empirical studies use an incidence function approach based on spatial patterns and key assumptions about extinction and colonization rates. Here we recast these assumptions as hypotheses to be tested using 18 years of historic detection survey data combined with four years of data from a new monitoring program for the Lower Keys marsh rabbit. We developed a new model to estimate probabilities of local extinction and colonization in the presence of nondetection, while accounting for estimated occupancy levels of neighboring patches. We used model selection to identify important drivers of population turnover and estimate the effective neighborhood size for this system. Several key relationships related to patch size and isolation that are often assumed in metapopulation models were supported: patch size was negatively related to the probability of extinction and positively related to colonization, and estimated occupancy of neighboring patches was positively related to colonization and negatively related to extinction probabilities. This latter relationship suggested the existence of rescue effects. In our study system, we inferred that coastal patches experienced higher probabilities of extinction and colonization than interior patches. Interior patches exhibited higher occupancy probabilities and may serve as refugia, permitting colonization of coastal patches following disturbances such as hurricanes and storm surges. Our modeling approach should be useful for incorporating neighbor occupancy into future metapopulation analyses and in dealing with other historic occupancy surveys that may not include the recommended levels of sampling replication.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Oikos","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1111/oik.01008","usgsCitation":"Eaton, M., Hughes, P.T., Hines, J., and Nichols, J., 2014, Testing metapopulation concepts: effects of patch characteristics and neighborhood occupancy on the dynamics of an endangered lagomorph: Oikos, v. 123, no. 6, p. 662-676, https://doi.org/10.1111/oik.01008.","productDescription":"15 p.","startPage":"662","endPage":"676","numberOfPages":"15","ipdsId":"IP-052535","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":285550,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/oik.01008"},{"id":285551,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Lower Florida Keys","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -81.730229,24.550671 ], [ -81.730229,24.849433 ], [ -81.288019,24.849433 ], [ -81.288019,24.550671 ], [ -81.730229,24.550671 ] ] ] } } ] }","volume":"123","issue":"6","noUsgsAuthors":false,"publicationDate":"2014-03-06","publicationStatus":"PW","scienceBaseUri":"53517066e4b05569d805a3db","contributors":{"authors":[{"text":"Eaton, Mitchell J.","contributorId":71308,"corporation":false,"usgs":true,"family":"Eaton","given":"Mitchell J.","affiliations":[],"preferred":false,"id":492346,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hughes, Phillip T.","contributorId":68874,"corporation":false,"usgs":true,"family":"Hughes","given":"Phillip","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":492345,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hines, James E. jhines@usgs.gov","contributorId":3506,"corporation":false,"usgs":true,"family":"Hines","given":"James E.","email":"jhines@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":492344,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":405,"corporation":false,"usgs":true,"family":"Nichols","given":"James D.","email":"jnichols@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":492343,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70100580,"text":"70100580 - 2014 - Clinal variation or validation of a subspecies? A case study of the Graptemys nigrinoda complex (Testudines: Emydidae)","interactions":[],"lastModifiedDate":"2014-04-03T11:50:16","indexId":"70100580","displayToPublicDate":"2014-04-03T11:38:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1019,"text":"Biological Journal of the Linnean Society","active":true,"publicationSubtype":{"id":10}},"title":"Clinal variation or validation of a subspecies? A case study of the Graptemys nigrinoda complex (Testudines: Emydidae)","docAbstract":"Widely distributed species often display intraspecific morphological variation due to the abiotic and biotic gradients experienced across their ranges. Historically, in many vertebrate taxa, such as birds and reptiles, these morphological differences within a species were used to delimit subspecies. <i>Graptemys nigrinoda</i> is an aquatic turtle species endemic to the Mobile Bay Basin. Colour pattern and morphological variability were used to describe a subspecies (<i>G. n. delticola</i>) from the lower reaches of the system, although it and the nominate subspecies also reportedly intergrade over a large portion of the range. Other researchers have suggested that these morphological differences merely reflect clinal variation. Our molecular data (mtDNA) did not support the existence of the subspecies, as the haplotypes were differentiated by only a few base pairs and one haplotype was shared between the putative subspecies. While there were significant morphological and pattern differences among putative specimens of <i>G. n. nigrinoda, G. n. delticola</i> and <i>G. n. nigrinoda</i> × <i>delticola</i>, these differences probably represent clinal variation as they were also related to environmental variables [i.e. cumulative drainage area and drainage (categorical)]. Specimens occupying slow-current, high-turbidity river reaches (e.g. the Tensaw River) exhibited greater relative carapace heights and more dark pigmentation, while specimens occupying fast-current, clearer rivers (e.g. the upper Alabama, Cahaba and Tallapoosa rivers) exhibited lower carapace heights and more yellow pigmentation. Given the absence of clear molecular and morphological differences that are related to drainage characteristics, we suggest that there is not sufficient evidence for the recognition of <i>G. n. delticola</i> as a distinct subspecies.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Biological Journal of the Linnean Society","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"The Linnean Society of London","publisherLocation":"London","doi":"10.1111/bij.12234","usgsCitation":"Ennen, J., Kalis, M.E., Patterson, A.L., Kreiser, B.R., Lovich, J.E., Godwin, J., and Qualls, C.P., 2014, Clinal variation or validation of a subspecies? A case study of the Graptemys nigrinoda complex (Testudines: Emydidae): Biological Journal of the Linnean Society, v. 111, no. 4, p. 810-822, https://doi.org/10.1111/bij.12234.","productDescription":"13 p.","startPage":"810","endPage":"822","numberOfPages":"13","ipdsId":"IP-052189","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":285533,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":285315,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/bij.12234"}],"country":"United States","state":"Alabama;Mississippi","otherGeospatial":"Alabama River;Cahaba River;Mobile Bay Basin;Tallapoosa River;Tensaw River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -89.1434,29.6228 ], [ -89.1434,35.2325 ], [ -84.5477,35.2325 ], [ -84.5477,29.6228 ], [ -89.1434,29.6228 ] ] ] } } ] }","volume":"111","issue":"4","noUsgsAuthors":false,"publicationDate":"2014-03-12","publicationStatus":"PW","scienceBaseUri":"5351702de4b05569d805a198","contributors":{"authors":[{"text":"Ennen, Joshua R.","contributorId":60368,"corporation":false,"usgs":false,"family":"Ennen","given":"Joshua R.","affiliations":[{"id":13216,"text":"Tennessee Aquarium Conservation Institute","active":true,"usgs":false}],"preferred":false,"id":492336,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kalis, Marley E.","contributorId":42874,"corporation":false,"usgs":true,"family":"Kalis","given":"Marley","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":492334,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patterson, Adam L.","contributorId":103181,"corporation":false,"usgs":true,"family":"Patterson","given":"Adam","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":492338,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kreiser, Brian R.","contributorId":47691,"corporation":false,"usgs":true,"family":"Kreiser","given":"Brian","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":492335,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":492332,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Godwin, James","contributorId":81015,"corporation":false,"usgs":true,"family":"Godwin","given":"James","affiliations":[],"preferred":false,"id":492337,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Qualls, Carl P.","contributorId":19688,"corporation":false,"usgs":true,"family":"Qualls","given":"Carl","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":492333,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70049065,"text":"ofr20131268 - 2014 - Airborne geophysical surveys conducted in western Nebraska, 2010: contractor reports and data","interactions":[],"lastModifiedDate":"2014-10-06T13:02:59","indexId":"ofr20131268","displayToPublicDate":"2014-04-03T08:28:00","publicationYear":"2014","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":"2013-1268","title":"Airborne geophysical surveys conducted in western Nebraska, 2010: contractor reports and data","docAbstract":"<p>This report contains three contractor reports and data files for an airborne electromagnetic survey flown from June 28 to July 7, 2010. The first report; “SkyTEM Survey: Nebraska, USA, Data” describes data aquisition and processing from a time-domain electromagnetic and magnetic survey performed by SkyTEM Canada, Inc. (the North American SkyTEM subsidiary), in western Nebraska, USA. Digital data for this report are given in Appendix 1. The airborne geophysical data from the SkyTEM survey subsequently were processed and inverted by Aarhus Geophysics ApS, Aarhus, Denmark, to produce resistivity depth sections along each flight line. The result of that processing is described in two reports presented in Appendix 2, “Processing and inversion of SkyTEM data from USGS Area UTM–13” and “Processing and inversion of SkyTEM data from USGS Area UTM–14.”</p>\n<br/>\n<p>Funding for these surveys was provided by the North Platte Natural Resources District, the South Platte Natural Resources District, and the Twin Platte Natural Resources District, in Scottsbluff, Sidney, and North Platte, Nebraska, respectively. Any additional information concerning the geophysical data may be obtained from the U.S. Geological Survey Crustal Geophysics and Geochemistry Science Center, Denver Colorado.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131268","collaboration":"Prepared in cooperation with the NorthPlatte, South Platte, and Twin Platte Natural Resource Districts, Nebraska","usgsCitation":"U.S.Geological Survey Crustal Geophysical and Geochemical Science Center, 2014, Airborne geophysical surveys conducted in western Nebraska, 2010: contractor reports and data: U.S. Geological Survey Open-File Report 2013-1268, Report: iii, 4 p.; 2 Appendices, https://doi.org/10.3133/ofr20131268.","productDescription":"Report: iii, 4 p.; 2 Appendices","numberOfPages":"7","onlineOnly":"Y","ipdsId":"IP-051498","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":285369,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131268.jpg"},{"id":285338,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1268/pdf/ofr2013-1268.pdf"},{"id":285339,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1268/downloads/APPENDIX1/"},{"id":285340,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1268/downloads/APPENDIX2/"},{"id":285317,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1268/"}],"country":"United States","state":"Nebraska","otherGeospatial":"Western Nebraska","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -104.05,40.12 ], [ -104.05,43.0 ], [ -99.2,43.0 ], [ -99.2,40.12 ], [ -104.05,40.12 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53516f2de4b05569d805a030","contributors":{"authors":[{"text":"U.S.Geological Survey Crustal Geophysical and Geochemical Science Center","contributorId":128012,"corporation":true,"usgs":false,"organization":"U.S.Geological Survey Crustal Geophysical and Geochemical Science Center","id":535608,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70055928,"text":"sir20135206 - 2014 - Geochronology and correlation of Tertiary volcanic and intrusive rocks in part of the southern Toquima Range, Nye County, Nevada","interactions":[],"lastModifiedDate":"2014-04-03T08:34:56","indexId":"sir20135206","displayToPublicDate":"2014-04-03T08:21:00","publicationYear":"2014","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":"2013-5206","title":"Geochronology and correlation of Tertiary volcanic and intrusive rocks in part of the southern Toquima Range, Nye County, Nevada","docAbstract":"<p>Extensive volcanic and intrusive igneous activity, partly localized along regional structural zones, characterized the southern Toquima Range, Nevada, in the late Eocene, Oligocene, and Miocene. The general chronology of igneous activity has been defined previously. This major episode of Tertiary magmatism began with emplacement of a variety of intrusive rocks, followed by formation of nine major calderas and associated with voluminous extrusive and additional intrusive activity. Emplacement of volcanic eruptive and collapse megabreccias accompanied formation of some calderas. Penecontemporaneous volcanism in central Nevada resulted in deposition of distally derived outflow facies ash-flow tuff units that are interleaved in the Toquima Range with proximally derived ash-flow tuffs.</p>\n<br/>\n<p>Eruption of the Northumberland Tuff in the north part of the southern Toquima Range and collapse of the Northumberland caldera occurred about 32.3 million years ago. The poorly defined Corcoran Canyon caldera farther to the southeast formed following eruption of the tuff of Corcoran Canyon about 27.2 million years ago. The Big Ten Peak caldera in the south part of the southern Toquima Range Tertiary volcanic complex formed about 27 million years ago during eruption of the tuff of Big Ten Peak and associated air-fall tuffs. The inferred Ryecroft Canyon caldera formed in the south end of the Monitor Valley adjacent to the southern Toquima Range and just north of the Big Ten Peak caldera in response to eruption of the tuff of Ryecroft Canyon about 27 million years ago, and the Moores Creek caldera just south of the Northumberland caldera developed at about the same time. Eruption of the tuff of Mount Jefferson about 26.8 million years ago was accompanied by collapse of the Mount Jefferson caldera in the central part of the southern Toquima Range. An inferred caldera, mostly buried beneath alluvium of Big Smoky Valley southwest of the Mount Jefferson caldera, formed about 26.5 million years ago with eruption of the tuff of Round Mountain. The Manhattan caldera south of the Mount Jefferson caldera and northwest of the Big Ten Peak caldera formed in association with eruption of a series of tuffs, principally the Round Rock Formation, mostly ash-flow tuff, about 24.4 million years ago.</p>\n<br/>\n<p>Extensive <sup>40</sup>Ar/<sup>39</sup>Ar dating of about 60 samples that represent many of the Tertiary extrusive and intrusive rocks in the southern Toquima Range provides precise ages that refine the chronology of previously dated units. New geochronologic data indicate that the petrogenetically related Corcoran Canyon, Ryecroft Canyon, and Mount Jefferson calderas formed during a period of about 560,000 years.</p>\n<br/>\n<p>Electron microprobe analyses of phenocrysts from 20 samples of six dated units underscore inferred petrogenetic relations among some of these units. In particular, compositions of augite, hornblende, and biotite in tuffs erupted from the Corcoran Canyon, Ryecroft Canyon, and Mount Jefferson calderas are similar, which suggests that magmas represented by these tuffs have similar petrogenetic histories. The unique occurrence of hypersthene in Isom-type tuff confirms its derivation from a source beyond the southern Toquima Range.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135206","usgsCitation":"Shawe, D., Snee, L., Byers, F.M., and du Bray, E.A., 2014, Geochronology and correlation of Tertiary volcanic and intrusive rocks in part of the southern Toquima Range, Nye County, Nevada: U.S. Geological Survey Scientific Investigations Report 2013-5206, Report: v, 104 p.; Map: 43.31 x 31.37 inches; Appendixes 1-8, https://doi.org/10.3133/sir20135206.","productDescription":"Report: v, 104 p.; Map: 43.31 x 31.37 inches; Appendixes 1-8","numberOfPages":"115","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-038082","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":285351,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135206.jpg"},{"id":285341,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5206/pdf/sir2013-5206.pdf"},{"id":285342,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2013/5206/pdf/plate_1.pdf"},{"id":285343,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5206/downloads/appendix_1.xlsx"},{"id":285344,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5206/downloads/appendix_2.xlsx"},{"id":285345,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5206/downloads/appendix_3.xlsx"},{"id":285346,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5206/downloads/appendix_4.xlsx"},{"id":285347,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5206/downloads/appendix_5.xlsx"},{"id":285348,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5206/downloads/appendix_6.xlsx"},{"id":285349,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5206/downloads/appendix_7.xlsx"},{"id":285350,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5206/downloads/appendix_8.xlsx"},{"id":285316,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5206/"}],"scale":"48000","projection":"Universal Transverse Mercator projection","datum":"1927 North American datum","country":"United States","state":"Nevada","county":"Nye County","otherGeospatial":"Corcoran Canyon;Mount Jefferson;Ryecroft Canyon;Toquima Range","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -117.125,38.5 ], [ -117.125,38.75 ], [ -116.75,38.75 ], [ -116.75,38.5 ], [ -117.125,38.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5351703fe4b05569d805a214","contributors":{"authors":[{"text":"Shawe, Daniel R.","contributorId":91448,"corporation":false,"usgs":true,"family":"Shawe","given":"Daniel R.","affiliations":[],"preferred":false,"id":486283,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Snee, Lawrence W.","contributorId":81534,"corporation":false,"usgs":true,"family":"Snee","given":"Lawrence W.","affiliations":[],"preferred":false,"id":486282,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Byers, Frank M. Jr.","contributorId":35397,"corporation":false,"usgs":true,"family":"Byers","given":"Frank","suffix":"Jr.","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":486281,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"du Bray, Edward A. 0000-0002-4383-8394 edubray@usgs.gov","orcid":"https://orcid.org/0000-0002-4383-8394","contributorId":755,"corporation":false,"usgs":true,"family":"du Bray","given":"Edward","email":"edubray@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":486280,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70095679,"text":"ofr20141049 - 2014 - Soils, vegetation, and woody debris data from the 2001 Survey Line fire and a comparable unburned site, Tanana Flats region, Alaska","interactions":[],"lastModifiedDate":"2014-04-02T15:03:24","indexId":"ofr20141049","displayToPublicDate":"2014-04-02T14:56:00","publicationYear":"2014","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":"2014-1049","title":"Soils, vegetation, and woody debris data from the 2001 Survey Line fire and a comparable unburned site, Tanana Flats region, Alaska","docAbstract":"This report describes the collection and processing methodologies for samples obtained at two sites within Interior Alaska: (1) a location within the 2001 Survey Line burn, and (2) an unburned location, selected as a control. In 2002 and 2004 U.S. Geological Survey investigators measured soil properties including, but not limited to, bulk density, volumetric water content, carbon content, and nitrogen content from samples obtained from these sites. Stand properties, such as tree density, the amount of woody debris, and understory vegetation, were also measured and are presented in this report.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141049","issn":"2331-1258","usgsCitation":"Manies, K.L., Harden, J.W., and Holingsworth, T.N., 2014, Soils, vegetation, and woody debris data from the 2001 Survey Line fire and a comparable unburned site, Tanana Flats region, Alaska: U.S. Geological Survey Open-File Report 2014-1049, Report: iii, 20 p.; Tanana soil data, https://doi.org/10.3133/ofr20141049.","productDescription":"Report: iii, 20 p.; Tanana soil data","numberOfPages":"25","temporalStart":"2003-01-01","temporalEnd":"2004-12-31","ipdsId":"IP-044961","costCenters":[{"id":556,"text":"Soil Carbon Research","active":false,"usgs":true}],"links":[{"id":285313,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20141049.PNG"},{"id":285311,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1049/pdf/ofr2014-1049.pdf"},{"id":283481,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2014/1049/"},{"id":285312,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2014/1049/downloads/ofr2014-1049_data.zip"}],"country":"United States","state":"Alaska","otherGeospatial":"Tanana Flats;Tanana River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -148.422256,64.63788 ], [ -148.422256,64.710289 ], [ -148.188102,64.710289 ], [ -148.188102,64.63788 ], [ -148.422256,64.63788 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53517064e4b05569d805a3c3","contributors":{"authors":[{"text":"Manies, Kristen L. 0000-0003-4941-9657 kmanies@usgs.gov","orcid":"https://orcid.org/0000-0003-4941-9657","contributorId":2136,"corporation":false,"usgs":true,"family":"Manies","given":"Kristen","email":"kmanies@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":491341,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harden, Jennifer W. 0000-0002-6570-8259 jharden@usgs.gov","orcid":"https://orcid.org/0000-0002-6570-8259","contributorId":1971,"corporation":false,"usgs":true,"family":"Harden","given":"Jennifer","email":"jharden@usgs.gov","middleInitial":"W.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":491340,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holingsworth, Teresa N.","contributorId":47290,"corporation":false,"usgs":true,"family":"Holingsworth","given":"Teresa","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":491342,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70100468,"text":"70100468 - 2014 - Decadal surface water quality trends under variable climate, land use, and hydrogeochemical setting in Iowa, USA","interactions":[],"lastModifiedDate":"2018-09-14T15:54:17","indexId":"70100468","displayToPublicDate":"2014-04-02T10:53:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Decadal surface water quality trends under variable climate, land use, and hydrogeochemical setting in Iowa, USA","docAbstract":"Understanding how nitrogen fluxes respond to changes in agriculture and climate is important for improving water quality. In the midwestern United States, expansion of corn cropping for ethanol production led to increasing N application rates in the 2000s during a period of extreme variability of annual precipitation. To examine the effects of these changes, surface water quality was analyzed in 10 major Iowa Rivers. Several decades of concentration and flow data were analyzed with a statistical method that provides internally consistent estimates of the concentration history and reveals flow-normalized trends that are independent of year-to-year streamflow variations. Flow-normalized concentrations of nitrate+nitrite-N decreased from 2000 to 2012 in all basins. To evaluate effects of annual discharge and N loading on these trends, multiple conceptual models were developed and calibrated to flow-weighted annual concentrations. The recent declining concentration trends can be attributed to both very high and very low discharge in the 2000s and to the long (e.g., 8 year) subsurface residence times in some basins. Dilution of N and depletion of stored N occurs in years with high discharge. Reduced N transport and increased N storage occurs in low-discharge years. Central Iowa basins showed the greatest reduction in flow-normalized concentrations, likely because of smaller storage volumes and shorter residence times. Effects of land-use changes on the water quality of major Iowa Rivers may not be noticeable for years or decades in peripheral basins of Iowa, and may be obscured in the central basins where extreme flows strongly affect annual concentration trends.","language":"English","publisher":"Wiley","doi":"10.1002/2013WR014829","usgsCitation":"Green, C.T., Bekins, B.A., Kalkhoff, S.J., Hirsch, R.M., Liao, L., and Barnes, K., 2014, Decadal surface water quality trends under variable climate, land use, and hydrogeochemical setting in Iowa, USA: Water Resources Research, v. 50, no. 3, p. 2425-2443, https://doi.org/10.1002/2013WR014829.","productDescription":"19 p.","startPage":"2425","endPage":"2443","numberOfPages":"19","onlineOnly":"Y","ipdsId":"IP-052067","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":285296,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":285264,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/2013WR014829"}],"country":"United States","state":"Iowa","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -96.6395,40.3754 ], [ -96.6395,43.5012 ], [ -90.1426,43.5012 ], [ -90.1426,40.3754 ], [ -96.6395,40.3754 ] ] ] } } ] }","volume":"50","issue":"3","noUsgsAuthors":false,"publicationDate":"2014-03-19","publicationStatus":"PW","scienceBaseUri":"53517032e4b05569d805a1af","contributors":{"authors":[{"text":"Green, Christopher T. 0000-0002-6480-8194 ctgreen@usgs.gov","orcid":"https://orcid.org/0000-0002-6480-8194","contributorId":1343,"corporation":false,"usgs":true,"family":"Green","given":"Christopher","email":"ctgreen@usgs.gov","middleInitial":"T.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":492236,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bekins, Barbara A. 0000-0002-1411-6018 babekins@usgs.gov","orcid":"https://orcid.org/0000-0002-1411-6018","contributorId":1348,"corporation":false,"usgs":true,"family":"Bekins","given":"Barbara","email":"babekins@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":492237,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kalkhoff, Stephen J. 0000-0003-4110-1716 sjkalkho@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-1716","contributorId":1731,"corporation":false,"usgs":true,"family":"Kalkhoff","given":"Stephen","email":"sjkalkho@usgs.gov","middleInitial":"J.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":492238,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hirsch, Robert M. 0000-0002-4534-075X rhirsch@usgs.gov","orcid":"https://orcid.org/0000-0002-4534-075X","contributorId":2005,"corporation":false,"usgs":true,"family":"Hirsch","given":"Robert","email":"rhirsch@usgs.gov","middleInitial":"M.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":492239,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Liao, Lixia 0000-0003-2513-0680 lliao@usgs.gov","orcid":"https://orcid.org/0000-0003-2513-0680","contributorId":5311,"corporation":false,"usgs":true,"family":"Liao","given":"Lixia","email":"lliao@usgs.gov","affiliations":[],"preferred":true,"id":492240,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barnes, Kimberlee K.","contributorId":41476,"corporation":false,"usgs":true,"family":"Barnes","given":"Kimberlee K.","affiliations":[],"preferred":false,"id":492241,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
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