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\nDigital flood-inundation maps for a 12.4-mile reach of Big Creek that extends from 260 feet above the McGinnis Ferry Road bridge to the U.S. Geological Survey (USGS) streamgage at Big Creek below Hog Wallow Creek at Roswell, Georgia (02335757), were developed by the USGS in cooperation with the cities of Alpharetta and Roswell, Georgia. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage at Big Creek near Alpharetta, Georgia (02335700). Real-time stage information from this USGS streamgage may be obtained at http://waterdata.usgs.gov/ and can be used in conjunction with these maps to estimate near real-time areas of inundation. The National Weather Service (NWS) is incorporating results from this study into the Advanced Hydrologic Prediction Service (AHPS) flood-warning system http://water.weather.gov/ahps/). The NWS forecasts flood hydrographs for many streams where the USGS operates streamgages and provides flow data. The forecasted peak-stage information for the USGS streamgage at Big Creek near Alpharetta (02335700), available through the AHPS Web site, may be used in conjunction with the maps developed for this study to show predicted areas of flood inundation.
\nA one-dimensional step-backwater model was developed using the U.S. Army Corps of Engineers HEC–RAS software for Big Creek and was used to compute flood profiles for a 12.4-mile reach of Big Creek. The model was calibrated using the most current (2015) stage-discharge relations at two USGS streamgages on Big Creek: Big Creek near Alpharetta (02335700) and Big Creek below Hog Wallow Creek at Roswell (02335757). The hydraulic model was then used to simulate 19 water-surface profiles at 0.5-foot intervals at the Big Creek near Alpharetta streamgage. The profiles ranged from just above bankfull stage (6.0 feet) to approximately 1.95 feet above the highest recorded water level at the Alpharetta streamgage site (15.0 feet). The simulated water-surface profiles were then combined with a geographic information system digital elevation model—derived from light detection and ranging data having a 3.0-foot horizontal resolution—to delineate the area flooded at each 0.5-foot interval of stream stage.
\nThe availability of these maps, when combined with real-time stage information from USGS streamgages and forecasted stream stage from the NWS, provides emergency management personnel and residents with critical information during flood-response activities such as evacuations and road closures, in addition to post-flood recovery efforts.
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720 Gracern Road
Columbia, SC 29210
http://www.usgs.gov/water/southatlantic/
To better understand the fate and transport of neonicotinoid insecticides, water samples were collected from streams across the United States. In a nationwide study, at least one neonicotinoid was detected in 53 % of the samples collected, with imidacloprid detected most frequently (37 %), followed by clothianidin (24 %), thiamethoxam (21 %), dinotefuran (13 %), acetamiprid (3 %) and thiacloprid (0 %). Clothianidin and thiamethoxam concentrations were positively related to the percentage of the land use in cultivated crop production and imidacloprid concentrations were positively related to the percentage of urban area within the basin. Additional sampling was also conducted in targeted research areas to complement these national-scale results, including determining: (1) neonicotinoid concentrations during elevated flow conditions in an intensely agricultural region; (2) temporal patterns of neonicotinoids in heavily urbanised basins; (3) neonicotinoid concentrations in agricultural basins in a nationally important ecosystem; and (4) in-stream transport of neonicotinoids near a wastewater treatment plant. Across all study areas, at least one neonicotinoid was detected in 63 % of the 48 streams sampled.
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Center","active":true,"usgs":true}],"preferred":true,"id":568556,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70171067,"text":"70171067 - 2015 - Drivers and synergies in the management of inland fisheries: Searching for sustainable solutions","interactions":[],"lastModifiedDate":"2018-04-24T13:46:21","indexId":"70171067","displayToPublicDate":"2015-08-19T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":18,"text":"Abstract or summary"},"title":"Drivers and synergies in the management of inland fisheries: Searching for sustainable solutions","docAbstract":"Freshwater is a shared resource. Water challenges (i.e., too much, too little, too dirty) are recognized to have global implications. Many sectors rely upon water and, in some cases, the limited availability of water leads to tough decisions. Though inland fish and fisheries play important roles in providing food security, human well-being, and ecosystem productivity, this sector is often underappreciated in water resource planning because valuation is difficult and governance is complex, unclear, or non-existent. Additionally, inland fisheries are an economically small sector and, in most cases, the value of inland fisheries will never be the main driver of decision making.
\nAt the 2015 Global Conference on Inland Fisheries, we convened a Drivers and Synergies panel and working group to discuss competing sectors (e.g., hydropower, transportation, agriculture, mining and oil and gas extraction, forestry, tourism and recreation, and aquaculture) and large-scale drivers which exist predominately outside of the water sectors (e.g., economic growth, diversifying economies, population growth, urbanization, and climate change). Drivers will influence these sectors and tradeoffs will be made. Management of sustainable inland water systems requires making informed choices emphasizing those services that will provide sustainable benefits for humans while maintaining well-functioning ecological systems.
","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"Enhancing sustainability of inland fisheries through cross-sectoral collaboration","conferenceTitle":"American Fisheries Society 145th Annual Meeting","conferenceDate":"August 16-20, 2015","conferenceLocation":"Portland, OR","language":"English","publisher":"American Fisheries Society","usgsCitation":"Lynch, A., and Beard, 2015, Drivers and synergies in the management of inland fisheries: Searching for sustainable solutions, in Enhancing sustainability of inland fisheries through cross-sectoral collaboration, Portland, OR, August 16-20, 2015, HTML Document.","productDescription":"HTML Document","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063982","costCenters":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":321670,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":321669,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://afs.confex.com/afs/2015/webprogram/start.html"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5746ccb5e4b07e28b662dc76","contributors":{"authors":[{"text":"Lynch, Abigail 0000-0001-8449-8392 ajlynch@usgs.gov","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":169460,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","email":"ajlynch@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":629700,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beard, Jr. 0000-0003-2632-2350 dbeard@usgs.gov","orcid":"https://orcid.org/0000-0003-2632-2350","contributorId":169459,"corporation":false,"usgs":true,"family":"Beard","suffix":"Jr.","email":"dbeard@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":629699,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70156263,"text":"70156263 - 2015 - Key seabird areas in southern New England identified using a community occupancy model","interactions":[],"lastModifiedDate":"2022-11-10T16:25:15.899818","indexId":"70156263","displayToPublicDate":"2015-08-18T14:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2663,"text":"Marine Ecology Progress Series","active":true,"publicationSubtype":{"id":10}},"title":"Key seabird areas in southern New England identified using a community occupancy model","docAbstract":"Seabirds are of conservation concern, and as new potential risks to seabirds are arising, the need to provide unbiased estimates of species’ distributions is growing. We applied community occupancy models to detection/non-detection data collected from repeated aerial strip-transect surveys conducted in 2 large study plots off southern New England, USA; one off the coast of Rhode Island and the other in Nantucket Sound. A total of 17 seabird species were observed at least once in each study plot. We found that detection varied by survey date and effort for most species and the average detection probability across species was less than 0.4. We estimated the influence of water depth, sea surface temperature, and sea surface chl a concentration on species-specific occupancy. Diving species showed large differences between the 2 study plots in their predicted winter distributions, which were largely explained by water depth acting as a stronger predictor of occupancy in Rhode Island than in Nantucket Sound. Conversely, similarities between the 2 study plots in predicted winter distributions of surface-feeding species were explained by sea surface temperature or chlorophyll a concentration acting as predictors of these species’ occupancy in both study plots. We predicted the number of species at each site using the observed data in order to detect ‘hot-spots’ of seabird diversity and use in the 2 study plots. These results provide new information on detection of species, areas of use, and relationships with environmental variables that will be valuable for biologists and planners interested in seabird conservation in the region.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/meps11316","usgsCitation":"O’Connell, A.F., Flanders, N.P., Gardner, B., Winiarski, K.J., Paton, P.W., and Allison, T., 2015, Key seabird areas in southern New England identified using a community occupancy model: Marine Ecology Progress Series, v. 533, p. 277-290, https://doi.org/10.3354/meps11316.","productDescription":"13 p.","startPage":"277","endPage":"290","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065754","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":489199,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.uri.edu/nrs_facpubs/684","text":"External Repository"},{"id":306869,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts, Rhode Island","otherGeospatial":"Nantucket Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.08616827991628,\n 41.27118954497578\n ],\n [\n -70.0395161560485,\n 41.279537474410574\n ],\n [\n -69.99508556188886,\n 41.317924198169436\n ],\n [\n -70.01507932926107,\n 41.35795582003885\n ],\n [\n -69.99508556188886,\n 41.549432491630995\n ],\n [\n -69.95954108656126,\n 41.67898183236355\n ],\n [\n -70.14614958203185,\n 41.664047111649865\n ],\n [\n -70.18391558706759,\n 41.66736623791493\n ],\n [\n -70.22612465151963,\n 41.65740834581047\n ],\n [\n -70.25278300801521,\n 41.65906810143275\n ],\n [\n -70.32387195867095,\n 41.64412876095588\n ],\n [\n -70.43494844407006,\n 41.64744891381079\n ],\n [\n -70.50381586501766,\n 41.564393770796954\n ],\n [\n -70.56601869684138,\n 41.464586499997324\n ],\n [\n -70.57490481567342,\n 41.417957113748685\n ],\n [\n -70.50381586501766,\n 41.39129670826401\n ],\n [\n -70.30609972100689,\n 41.336275079067576\n ],\n [\n -70.26166912684724,\n 41.2978991610687\n ],\n [\n -70.2105739435637,\n 41.282876347251204\n ],\n [\n -70.08616827991628,\n 41.27118954497578\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.95882044294079,\n 41.367829886783426\n ],\n [\n -71.88995302199318,\n 41.23097435648074\n ],\n [\n -71.38566577828065,\n 41.33781303063924\n ],\n [\n -71.38344424857253,\n 41.42115900781667\n ],\n [\n -71.43898249127234,\n 41.45613246374154\n ],\n [\n -71.50118532309607,\n 41.44447673914905\n ],\n [\n -71.55894509550349,\n 41.39449991636312\n ],\n [\n -71.6366986352831,\n 41.40116571472586\n ],\n [\n -71.67668617002647,\n 41.37783242960529\n ],\n [\n -71.82108560104558,\n 41.34448463923664\n ],\n [\n -71.95882044294079,\n 41.367829886783426\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"533","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55d44922e4b0518e35469477","contributors":{"authors":[{"text":"O’Connell, Allan F. 0000-0001-7032-7023 aoconnell@usgs.gov","orcid":"https://orcid.org/0000-0001-7032-7023","contributorId":471,"corporation":false,"usgs":true,"family":"O’Connell","given":"Allan","email":"aoconnell@usgs.gov","middleInitial":"F.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":568441,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flanders, Nicholas P.","contributorId":146614,"corporation":false,"usgs":false,"family":"Flanders","given":"Nicholas","email":"","middleInitial":"P.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":568442,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gardner, Beth","contributorId":91612,"corporation":false,"usgs":false,"family":"Gardner","given":"Beth","affiliations":[{"id":13553,"text":"University of Washington-Seattle","active":true,"usgs":false}],"preferred":false,"id":568443,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Winiarski, Kristopher J.","contributorId":146615,"corporation":false,"usgs":false,"family":"Winiarski","given":"Kristopher","email":"","middleInitial":"J.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":568444,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Paton, Peter W. C.","contributorId":146616,"corporation":false,"usgs":false,"family":"Paton","given":"Peter","email":"","middleInitial":"W. C.","affiliations":[{"id":6923,"text":"University of Rhode Island, Kingston, RI","active":true,"usgs":false}],"preferred":false,"id":568445,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Allison, Taber","contributorId":146617,"corporation":false,"usgs":false,"family":"Allison","given":"Taber","affiliations":[],"preferred":false,"id":568446,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70156262,"text":"70156262 - 2015 - Source mechanism of small long-period events at Mount St. Helens in July 2005 using template matching, phase-weighted stacking, and full-waveform inversion","interactions":[],"lastModifiedDate":"2015-10-26T14:03:20","indexId":"70156262","displayToPublicDate":"2015-08-18T14:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Source mechanism of small long-period events at Mount St. Helens in July 2005 using template matching, phase-weighted stacking, and full-waveform inversion","docAbstract":"Long-period (LP, 0.5-5 Hz) seismicity, observed at volcanoes worldwide, is a recognized signature of unrest and eruption. Cyclic LP “drumbeating” was the characteristic seismicity accompanying the sustained dome-building phase of the 2004–2008 eruption of Mount St. Helens (MSH), WA. However, together with the LP drumbeating was a near-continuous, randomly occurring series of tiny LP seismic events (LP “subevents”), which may hold important additional information on the mechanism of seismogenesis at restless volcanoes. We employ template matching, phase-weighted stacking, and full-waveform inversion to image the source mechanism of one multiplet of these LP subevents at MSH in July 2005. The signal-to-noise ratios of the individual events are too low to produce reliable waveform-inversion results, but the events are repetitive and can be stacked. We apply network-based template matching to 8 days of continuous velocity waveform data from 29 June to 7 July 2005 using a master event to detect 822 network triggers. We stack waveforms for 359 high-quality triggers at each station and component, using a combination of linear and phase-weighted stacking to produce clean stacks for use in waveform inversion. The derived source mechanism pointsto the volumetric oscillation (~10 m3) of a subhorizontal crack located at shallow depth (~30 m) in an area to the south of Crater Glacier in the southern portion of the breached MSH crater. A possible excitation mechanism is the sudden condensation of metastable steam from a shallow pressurized hydrothermal system as it encounters cool meteoric water in the outer parts of the edifice, perhaps supplied from snow melt.
","language":"English","publisher":"Wiley","doi":"10.1002/2015JB012279","usgsCitation":"Matoza, R.S., Chouet, B.A., Dawson, P.B., Shearer, P., Haney, M.M., Waite, G.P., Moran, S.C., and Mikesell, T.D., 2015, Source mechanism of small long-period events at Mount St. Helens in July 2005 using template matching, phase-weighted stacking, and full-waveform inversion: Journal of Geophysical Research, v. 120, no. 9, p. 6351-6364, https://doi.org/10.1002/2015JB012279.","productDescription":"14 p.","startPage":"6351","endPage":"6364","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066288","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":471869,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://escholarship.org/uc/item/7dv8w3bq","text":"Publisher Index Page"},{"id":306870,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mount St. Helens","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.2723388671875,\n 46.14939437647686\n ],\n [\n -122.2723388671875,\n 46.283376780187254\n ],\n [\n -122.09793090820311,\n 46.283376780187254\n ],\n [\n -122.09793090820311,\n 46.14939437647686\n ],\n [\n -122.2723388671875,\n 46.14939437647686\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"120","issue":"9","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-18","publicationStatus":"PW","scienceBaseUri":"55d44923e4b0518e3546947c","contributors":{"authors":[{"text":"Matoza, Robin S.","contributorId":54873,"corporation":false,"usgs":true,"family":"Matoza","given":"Robin","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":568434,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chouet, Bernard A. 0000-0001-5527-0532 chouet@usgs.gov","orcid":"https://orcid.org/0000-0001-5527-0532","contributorId":3304,"corporation":false,"usgs":true,"family":"Chouet","given":"Bernard","email":"chouet@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":568433,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dawson, Phillip B. dawson@usgs.gov","contributorId":2751,"corporation":false,"usgs":true,"family":"Dawson","given":"Phillip","email":"dawson@usgs.gov","middleInitial":"B.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":568435,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shearer, Peter M.","contributorId":78946,"corporation":false,"usgs":true,"family":"Shearer","given":"Peter M.","affiliations":[],"preferred":false,"id":568436,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haney, Matthew M. mhaney@usgs.gov","contributorId":2943,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":false,"id":568437,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waite, Gregory P.","contributorId":146613,"corporation":false,"usgs":false,"family":"Waite","given":"Gregory","email":"","middleInitial":"P.","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":568438,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moran, Seth C. 0000-0001-7308-9649 smoran@usgs.gov","orcid":"https://orcid.org/0000-0001-7308-9649","contributorId":548,"corporation":false,"usgs":true,"family":"Moran","given":"Seth","email":"smoran@usgs.gov","middleInitial":"C.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":568439,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mikesell, T. Dylan","contributorId":52856,"corporation":false,"usgs":true,"family":"Mikesell","given":"T.","email":"","middleInitial":"Dylan","affiliations":[],"preferred":false,"id":568440,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70148086,"text":"sir20155073 - 2015 - Water-budgets and recharge-area simulations for the Spring Creek and Nittany Creek Basins and parts of the Spruce Creek Basin, Centre and Huntingdon Counties, Pennsylvania, Water Years 2000–06","interactions":[],"lastModifiedDate":"2015-08-27T13:38:16","indexId":"sir20155073","displayToPublicDate":"2015-08-17T12:00:00","publicationYear":"2015","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":"2015-5073","title":"Water-budgets and recharge-area simulations for the Spring Creek and Nittany Creek Basins and parts of the Spruce Creek Basin, Centre and Huntingdon Counties, Pennsylvania, Water Years 2000–06","docAbstract":"This report describes the results of a study by the U.S. Geological Survey in cooperation with ClearWater Conservancy and the Pennsylvania Department of Environmental Protection to develop a hydrologic model to simulate a water budget and identify areas of greater than average recharge for the Spring Creek Basin in central Pennsylvania. The model was developed to help policy makers, natural resource managers, and the public better understand and manage the water resources in the region. The Groundwater and Surface-water FLOW model (GSFLOW), which is an integration of the Precipitation-Runoff Modeling System (PRMS) and the Modular Groundwater Flow Model (MODFLOW-NWT), was used to simulate surface water and groundwater in the Spring Creek Basin for water years 2000–06. Because the groundwater and surface-water divides for the Spring Creek Basin do not coincide, the study area includes the Nittany Creek Basin and headwaters of the Spruce Creek Basin. The hydrologic model was developed by the use of a stepwise process: (1) develop and calibrate a PRMS model and steady-state MODFLOW-NWT model; (2) re-calibrate the steady-state MODFLOW-NWT model using potential recharge estimates simulated from the PRMS model, and (3) integrate the PRMS and MODFLOW-NWT models into GSFLOW. The individually calibrated PRMS and MODFLOW-NWT models were used as a starting point for the calibration of the fully coupled GSFLOW model. The GSFLOW model calibration was done by comparing observations and corresponding simulated values of streamflow from 11 streamgages and groundwater levels from 16 wells. The cumulative water budget and individual water budgets for water years 2000–06 were simulated by using GSFLOW. The largest source and sink terms are represented by precipitation and evapotranspiration, respectively. For the period simulated, a net surplus in the water budget was computed where inflows exceeded outflows by about 1.7 billion cubic feet (0.47 inches per year over the basin area); storage increased by about the same amount to balance the budget. The rate and distribution of recharge throughout the Spring Creek, Nittany Creek, and Spruce Creek Basins is variable as a result of the high degree of hydrogeologic heterogeneity and karst features. The greatest amount of recharge was simulated in the carbonate-bedrock valley, near the toe slopes of Nittany and Tussey Mountains, in the Scotia Barrens, and along the area coinciding with the Gatesburg Formation. Runoff extremes were observed for water years 2001 (dry year) and 2004 (wet year). Simulated average recharge rates (water reaching the saturated zone as defined in GSFLOW) for 2001 and 2004 were 5.4 in/yr and 22.0 in/yr, respectively. Areas where simulations show large variations in annual recharge between wet and dry years are the same areas where simulated recharge was large. Those areas where rates of groundwater recharge are much higher than average, and are capable of accepting substantially greater quantities of recharge during wet years, might be considered critical for maintaining the flow of springs, stream base flow, or the source of water to supply wells. The slopes of the Bald Eagle, Tussey, and Nittany Mountains are relatively insensitive to variations in recharge, primarily because of reduced infiltration rates and steep slopes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155073","collaboration":"Prepared in cooperation with the Clearwater Conservancy and Pennsylvania Department of Environmental Protection","usgsCitation":"Fulton, J.W., Risser, D.W., Regan, R.S., Walker, J.F., Hunt, R.J., Niswonger, R.G., Hoffman, S.A., and Markstrom, S.L., 2015, Water-budgets and recharge-area simulations for the Spring Creek and Nittany Creek Basins and parts of the Spruce Creek Basin, Centre and Huntingdon Counties, Pennsylvania, Water Years 2000–06: U.S. Geological Survey Scientific Investigations Report 2015–5073, 86 p, https://dx.doi.org/10.3133/sir20155073.","productDescription":"x, 86 p.","numberOfPages":"100","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-006529","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":306786,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5073/coverthb.jpg"},{"id":306791,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5073/sir20155073.pdf","text":"Report","size":"27.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5-83"}],"country":"United States","state":"Pennsylvania","county":"Centre County, Huntingdon County","otherGeospatial":"Nittany Creek Basin, Spring Creek Basin, Spruce Creek Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.7179718017578,\n 40.979898069620155\n ],\n [\n -77.55661010742188,\n 40.86004454780482\n ],\n [\n -77.76466369628905,\n 40.7743018636372\n ],\n [\n -77.87384033203124,\n 40.74205475883487\n ],\n [\n -77.93975830078125,\n 40.706148461723764\n ],\n [\n -78.05648803710938,\n 40.66188943992171\n ],\n [\n -78.11897277832031,\n 40.62385529380968\n ],\n [\n -78.16154479980469,\n 40.59283882963389\n ],\n [\n -78.277587890625,\n 40.643135583312805\n ],\n [\n -78.16497802734375,\n 40.730608477796636\n ],\n [\n -78.01666259765625,\n 40.82212357516945\n ],\n [\n -77.82440185546875,\n 40.9280401053324\n ],\n [\n -77.7179718017578,\n 40.979898069620155\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
http://pa.water.usgs.gov/
Documentation of the interacting effects of river regulation and climate on riparian vegetation has typically been limited to small segments of rivers or focused on individual plant species. We examine spatiotemporal variability in riparian vegetation for the Colorado River in Grand Canyon relative to river regulation and climate, over the five decades since completion of the upstream Glen Canyon Dam in 1963. Long-term changes along this highly modified, large segment of the river provide insights for management of similar riparian ecosystems around the world. We analyze vegetation extent based on maps and imagery from eight dates between 1965 and 2009, coupled with the instantaneous hydrograph for the entire period. Analysis confirms a net increase in vegetated area since completion of the dam. Magnitude and timing of such vegetation changes are river stage-dependent. Vegetation expansion is coincident with inundation frequency changes and is unlikely to occur for time periods when inundation frequency exceeds approximately 5%. Vegetation expansion at lower zones of the riparian area is greater during the periods with lower peak and higher base flows, while vegetation at higher zones couples with precipitation patterns and decreases during drought. Short pulses of high flow, such as the controlled floods of the Colorado River in 1996, 2004, and 2008, do not keep vegetation from expanding onto bare sand habitat. Management intended to promote resilience of riparian vegetation must contend with communities that are sensitive to the interacting effects of altered flood regimes and water availability from river and precipitation.
","language":"English","publisher":"Wiley","doi":"10.1002/2015JG002991","usgsCitation":"Sankey, J.B., Ralston, B.E., Grams, P.E., Schmidt, J.C., and Cagney, L.E., 2015, Riparian vegetation, Colorado River, and climate: five decades of spatiotemporal dynamics in the Grand Canyon with river regulation: Journal of Geophysical Research, v. 120, no. 8, p. 1532-1547, https://doi.org/10.1002/2015JG002991.","productDescription":"16 p.","startPage":"1532","endPage":"1547","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057695","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":471873,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jg002991","text":"Publisher Index Page"},{"id":438687,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7J67F0P","text":"USGS data release","linkHelpText":"Riparian vegetation, Colorado River, and climate: five decades of spatio-temporal dynamics in the Grand Canyon with river regulation"},{"id":306821,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.87489036365587,\n 36.183332582730785\n ],\n [\n -114.96663444035812,\n 36.1133656493287\n ],\n [\n -114.7780493938034,\n 35.77500270138579\n ],\n [\n -114.53849541574752,\n 35.7956760430293\n ],\n [\n -114.23777871989012,\n 35.84527012003289\n ],\n [\n -114.02370920758482,\n 35.80807746686432\n ],\n [\n -113.90648066513195,\n 35.71708874712901\n ],\n [\n -113.702604939127,\n 35.52235542753424\n ],\n [\n -113.49872921312203,\n 35.57211964284535\n ],\n [\n -113.16233426521372,\n 35.617709701661525\n ],\n [\n -113.0552995090609,\n 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PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-13","publicationStatus":"PW","scienceBaseUri":"55d2f7a4e4b0518e35468cc1","contributors":{"authors":[{"text":"Sankey, Joel B. 0000-0003-3150-4992 jsankey@usgs.gov","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":3935,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel","email":"jsankey@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":568135,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ralston, Barbara E. 0000-0001-9991-8994 bralston@usgs.gov","orcid":"https://orcid.org/0000-0001-9991-8994","contributorId":606,"corporation":false,"usgs":true,"family":"Ralston","given":"Barbara","email":"bralston@usgs.gov","middleInitial":"E.","affiliations":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"preferred":false,"id":568326,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grams, Paul E. 0000-0002-0873-0708 pgrams@usgs.gov","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":1830,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","email":"pgrams@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":568327,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmidt, John C. 0000-0002-2988-3869 jcschmidt@usgs.gov","orcid":"https://orcid.org/0000-0002-2988-3869","contributorId":1983,"corporation":false,"usgs":true,"family":"Schmidt","given":"John","email":"jcschmidt@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":568328,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cagney, Laura E. 0000-0003-3282-2458 lcagney@usgs.gov","orcid":"https://orcid.org/0000-0003-3282-2458","contributorId":4744,"corporation":false,"usgs":true,"family":"Cagney","given":"Laura","email":"lcagney@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":568329,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70155952,"text":"ofr20151152 - 2015 - A conceptual model for site-level ecology of the giant gartersnake (Thamnophis gigas) in the Sacramento Valley, California","interactions":[],"lastModifiedDate":"2015-08-17T09:41:12","indexId":"ofr20151152","displayToPublicDate":"2015-08-14T18:00:00","publicationYear":"2015","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":"2015-1152","title":"A conceptual model for site-level ecology of the giant gartersnake (Thamnophis gigas) in the Sacramento Valley, California","docAbstract":"Giant gartersnakes (Thamnophis gigas) comprise a species of semi-aquatic snakes precinctive to marshes in the Central Valley of California (Hansen and Brode, 1980; Rossman and others, 1996). Because more than 90 percent of their historical wetland habitat has been converted to other uses (Frayer and others, 1989; Garone, 2007), giant gartersnakes have been listed as threatened by the State of California (California Department of Fish and Game Commission , 1971) and the United States (U.S. Fish and Wildlife Service, 1993). Giant gartersnakes currently occur in a highly modified landscape, with most extant populations occurring in the rice - growing regions of the Sacramento Valley, especially near areas that historically were tule marsh habitat (Halstead and others, 2010, 2014).
\nIn ricelands and managed marshes, many operational decisions likely affect the health and viability of giant gartersnake populations. Land-use decisions, including the management of water, aquatic vegetation, terrestrial vegetation, and co-occurring species, have the potential to affect giant gartersnakes. Little is known, however, about the effects of these types of decisions on the viability of giant gartersnake populations. Bayesian network models are a useful tool to help guide decisions with uncertain outcomes. These models require the articulation of what experts think they know about a system, and facilitate learning about the hypothesized relations (Marcot and others, 2001; Uusitalo , 2007).
\nBayesian networks further provide a clear visual display of the model that facilitates understanding among various stakeholders (Marcot and others, 2001; Uusitalo , 2007). Empirical data and expert judgment can be combined, as continuous or categorical variables, to update knowledge about the system (Marcot and others, 2001; Uusitalo , 2007). Importantly, Bayesian network models allow inference from causes to consequences, but also from consequences to causes, so that data can inform the states of nodes (values of different random variables) in either direction (Marcot and others, 2001; Uusitalo , 2007). Because they can incorporate both decision nodes that represent management actions and utility nodes that quantify the costs and benefits of outcomes, Bayesian networks are ideally suited to risk analysis and adaptive management (Nyberg and others, 2006; Howes and others, 2010). Thus, Bayesian network models are useful in situations where empirical data are not available, such as questions concerning the responses of giant gartersnakes to management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151152","collaboration":"Prepared in cooperation with the California Department of Water Resources","usgsCitation":"Halstead, B.J., Wylie, G.D., Casazza, M.L., Hansen, E.C., Scherer, R.D., and Patterson, L.C., 2015, A conceptual model for site-level ecology of the giant gartersnake (Thamnophis gigas) in the Sacramento Valley, California: U.S. Geological Survey Open-File Report 2015-1152, 152 p., https://dx.doi.org/10.3133/ofr20151152.","productDescription":"iv, 152 p.","numberOfPages":"160","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-061941","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":306765,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1152/coverthb.jpg"},{"id":306766,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1152/ofr20151152.pdf","text":"Report","size":"4.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1152"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.26684570312499,\n 38.47079371120379\n ],\n [\n -122.26684570312499,\n 39.51675478434244\n ],\n [\n -121.42639160156249,\n 39.51675478434244\n ],\n [\n -121.42639160156249,\n 38.47079371120379\n ],\n [\n -122.26684570312499,\n 38.47079371120379\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
http://werc.usgs.gov/
Measuring vertically nested temperatures at the streambed interface poses practical challenges that are addressed here with a new discrete subsurface temperature profiling probe. We describe a new temperature probe and its application for heat as a tracer investigations to demonstrate the probe's utility. Accuracy and response time of temperature measurements made at 6 discrete depths in the probe were analyzed in the laboratory using temperature bath experiments. We find the temperature probe to be an accurate and robust instrument that allows for easily installation and long-term monitoring in highly variable environments. Because the probe is inexpensive and versatile, it is useful for many environmental applications that require temperature data collection for periods of several months in environments that are difficult to access or require minimal disturbance.
","language":"English","publisher":"American Geophysical Union","publisherLocation":"Washington, D.C.","doi":"10.1002/2015WR017574","usgsCitation":"Naranjo, R.C., and Turcotte, R., 2015, A new temperature profiling probe for investigating groundwater-surface water interaction: Water Resources Research, v. 51, no. 9, p. 7790-7797, https://doi.org/10.1002/2015WR017574.","productDescription":"8 p.","startPage":"7790","endPage":"7797","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058645","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":307101,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"9","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-13","publicationStatus":"PW","scienceBaseUri":"55d84bade4b0518e3546efc5","contributors":{"authors":[{"text":"Naranjo, Ramon C. 0000-0003-4469-6831 rnaranjo@usgs.gov","orcid":"https://orcid.org/0000-0003-4469-6831","contributorId":3391,"corporation":false,"usgs":true,"family":"Naranjo","given":"Ramon","email":"rnaranjo@usgs.gov","middleInitial":"C.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":568927,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Turcotte, Robert","contributorId":146772,"corporation":false,"usgs":false,"family":"Turcotte","given":"Robert","email":"","affiliations":[{"id":16740,"text":"Alpha Mach, INC","active":true,"usgs":false}],"preferred":false,"id":568928,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70148360,"text":"70148360 - 2015 - Hydroacoustic signatures of Colorado Riverbed sediments in Marble and Grand Canyons using multibeam sonar","interactions":[],"lastModifiedDate":"2018-04-23T13:09:31","indexId":"70148360","displayToPublicDate":"2015-08-14T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Hydroacoustic signatures of Colorado Riverbed sediments in Marble and Grand Canyons using multibeam sonar","docAbstract":"Characterizing the large-scale sedimentary make-up of heterogeneous riverbeds (Nelson et al., 2014), which consist of a patchwork of sediment types over small scales (less than one to several tens of meters) (Dietrich and Smith, 1984) requires high resolution measurements of sediment grain size. Capturing such variability with conventional physical (e.g. grabs, cores, and dredges) or underwater photographic sampling (Rubin et al., 2007; Buscombe et al., 2014a) would be prohibitively costly and time-consuming. However, characterizing bed sediments using high-frequency (several hundred kilohertz) acoustic backscatter from swath-mapping systems has the potential to provide near complete coverage of the bed (Brown and Blondel, 2009; Brown et al., 2011; Snellen et al., 2013), at resolutions down to a few centimeters, which photographic sampling could not practically achieve within the same time and with the same positional accuracy.
In shallow water, the physics of high frequency scattering of sound are relatively poorly understood, therefore acoustic sediment classification are almost always statistical (Snellen et al., 2013). Many such methods proposed to date are designed for characterizing large areas of seabed (Brown and Blondel, 2009; Brown et al., 2011) at relatively poor resolution (tens of meters to several hundred meters) and therefore rely on aggregation of data over scales much larger than the typical scales of sediment patchiness on heterogeneous riverbeds. In response to this need, Buscombe et al. (2014b, 2014c) developed a new statistical method for acoustic sediment classification based on spectral analysis of backscatter. This method is both continuous in coverage and of sufficient resolution (order meter or less) to characterize sediment variability on patchy riverbeds. Here, we apply these methods to multibeam echosounder (MBES) data collected from the bed of the Colorado River in Marble and Grand Canyons.
Sediment dynamics on the Colorado River in Grand Canyon National Park have been studied for several decades (e.g. Howard and Dolan, 1981; Rubin et al., 2002). Particular focus has been given to sandbars in large eddies downstream of tributary debris fans (Schmidt, 1990) because they are considered valuable resources by stakeholders and managers. Due to the severe limitations in sand supply imposed by Glen Canyon Dam (Howard and Dolan, 1981; Topping et al., 2000; Hazel et al., 2006), understanding the effectiveness of sandbar management practices, such as controlled floods (Rubin et al. 2002; Topping et al., 2006; Hazel et al., 2010), and the long-term fate of sand in Grand Canyon over decadal timescales, requires construction of accurate sand budgets, which involves detailed monitoring of influx, efflux and changes in sand storage (Topping et al., 2000; Topping et al., 2010; Grams et al., 2013) and assessments of uncertainties in sand-budget calculations (Grams et al., 2013).
In order to estimate the sand budget, it is necessary to estimate what component of observed morphological changes is sand and what component is coarser. Grams et al. (2013) classified sand and coarse substrates using topographic roughness derived from digital elevation models, but the classification skill was estimated to be only 60-70%. In addition, sand bedforms had to be delineated manually, and validation was based on grain-size observations with positional uncertainties up to tens of meters. Because the morphology of the Colorado riverbed in Grand Canyon is mapped - to a large extent - using MBES (Kaplinski et al., 2009), the primary motivation for the present study is to examine how uncertainties in sand budgets can be constrained by producing maps of surface sediment types using the completely automated methods of Buscombe et al (2014b, 2014c) based on statistical analysis of MBES acoustic backscatter.
","conferenceTitle":"3rd Joint Federal Interagency Conference","conferenceDate":"April 19-23, 2015","conferenceLocation":"Reno, NV","language":"English","publisher":"Joint Federal Interagency Conference","usgsCitation":"Buscombe, D.D., Grams, P.E., Kaplinski, M., Tusso, R.B., and Rubin, D.M., 2015, Hydroacoustic signatures of Colorado Riverbed sediments in Marble and Grand Canyons using multibeam sonar, 3rd Joint Federal Interagency Conference, Reno, NV, April 19-23, 2015, 12 p.","productDescription":"12 p.","ipdsId":"IP-060883","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":342198,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":353659,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2015/openconf/modules/request.php?module=oc_program&action=summary.php&id=76"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon, Marble Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.5,\n 36\n ],\n [\n -111.25,\n 36\n ],\n [\n -111.25,\n 37\n ],\n [\n -112.5,\n 37\n ],\n [\n -112.5,\n 36\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"593910b0e4b0764e6c5e888c","contributors":{"authors":[{"text":"Buscombe, Daniel D. 0000-0001-6217-5584 dbuscombe@usgs.gov","orcid":"https://orcid.org/0000-0001-6217-5584","contributorId":5020,"corporation":false,"usgs":false,"family":"Buscombe","given":"Daniel","email":"dbuscombe@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":547841,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grams, Paul E. 0000-0002-0873-0708 pgrams@usgs.gov","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":1830,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","email":"pgrams@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":547842,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kaplinski, Matthew","contributorId":14917,"corporation":false,"usgs":true,"family":"Kaplinski","given":"Matthew","affiliations":[],"preferred":false,"id":547843,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tusso, Robert B. 0000-0001-7541-3713 rtusso@usgs.gov","orcid":"https://orcid.org/0000-0001-7541-3713","contributorId":4079,"corporation":false,"usgs":true,"family":"Tusso","given":"Robert","email":"rtusso@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":547844,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rubin, David M. 0000-0003-1169-1452 drubin@usgs.gov","orcid":"https://orcid.org/0000-0003-1169-1452","contributorId":3159,"corporation":false,"usgs":true,"family":"Rubin","given":"David","email":"drubin@usgs.gov","middleInitial":"M.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":547845,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70155988,"text":"70155988 - 2015 - Glaciers and ice caps outside Greenland","interactions":[],"lastModifiedDate":"2018-07-07T18:06:40","indexId":"70155988","displayToPublicDate":"2015-08-13T16:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1112,"text":"Bulletin of the American Meteorological Society","onlineIssn":"1520-0477","printIssn":"0003-0007","active":true,"publicationSubtype":{"id":10}},"title":"Glaciers and ice caps outside Greenland","docAbstract":"Mountain glaciers and ice caps cover an area of over 400 000 km2 in the Arctic, and are a major influence on global sea level (Gardner et al. 2011, 2013; Jacob et al. 2012). They gain mass by snow accumulation and lose mass by meltwater runoff. Where they terminate in water (ocean or lake), they also lose mass by iceberg calving. The climatic mass balance (Bclim, the difference between annual snow accumulation and annual meltwater runoff) is a widely used index of how glaciers respond to climate variability and change. The total mass balance (ΔM) is defined as the difference between annual snow accumulation and annual mass losses (by iceberg calving plus runoff).
","language":"English","publisher":"American Meteorological Society","publisherLocation":"Washington, D.C.","usgsCitation":"Sharp, M., Wolken, G., Burgess, D., Cogley, J., Copland, L., Thomson, L., Arendt, A., Wouters, B., Kohler, J., Andreassen, L.M., O’Neel, S., and Pelto, M., 2015, Glaciers and ice caps outside Greenland: Bulletin of the American Meteorological Society, v. 96, no. 7, p. S135-S137.","productDescription":"Sxvi., S267","startPage":"S135","endPage":"S137","numberOfPages":"288","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063693","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":306714,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":306713,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www2.ametsoc.org/ams/index.cfm/publications/bulletin-of-the-american-meteorological-society-bams/state-of-the-climate/"}],"volume":"96","issue":"7","edition":"Supplement","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55cdb1abe4b08400b1fe13a5","contributors":{"authors":[{"text":"Sharp, Marin","contributorId":146359,"corporation":false,"usgs":false,"family":"Sharp","given":"Marin","email":"","affiliations":[{"id":12799,"text":"University of Alberta, Edmonton, Alberta, Canada","active":true,"usgs":false}],"preferred":false,"id":567562,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolken, G.","contributorId":146508,"corporation":false,"usgs":false,"family":"Wolken","given":"G.","email":"","affiliations":[],"preferred":false,"id":568070,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burgess, D.","contributorId":146509,"corporation":false,"usgs":false,"family":"Burgess","given":"D.","email":"","affiliations":[],"preferred":false,"id":568071,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cogley, J.G.","contributorId":58549,"corporation":false,"usgs":true,"family":"Cogley","given":"J.G.","email":"","affiliations":[],"preferred":false,"id":568072,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Copland, L.","contributorId":146510,"corporation":false,"usgs":false,"family":"Copland","given":"L.","affiliations":[],"preferred":false,"id":568073,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thomson, L.","contributorId":146511,"corporation":false,"usgs":false,"family":"Thomson","given":"L.","email":"","affiliations":[],"preferred":false,"id":568074,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Arendt, A.","contributorId":146512,"corporation":false,"usgs":false,"family":"Arendt","given":"A.","email":"","affiliations":[],"preferred":false,"id":568075,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wouters, B.","contributorId":146513,"corporation":false,"usgs":false,"family":"Wouters","given":"B.","email":"","affiliations":[],"preferred":false,"id":568076,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kohler, J.","contributorId":66476,"corporation":false,"usgs":true,"family":"Kohler","given":"J.","email":"","affiliations":[],"preferred":false,"id":568077,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Andreassen, L. M.","contributorId":146514,"corporation":false,"usgs":false,"family":"Andreassen","given":"L.","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":568078,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"O’Neel, Shad 0000-0002-9185-0144 soneel@usgs.gov","orcid":"https://orcid.org/0000-0002-9185-0144","contributorId":166740,"corporation":false,"usgs":true,"family":"O’Neel","given":"Shad","email":"soneel@usgs.gov","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":567561,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Pelto, M.","contributorId":146515,"corporation":false,"usgs":false,"family":"Pelto","given":"M.","affiliations":[],"preferred":false,"id":568079,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70156021,"text":"70156021 - 2015 - Gene transcription in polar bears (Ursus maritimus) from disparate populations","interactions":[],"lastModifiedDate":"2015-08-13T14:42:28","indexId":"70156021","displayToPublicDate":"2015-08-13T15:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3093,"text":"Polar Biology","active":true,"publicationSubtype":{"id":10}},"title":"Gene transcription in polar bears (Ursus maritimus) from disparate populations","docAbstract":"Polar bears in the Beaufort (SB) and Chukchi (CS) Seas experience different environments due primarily to a longer history of sea ice loss in the Beaufort Sea. Ecological differences have been identified as a possible reason for the generally poorer body condition and reproduction of Beaufort polar bears compared to those from the Chukchi, but the influence of exposure to other stressors remains unknown. We use molecular technology, quantitative PCR, to identify gene transcription differences among polar bears from the Beaufort and Chukchi Seas as well as captive healthy polar bears. We identified significant transcriptional differences among a priori groups (i.e., captive bears, SB 2012, SB 2013, CS 2013) for ten of the 14 genes of interest (i.e., CaM, HSP70, CCR3, TGFβ, COX2, THRα, T-bet, Gata3, CD69, and IL17); transcription levels of DRβ, IL1β, AHR, and Mx1 did not differ among groups. Multivariate analysis also demonstrated separation among the groups of polar bears. Specifically, we detected transcript profiles consistent with immune function impairment in polar bears from the Beaufort Sea, when compared with Chukchi and captive polar bears. Although there is no strong indication of differential exposure to contaminants or pathogens between CS and SB bears, there are clearly differences in important transcriptional responses between populations. Further investigation is warranted to refine interpretation of potential effects of described stress-related conditions for the SB population.
","language":"English","publisher":"Springer","publisherLocation":"Heidelberg, Germany","doi":"10.1007/s00300-015-1705-0","usgsCitation":"Bowen, L., Miles, A.K., Waters-Dynes, S.C., Meyerson, R., Rode, K.D., and Atwood, T.C., 2015, Gene transcription in polar bears (Ursus maritimus) from disparate populations: Polar Biology, v. 38, no. 9, p. 1413-1427, https://doi.org/10.1007/s00300-015-1705-0.","productDescription":"15 p.","startPage":"1413","endPage":"1427","numberOfPages":"15","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2011-03-01","temporalEnd":"2012-12-31","ipdsId":"IP-064965","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":306680,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","issue":"9","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2015-04-26","publicationStatus":"PW","scienceBaseUri":"55cdb1abe4b08400b1fe13a3","contributors":{"authors":[{"text":"Bowen, Lizabeth 0000-0001-9115-4336 lbowen@usgs.gov","orcid":"https://orcid.org/0000-0001-9115-4336","contributorId":4539,"corporation":false,"usgs":true,"family":"Bowen","given":"Lizabeth","email":"lbowen@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":567683,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miles, A. Keith 0000-0002-3108-808X keith_miles@usgs.gov","orcid":"https://orcid.org/0000-0002-3108-808X","contributorId":196,"corporation":false,"usgs":true,"family":"Miles","given":"A.","email":"keith_miles@usgs.gov","middleInitial":"Keith","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":567684,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waters-Dynes, Shannon C. 0000-0002-9707-4684 swaters@usgs.gov","orcid":"https://orcid.org/0000-0002-9707-4684","contributorId":5826,"corporation":false,"usgs":true,"family":"Waters-Dynes","given":"Shannon","email":"swaters@usgs.gov","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":567685,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meyerson, Randi","contributorId":146389,"corporation":false,"usgs":false,"family":"Meyerson","given":"Randi","email":"","affiliations":[{"id":16683,"text":"Toledo Zoo, Toledo, OH","active":true,"usgs":false}],"preferred":false,"id":567686,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":567687,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":567688,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70156015,"text":"70156015 - 2015 - The influence of grain size, grain color, and suspended-sediment concentration on light attenuation: why fine-grained terrestrial sediment is bad for coral reef ecosystems","interactions":[],"lastModifiedDate":"2015-08-13T14:35:48","indexId":"70156015","displayToPublicDate":"2015-08-13T15:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1338,"text":"Coral Reefs","active":true,"publicationSubtype":{"id":10}},"title":"The influence of grain size, grain color, and suspended-sediment concentration on light attenuation: why fine-grained terrestrial sediment is bad for coral reef ecosystems","docAbstract":"Sediment has been shown to be a major stressor to coral reefs globally. Although many researchers have tested the impact of sedimentation on coral reef ecosystems in both the laboratory and the field and some have measured the impact of suspended sediment on the photosynthetic response of corals, there has yet to be a detailed investigation on how properties of the sediment itself can affect light availability for photosynthesis. We show that finer-grained and darker-colored sediment at higher suspended-sediment concentrations attenuates photosynthetically active radiation (PAR) significantly more than coarser, lighter-colored sediment at lower concentrations and provide PAR attenuation coefficients for various grain sizes, colors, and suspended-sediment concentrations that are needed for biophysical modeling. Because finer-grained sediment particles settle more slowly and are more susceptible to resuspension, they remain in the water column longer, thus causing greater net impact by reducing light essential for photosynthesis over a greater duration. This indicates that coral reef monitoring studies investigating sediment impacts should concentrate on measuring fine-grained lateritic and volcanic soils, as opposed to coarser-grained siliceous and carbonate sediment. Similarly, coastal restoration efforts and engineering solutions addressing long-term coral reef ecosystem health should focus on preferentially retaining those fine-grained soils rather than coarse silt and sand particles.
","language":"English","publisher":"Springer","publisherLocation":"Heidelberg, Germany","doi":"10.1007/s00338-015-1268-0","usgsCitation":"Storlazzi, C.D., Norris, B., and Rosenberger, K.J., 2015, The influence of grain size, grain color, and suspended-sediment concentration on light attenuation: why fine-grained terrestrial sediment is bad for coral reef ecosystems: Coral Reefs, v. 34, no. 3, p. 967-975, https://doi.org/10.1007/s00338-015-1268-0.","productDescription":"9 p.","startPage":"967","endPage":"975","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060458","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":306678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-05-20","publicationStatus":"PW","scienceBaseUri":"55cdb1b1e4b08400b1fe13c7","contributors":{"authors":[{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":567667,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Norris, Benjamin","contributorId":65001,"corporation":false,"usgs":true,"family":"Norris","given":"Benjamin","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":567668,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosenberger, Kurt J. 0000-0002-5185-5776 krosenberger@usgs.gov","orcid":"https://orcid.org/0000-0002-5185-5776","contributorId":140453,"corporation":false,"usgs":true,"family":"Rosenberger","given":"Kurt","email":"krosenberger@usgs.gov","middleInitial":"J.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":567669,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70156005,"text":"70156005 - 2015 - Sensitivity of intermittent streams to climate variations in the USA","interactions":[],"lastModifiedDate":"2016-06-15T16:01:05","indexId":"70156005","displayToPublicDate":"2015-08-13T15:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Sensitivity of intermittent streams to climate variations in the USA","docAbstract":"There is a great deal of interest in the literature on streamflow changes caused by climate change because of the potential negative effects on aquatic biota and water supplies. Most previous studies have primarily focused on perennial streams, and there have been only a few studies examining the effect of climate variability on intermittent streams. Our objectives in this study were to (1) identify regions of similar zero-flow behavior, and (2) evaluate the sensitivity of intermittent streams to historical variability in climate in the United States. This study was carried out at 265 intermittent streams by evaluating: (1) correlations among time series of flow metrics (number of zero-flow events, the average of the central 50% and largest 10% of flows) with climate (magnitudes, durations and intensity), and (2) decadal changes in the seasonality and long-term trends of these flow metrics. Results identified five distinct seasonality patterns in the zero-flow events. In addition, strong associations between the low-flow metrics and historical changes in climate were found. The decadal analysis suggested no significant seasonal shifts or decade-to-decade trends in the low-flow metrics. The lack of trends or changes in seasonality is likely due to unchanged long-term patterns in precipitation over the time period examined.
","language":"English","publisher":"John Wiley & Sons","publisherLocation":"Chichester, UK","doi":"10.1002/rra.2939","usgsCitation":"Eng, K., Wolock, D.M., and Dettinger, M., 2015, Sensitivity of intermittent streams to climate variations in the USA: River Research and Applications, v. 32, no. 5, p. 885-895, https://doi.org/10.1002/rra.2939.","productDescription":"11 p.","startPage":"885","endPage":"895","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066904","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":306676,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.1064453125,\n 49.009050809382046\n ],\n [\n -104.0625,\n 41.0130657870063\n ],\n [\n -102.1728515625,\n 40.94671366508002\n ],\n [\n -102.0849609375,\n 36.98500309285596\n ],\n [\n -114.12597656249999,\n 37.020098201368114\n ],\n [\n -117.5537109375,\n 37.3002752813443\n ],\n [\n -120.14648437499999,\n 39.06184913429154\n ],\n [\n -119.92675781249999,\n 41.96765920367816\n ],\n [\n -124.27734374999999,\n 42.032974332441405\n ],\n [\n -124.45312499999999,\n 40.212440718286466\n ],\n [\n -121.6845703125,\n 36.13787471840729\n ],\n [\n -118.69628906249999,\n 34.08906131584996\n ],\n [\n -117.158203125,\n 32.54681317351514\n ],\n [\n -114.82910156249999,\n 32.694865977875075\n ],\n [\n -111.09374999999999,\n 31.31610138349565\n ],\n [\n -108.19335937499999,\n 31.27855085894653\n ],\n [\n -108.19335937499999,\n 31.80289258670676\n ],\n [\n -106.34765625,\n 31.80289258670676\n ],\n [\n -104.80957031249999,\n 30.29701788337205\n ],\n [\n -104.32617187499999,\n 29.458731185355344\n ],\n [\n -103.095703125,\n 28.9600886880068\n ],\n [\n -102.39257812499999,\n 29.84064389983441\n ],\n [\n -101.77734374999999,\n 29.84064389983441\n ],\n [\n -100.634765625,\n 29.036960648558267\n ],\n [\n -99.49218749999999,\n 27.488781168937997\n ],\n [\n -99.1845703125,\n 26.352497858154\n ],\n [\n -97.119140625,\n 25.878994400196202\n ],\n [\n -97.42675781249999,\n 27.254629577800088\n ],\n [\n -96.328125,\n 28.613459424004414\n ],\n [\n -94.74609375,\n 29.305561325527698\n ],\n [\n -91.93359375,\n 29.57345707301757\n ],\n [\n -89.033203125,\n 28.998531814051795\n ],\n [\n -89.7802734375,\n 31.015278981711266\n ],\n [\n -91.3623046875,\n 30.93992433102347\n ],\n [\n -91.23046875,\n 32.99023555965106\n ],\n [\n -89.5166015625,\n 36.527294814546245\n ],\n [\n -83.60595703125,\n 36.63316209558658\n ],\n [\n -81.97998046875,\n 37.52715361723378\n ],\n [\n -82.6611328125,\n 38.22091976683121\n ],\n [\n -82.529296875,\n 38.444984668894705\n ],\n [\n -81.4306640625,\n 39.41922073655956\n ],\n [\n -80.85937499999999,\n 39.67337039176558\n ],\n [\n -80.5517578125,\n 40.212440718286466\n ],\n [\n -80.5078125,\n 41.86956082699455\n ],\n [\n -87.56103515625,\n 41.85319643776675\n ],\n [\n -87.8466796875,\n 42.52069952914966\n ],\n [\n -90.68115234375,\n 42.50450285299051\n ],\n [\n -91.23046875,\n 43.46886761482925\n ],\n [\n -96.17431640625,\n 43.48481212891603\n ],\n [\n -96.52587890625,\n 48.980216985374994\n ],\n [\n -104.1064453125,\n 49.009050809382046\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-07","publicationStatus":"PW","scienceBaseUri":"55cdb1b0e4b08400b1fe13c1","contributors":{"authors":[{"text":"Eng, Ken 0000-0001-6838-5849 keng@usgs.gov","orcid":"https://orcid.org/0000-0001-6838-5849","contributorId":3580,"corporation":false,"usgs":true,"family":"Eng","given":"Ken","email":"keng@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":567628,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolock, David M. 0000-0002-6209-938X dwolock@usgs.gov","orcid":"https://orcid.org/0000-0002-6209-938X","contributorId":540,"corporation":false,"usgs":true,"family":"Wolock","given":"David","email":"dwolock@usgs.gov","middleInitial":"M.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":567629,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dettinger, Mike 0000-0002-7509-7332 mddettin@usgs.gov","orcid":"https://orcid.org/0000-0002-7509-7332","contributorId":859,"corporation":false,"usgs":true,"family":"Dettinger","given":"Mike","email":"mddettin@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":false,"id":567630,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155814,"text":"70155814 - 2015 - Natural recharge estimation and uncertainty analysis of an adjudicated groundwater basin using a regional-scale flow and subsidence model (Antelope Valley, California, USA)","interactions":[],"lastModifiedDate":"2015-08-13T10:33:01","indexId":"70155814","displayToPublicDate":"2015-08-13T11:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Natural recharge estimation and uncertainty analysis of an adjudicated groundwater basin using a regional-scale flow and subsidence model (Antelope Valley, California, USA)","docAbstract":"Groundwater has provided 50–90 % of the total water supply in Antelope Valley, California (USA). The associated groundwater-level declines have led the Los Angeles County Superior Court of California to recently rule that the Antelope Valley groundwater basin is in overdraft, i.e., annual pumpage exceeds annual recharge. Natural recharge consists primarily of mountain-front recharge and is an important component of the total groundwater budget in Antelope Valley. Therefore, natural recharge plays a major role in the Court’s decision. The exact quantity and distribution of natural recharge is uncertain, with total estimates from previous studies ranging from 37 to 200 gigaliters per year (GL/year). In order to better understand the uncertainty associated with natural recharge and to provide a tool for groundwater management, a numerical model of groundwater flow and land subsidence was developed. The transient model was calibrated using PEST with water-level and subsidence data; prior information was incorporated through the use of Tikhonov regularization. The calibrated estimate of natural recharge was 36 GL/year, which is appreciably less than the value used by the court (74 GL/year). The effect of parameter uncertainty on the estimation of natural recharge was addressed using the Null-Space Monte Carlo method. A Pareto trade-off method was also used to portray the reasonableness of larger natural recharge rates. The reasonableness of the 74 GL/year value and the effect of uncertain pumpage rates were also evaluated. The uncertainty analyses indicate that the total natural recharge likely ranges between 34.5 and 54.3 GL/year.
","language":"English","publisher":"Springer","publisherLocation":"Heidelberg, Germany","doi":"10.1007/s10040-015-1281-y","usgsCitation":"Siade, A.J., Nishikawa, T., and Martin, P., 2015, Natural recharge estimation and uncertainty analysis of an adjudicated groundwater basin using a regional-scale flow and subsidence model (Antelope Valley, California, USA): Hydrogeology Journal, v. 23, no. 6, p. 1267-1291, https://doi.org/10.1007/s10040-015-1281-y.","productDescription":"25 p.","startPage":"1267","endPage":"1291","numberOfPages":"25","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-037195","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":471880,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-015-1281-y","text":"Publisher Index Page"},{"id":306633,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Antelope Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.19503784179688,\n 35.16819542676796\n ],\n [\n -118.90228271484374,\n 34.84536693184099\n ],\n [\n -118.91189575195312,\n 34.78222760653013\n ],\n [\n -117.45620727539062,\n 34.30260622622907\n ],\n [\n -117.54959106445312,\n 35.163704834815874\n ],\n [\n -118.19503784179688,\n 35.16819542676796\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","issue":"6","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-24","publicationStatus":"PW","scienceBaseUri":"55cdb1ade4b08400b1fe13b1","contributors":{"authors":[{"text":"Siade, Adam J. asiade@usgs.gov","contributorId":1533,"corporation":false,"usgs":true,"family":"Siade","given":"Adam","email":"asiade@usgs.gov","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566453,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nishikawa, Tracy 0000-0002-7348-3838 tnish@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-3838","contributorId":1515,"corporation":false,"usgs":true,"family":"Nishikawa","given":"Tracy","email":"tnish@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566455,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Peter pmmartin@usgs.gov","contributorId":799,"corporation":false,"usgs":true,"family":"Martin","given":"Peter","email":"pmmartin@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566454,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155830,"text":"sir20145167 - 2015 - Numerical simulation of groundwater flow, resource optimization, and potential effects of prolonged drought for the Citizen Potawatomi Nation Tribal Jurisdictional Area, central Oklahoma","interactions":[],"lastModifiedDate":"2018-02-05T15:03:57","indexId":"sir20145167","displayToPublicDate":"2015-08-13T10:30:00","publicationYear":"2015","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":"2014-5167","title":"Numerical simulation of groundwater flow, resource optimization, and potential effects of prolonged drought for the Citizen Potawatomi Nation Tribal Jurisdictional Area, central Oklahoma","docAbstract":"A hydrogeological study including two numerical groundwater-flow models was completed for the Citizen Potawatomi Nation Tribal Jurisdictional Area of central Oklahoma. One numerical groundwater-flow model, the Citizen Potawatomi Nation model, encompassed the jurisdictional area and was based on the results of a regional-scale hydrogeological study and numerical groundwater flow model of the Central Oklahoma aquifer, which had a geographic extent that included the Citizen Potawatomi Nation Tribal Jurisdictional Area. The Citizen Potawatomi Nation numerical groundwater-flow model included alluvial aquifers not in the original model and improved calibration using automated parameter-estimation techniques. The Citizen Potawatomi Nation numerical groundwater-flow model was used to analyze the groundwater-flow system and the effects of drought on the volume of groundwater in storage and streamflow in the North Canadian River. A more detailed, local-scale inset model was constructed from the Citizen Potawatomi Nation model to estimate available groundwater resources for two Citizen Potawatomi Nation economic development zones near the North Canadian River, the geothermal supply area and the Iron Horse Industrial Park.
\nGroundwater pumping rates at potential well locations were optimized using the most recent version of the U.S. Geological Survey Groundwater-Management Process for MODFLOW. The objectives of optimization were to determine if a total pumping rate of 500 gallons per minute could be pumped from 5 wells at the geothermal supply area and to maximize discharge from 16 wells at the Iron Horse Industrial Park without exceeding specified head drawdown constraints at the pumping wells and thus prevent groundwater depletion.
\nThe inset model was used to estimate North Canadian River streamflow depletion caused by optimized pumping at the Iron Horse Industrial Park because water quality was a concern, and the river may have degraded water quality compared to water in other parts of the alluvial aquifer. The fate of streamflow that infiltrates into groundwater because of pumping was not directly determined, but it was assumed that this water could end up in the well discharge, and was considered to be a maximum proportion of well discharge derived from the North Canadian River.
\nThe total optimized continuous pumping rate from five managed wells at the geothermal supply area was 638 gallons per minute, which exceeded the target pumping rate of 500 gallons per minute. The total continuous pumping rate from 16 wells at the Iron Horse Industrial Park was 1,472 gallons per minute, which induced stream infiltration of approximately 4.1 gallons per minute (approximately 0.3 percent of the total well discharge) from the North Canadian River.
\nTo estimate the effects of drought on water resources in the Citizen Potawatomi Nation Tribal Jurisdictional Area, a hypothetical 10-year drought during which precipitation would decrease by 50 percent was simulated by decreasing model groundwater recharge by the same proportion for the period 1990–2000 of the transient model. The effects of the drought were estimated by calculating the change in the volume of groundwater storage and groundwater flow to streams at the end of the drought period, and the change in simulated streamflow in the North Canadian River at the streamflow-gaging station at Shawnee, Okla., during and after the drought.
\nThe hypothetical decrease in recharge during the simulated drought caused groundwater in storage over the entire model in the study area to decrease by 361,500 acre-feet (14,100 acre-feet in the North Canadian River alluvial aquifer and 347,400 acre-feet in the Central Oklahoma aquifer), or approximately 0.2 percent of the total groundwater in storage over the drought period. This small percentage of groundwater loss showed that the Central Oklahoma aquifer as a bedrock aquifer has relatively low rates of recharge from the surface relative to the approximate storage. The budget for base flow to the North Canadian River indicated that the change in groundwater flow to the North Canadian River decreased during the 10-year drought by 386,500 acre-feet, or 37 percent. In all other parts of the Citizen Potawatomi Nation Tribal Jurisdictional Area, base flow decreased by 292,000 acre-feet, or 28 percent. Streamflow in the North Canadian River at the streamflow-gaging station at Shawnee, Okla., decreased during the hypothetical drought by as much as 28 percent, and the mean change in streamflow decreased as much as 16 percent. Streamflow at the Shawnee streamflow-gaging station did not recover to nondrought conditions until about 3 years after the simulated drought ended, during the relatively wet year of 2007.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145167","collaboration":"Prepared in cooperation with the Citizen Potawatomi Nation","usgsCitation":"Ryter, D.R., Kunkel, C.D., Peterson, S.M., and Traylor, J.P., 2018, Numerical simulation of groundwater flow, resource optimization, and potential effects of prolonged drought for the Citizen Potawatomi Nation Tribal Jurisdictional Area, central Oklahoma (ver. 1.2, February 2018), U.S. Geological Survey Scientific Investigations Report 2014–5167, 27 p., https://doi.org/10.3133/sir20145167.","productDescription":"viii, 27 p.","numberOfPages":"39","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058249","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":306619,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2014/5167/coverthb4.jpg"},{"id":350836,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5167/sir20145167.pdf","text":"Report","size":"1.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2014–5167"},{"id":350837,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2014/5167/versionHist_v1_2.txt","text":"Version History","size":"1.21 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2014–5167 Version History"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Citizen Potawatomi National Tribal Jurisdictional Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.30453491210938,\n 35.5802672918083\n ],\n [\n -97.28805541992188,\n 34.96137177524017\n ],\n [\n -97.25784301757812,\n 34.951241964789645\n ],\n [\n -97.17544555664062,\n 34.928726792983845\n ],\n [\n -97.08892822265625,\n 34.929852698375804\n ],\n [\n -97.00790405273438,\n 34.90620544067929\n ],\n [\n -96.93511962890624,\n 34.96812428662085\n ],\n [\n -96.86920166015625,\n 34.92760087214065\n ],\n [\n -96.82525634765625,\n 34.94448806230625\n ],\n [\n -96.79641723632812,\n 34.939985151560435\n ],\n [\n -96.79229736328125,\n 34.8971951696173\n ],\n [\n -96.75796508789062,\n 34.90057413710918\n ],\n [\n -96.734619140625,\n 34.86565143737161\n ],\n [\n -96.71539306640625,\n 34.858890491257824\n ],\n [\n -96.734619140625,\n 35.41479572901859\n ],\n [\n -96.7510986328125,\n 35.40360292969232\n ],\n [\n -96.78543090820312,\n 35.40696093270201\n ],\n [\n -96.8280029296875,\n 35.42374884923695\n ],\n [\n -96.866455078125,\n 35.431582013221295\n ],\n [\n -96.87332153320311,\n 35.39912537474419\n ],\n [\n -96.84997558593749,\n 35.37337460834958\n ],\n [\n -96.82662963867186,\n 35.36105611877928\n ],\n [\n -96.82937622070312,\n 35.345375322554474\n ],\n [\n -96.86508178710936,\n 35.35321610123821\n ],\n [\n -96.87744140625,\n 35.33641350071231\n ],\n [\n -96.8994140625,\n 35.31736632923788\n ],\n [\n -96.9598388671875,\n 35.33641350071231\n ],\n [\n -97.00790405273438,\n 35.36105611877928\n ],\n [\n -97.04086303710938,\n 35.394647571120544\n ],\n [\n -97.04635620117188,\n 35.428225036228106\n ],\n [\n -97.10678100585936,\n 35.460669951495305\n ],\n [\n -97.15072631835938,\n 35.47856499535729\n ],\n [\n -97.15896606445312,\n 35.49757411565533\n ],\n [\n -97.16720581054688,\n 35.4925427272974\n ],\n [\n -97.18231201171875,\n 35.50372316239306\n ],\n [\n -97.20634460449219,\n 35.50316417759601\n ],\n [\n -97.23381042480469,\n 35.51434313431818\n ],\n [\n -97.2454833984375,\n 35.53054985697859\n ],\n [\n -97.24891662597656,\n 35.55178129792757\n ],\n [\n -97.28118896484375,\n 35.58808520476325\n ],\n [\n -97.30316162109375,\n 35.595902354416566\n ],\n [\n -97.30453491210938,\n 35.5802672918083\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted August 13, 2015; Version 1.1: February 24, 2016; Version 1.2: February 5, 2018","contact":"Director, Oklahoma Water Science Center
U.S. Geological Survey
202 NW 66th, Bldg 7
Oklahoma City, OK 73116
http://ok.water.usgs.gov/
Climate change predictions include warming and drying trends, which are expected to be particularly pronounced in the southwestern United States. In this region, grassland dynamics are tightly linked to available moisture, yet it has proven difficult to resolve what aspects of climate drive vegetation change. In part, this is because it is unclear how heterogeneity in soils affects plant responses to climate. Here, we combine climate and soil properties with a mechanistic soil water model to explain temporal fluctuations in perennial grass cover, quantify where and the degree to which incorporating soil water dynamics enhances our ability to understand temporal patterns, and explore the potential consequences of climate change by assessing future trajectories of important climate and soil water variables. Our analyses focused on long-term (20–56 years) perennial grass dynamics across the Colorado Plateau, Sonoran, and Chihuahuan Desert regions. Our results suggest that climate variability has negative effects on grass cover, and that precipitation subsidies that extend growing seasons are beneficial. Soil water metrics, including the number of dry days and availability of water from deeper (>30 cm) soil layers, explained additional grass cover variability. While individual climate variables were ranked as more important in explaining grass cover, collectively soil water accounted for 40–60% of the total explained variance. Soil water conditions were more useful for understanding the responses of C3 than C4 grass species. Projections of water balance variables under climate change indicate that conditions that currently support perennial grasses will be less common in the future, and these altered conditions will be more pronounced in the Chihuahuan Desert and Colorado Plateau. We conclude that incorporating multiple aspects of climate and accounting for soil variability can improve our ability to understand patterns, identify areas of vulnerability, and predict the future of desert grasslands.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13043","usgsCitation":"Gremer, J., Bradford, J.B., Munson, S.M., and Duniway, M.C., 2015, Desert grassland responses to climate and soil moisture suggest divergent vulnerabilities across the southwestern United States: Global Change Biology, v. 21, no. 11, p. 4049-4062, https://doi.org/10.1111/gcb.13043.","productDescription":"14 p.","startPage":"4049","endPage":"4062","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063581","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":471882,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://www.escholarship.org/uc/item/8n66p641","text":"External Repository"},{"id":306709,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n 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jbradford@usgs.gov","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":611,"corporation":false,"usgs":true,"family":"Bradford","given":"John","email":"jbradford@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":566833,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":566835,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":566836,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70155980,"text":"70155980 - 2015 - Assessing juvenile salmon rearing habitat and associated predation risk in a lower Snake River reservoir","interactions":[],"lastModifiedDate":"2016-12-19T11:31:31","indexId":"70155980","displayToPublicDate":"2015-08-13T03:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Assessing juvenile salmon rearing habitat and associated predation risk in a lower Snake River reservoir","docAbstract":"Subyearling fall Chinook salmon (Oncorhynchus tshawytscha) in the Columbia River basin exhibit a transient rearing strategy and depend on connected shoreline habitats during freshwater rearing. Impoundment has greatly reduced the amount of shallow-water rearing habitat that is exacerbated by the steep topography of reservoirs. Periodic dredging creates opportunities to strategically place spoils to increase the amount of shallow-water habitat for subyearlings while at the same time reducing the amount of unsuitable area that is often preferred by predators. We assessed the amount and spatial arrangement of subyearling rearing habitat in Lower Granite Reservoir on the Snake River to guide future habitat improvement efforts. A spatially explicit habitat assessment was conducted using physical habitat data, two-dimensional hydrodynamic modelling and a statistical habitat model in a geographic information system framework. We used field collections of subyearlings and a common predator [smallmouth bass (Micropterus dolomieu)] to draw inferences about predation risk within specific habitat types. Most of the high-probability rearing habitat was located in the upper half of the reservoir where gently sloping landforms created low lateral bed slopes and shallow-water habitats. Only 29% of shorelines were predicted to be suitable (probability >0.5) for subyearlings, and the occurrence of these shorelines decreased in a downstream direction. The remaining, less suitable areas were composed of low-probability habitats in unmodified (25%) and riprapped shorelines (46%). As expected, most subyearlings were found in high-probability habitat, while most smallmouth bass were found in low-probability locations. However, some subyearlings were found in low-probability habitats, such as riprap, where predation risk could be high. Given their transient rearing strategy and dependence on shoreline habitats, subyearlings could benefit from habitat creation efforts in the lower reservoir where high-probability habitat is generally lacking. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.2934","usgsCitation":"Tiffan, K.F., Hatten, J.R., and Trachtenbarg, D.A., 2015, Assessing juvenile salmon rearing habitat and associated predation risk in a lower Snake River reservoir: River Research and Applications, v. 32, no. 5, p. 1030-1038, https://doi.org/10.1002/rra.2934.","productDescription":"9 p.","startPage":"1030","endPage":"1038","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064916","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":306672,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lower Snake river reservior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.69653320312499,\n 46.67582559793001\n ],\n [\n -117.53173828125,\n 46.66074749832068\n ],\n [\n -117.40264892578124,\n 46.6268063953552\n ],\n [\n -117.29553222656249,\n 46.53052428878426\n ],\n [\n -117.21313476562499,\n 46.411351502899215\n ],\n [\n -117.07305908203124,\n 46.39998810407942\n ],\n [\n -117.07855224609376,\n 46.34313560260196\n ],\n [\n -117.0098876953125,\n 46.28622391806706\n ],\n [\n -116.98242187499999,\n 46.214050815339526\n ],\n [\n -116.97418212890625,\n 46.11513371326539\n ],\n [\n -116.91375732421875,\n 46.128459837044915\n ],\n [\n -116.93847656250001,\n 46.26534147068603\n ],\n [\n -116.99615478515624,\n 46.36588370484979\n ],\n [\n -116.98242187499999,\n 46.40188216826328\n ],\n [\n -116.82861328125001,\n 46.430285240839964\n ],\n [\n -116.84234619140624,\n 46.464349400461124\n ],\n [\n -117.103271484375,\n 46.45110475854117\n ],\n [\n -117.26257324218749,\n 46.56452573114373\n ],\n [\n -117.3065185546875,\n 46.62492015414768\n ],\n [\n -117.3944091796875,\n 46.685247274319565\n ],\n [\n -117.46307373046874,\n 46.717268685073954\n ],\n [\n -117.69653320312499,\n 46.72291755083757\n ],\n [\n -117.69653320312499,\n 46.67582559793001\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-03","publicationStatus":"PW","scienceBaseUri":"55cdb1a7e4b08400b1fe139d","contributors":{"authors":[{"text":"Tiffan, Kenneth F. 0000-0002-5831-2846 ktiffan@usgs.gov","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":3200,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","email":"ktiffan@usgs.gov","middleInitial":"F.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":567529,"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":567530,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Trachtenbarg, David A","contributorId":146351,"corporation":false,"usgs":false,"family":"Trachtenbarg","given":"David","email":"","middleInitial":"A","affiliations":[{"id":16680,"text":"U.S. Army Corps of Engineers, Walla Walla District, Walla Walla, WA 99362","active":true,"usgs":false}],"preferred":false,"id":567531,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155509,"text":"sir20155106 - 2015 - Hydrologic budget and conditions of Permian, Pennsylvanian, and Mississippian aquifers in the Appalachian Plateaus physiographic province","interactions":[],"lastModifiedDate":"2015-10-26T14:28:11","indexId":"sir20155106","displayToPublicDate":"2015-08-12T15:45:00","publicationYear":"2015","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":"2015-5106","title":"Hydrologic budget and conditions of Permian, Pennsylvanian, and Mississippian aquifers in the Appalachian Plateaus physiographic province","docAbstract":"In response to challenges to groundwater availability posed by historic land-use practices, expanding development of hydrocarbon resources, and drought, the U.S. Geological Survey Groundwater Resources Program began a regional assessment of the Appalachian Plateaus aquifers in 2013 that incorporated a hydrologic landscape approach to estimate all components of the hydrologic system: surface runoff, base flow from groundwater, and interaction with atmospheric water (precipitation and evapotranspiration). This assessment was intended to complement other Federal and State investigations and provide foundational groundwater-related datasets in the Appalachian Plateaus.
\nA regional Soil-Water-Balance model was constructed for a 160,000-square-mile study area that extended to the topographic divide of all streams originating outside but flowing into areas underlain by Appalachian Plateaus aquifers. The model incorporated soil, landscape, and climate variables to estimate an annual water budget for the 32-year period from 1980 to 2011 and was calibrated using base-flow data estimated by hydrograph separation techniques from 20 streamflow gaging stations across the study area. Over this period, an average of 47 inches per year (in/yr) of precipitation fell on Appalachian Plateaus aquifers. Simulations from the regional Soil-Water-Balance model indicate that only 19 percent of the precipitation or an average 9 in/yr recharged aquifers, and 19 percent resulted in surface runoff to streams. The remaining 62 percent, an average of 27 in/yr of water, was returned to the atmosphere via evapotranspiration. Because withdrawals from aquifers due to pumping equated to less than 1 percent of the water budget, differences in predevelopment and postdevelopment regional water budgets of the Appalachian Plateaus were minimal. Storage changes caused by filling of abandoned coal-mine aquifers and long-term differences in aquifer storage resulting from climate fluctuations constitute a small portion of the overall water budget.
\nThe percentage of precipitation that results in recharge, runoff, or evapotranspiration from the landscape varies annually by up to a factor of two depending on temporal changes in prevailing climate conditions and spatial changes in basin characteristics, precipitation patterns, and sources of atmospheric moisture over a large study area. A comparison of water-budget estimates from the regional Soil-Water-Balance model for a dry year (1988) and wet year (2004) showed that evapotranspiration accounts for most of the annual differences in precipitation. As a portion of annual precipitation, evapotranspiration ranged from 69 percent (dry year) to 52 percent (wet year), a range four times greater than the 15 percent (dry year) to 18 percent (wet year) range estimated for recharge. Evapotranspiration as a percentage of precipitation peaks during dry periods, whereas base flow and runoff tend to reach minimum values. During wet periods, this relationship is reversed and base flow and runoff as a percentage of precipitation generally peak while evapotranspiration percentages reach minimum values. Annual recharge in the Appalachian Plateaus reaches a maximum at near 20 percent of annual precipitation, regardless of the severity of wet conditions.
\nHydrograph separation data from 849 streamflow gaging stations in the study area were used to assess trends in streamflow, base flow, surface runoff, and base-flow index, or ratio of base flow to streamflow, in the Appalachian Plateaus for the period from 1930 to 2011. Annual data anomalies for each of the four variables were individually defined as the annual standard deviation from the mean at all 849 streamflow gaging stations. Annual data anomalies confirm the close relation of annual precipitation to both base flow and runoff components of streamflow, and both components increased during the period of analysis. Around 1970, conditions shifted streamflow from values generally below to above long-term means. At a regional scale, increases in base flow account for most of these observed increases in mean annual streamflow. The independence of the base-flow index to annual climate trends indicate that changes in the components of streamflow of the Appalachian Plateaus are probably in response to shifts in seasonal precipitation or widespread land-use practices.
\nA subset of 77 index streamgages, defined as having 60 or more years of complete record between the years 1930 and 2011 with no more than 20 percent missing data, was selected to show spatial patterns of change in the water budget. Data from the index streamgages showed that the overall trends in base flow are dependent upon the period of evaluation. Long-term (1930–2011) increases in base flow were observed throughout the study area. For two shorter periods (1930–1969 and 1970–2011) trends in base flow were largely negative. In general, spatial patterns of change in streamflow, base flow, and runoff were mixed but generally consistent with prevailing climate patterns and land-use changes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155106","collaboration":"Groundwater Resources Program","usgsCitation":"McCoy, K.J., Yager, R.M., Nelms, D.L., Ladd, D.E., Monti, Jack, Jr., and Kozar, M.D., 2015, Hydrologic budget and conditions of Permian, Pennsylvanian, and Mississippian aquifers in the Appalachian Plateaus Physiographic Province (ver. 1.1, October 2015): U.S. Geological Survey Scientific Investigations Report 2015–5106, 77 p., https://dx.doi.org/10.3133/sir20155106.","productDescription":"vii, 77 p.","numberOfPages":"90","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060623","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":306582,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5106/sir20155106.pdf","text":"Report","size":"36.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5106"},{"id":306581,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5106/images/coverthb.jpg"},{"id":309929,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2015/5106/versionHist.txt","text":"October 26, 2015","size":"1.06 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2015-5106"}],"country":"United States","state":"Alabama, Kentucky, Maryland, Ohio, Pennslyvania, Virginia, Tennessee, West Virginia","otherGeospatial":"Mississippian aquifer, Pennsylvanian aquifer, Permian aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.31103515625,\n 41.705728515237524\n ],\n [\n -81.2109375,\n 41.83682786072714\n ],\n [\n -82.79296874999999,\n 41.36031866306708\n ],\n [\n -83.8037109375,\n 38.66835610151509\n ],\n [\n -86.98974609375,\n 34.97600151317591\n ],\n [\n -88.22021484375,\n 34.79576153473033\n ],\n [\n -88.39599609375,\n 32.62087018318113\n ],\n [\n -85.4736328125,\n 34.95799531086792\n ],\n [\n -83.3203125,\n 36.5978891330702\n ],\n [\n -80.22216796875,\n 37.474858084971046\n ],\n [\n -78.5302734375,\n 39.707186656826565\n ],\n [\n -76.31103515625,\n 41.705728515237524\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted August 13, 2015; Version 1.1: October 26, 2015","contact":"Director, Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
http://va.water.usgs.gov
Peak ground accelerations (PGAs) in the epicentral region of the 1811–1812 New Madrid, Missouri, earthquakes are inferred from liquefaction to have been no greater than ∼0.35g. PGA is inferred in an 11,380 km2 area in the Lower Mississippi Valley in Arkansas and Missouri where liquefaction was extensive in 1811–1812. PGA was inferred by applying liquefaction probability curves, which were originally developed for liquefaction hazard mapping, to detailed maps of liquefaction by Obermeier (1989). The low PGA is inferred because both a shallow (1.5 m deep) water table and a large moment magnitude (M 7.7) earthquake were assumed in the analysis. If a deep (5.0 m) water table and a small magnitude (M 6.8) earthquake are assumed, the maximum inferred PGA is 1.10g. Both inferred PGA values are based on an assumed and poorly constrained correction for sand aging. If an aging correction is not assumed, then the inferred PGA is no greater than 0.22g. A low PGA value may be explained by nonlinear site response. Soils in the study area have an averageVS30 of 220±15 m/s. A low inferred PGA is consistent with PGA values estimated from ground‐motion prediction equations that have been proposed for the New Madrid seismic zone when these estimates are corrected for nonlinear soil site effects. This application of liquefaction probability curves demonstrates their potential usefulness in paleoseismology.
","language":"English","publisher":"Seismological Society of Amercia","doi":"10.1785/0120130258","usgsCitation":"Holzer, T.L., Noce, T.E., and Bennett, M.J., 2015, Strong ground motion inferred from liquefaction caused by the 1811-1812 New Madrid, Missouri, earthquakes: Bulletin of the Seismological Society of America, v. 105, no. 5, p. 2589-2603, https://doi.org/10.1785/0120130258.","productDescription":"15 p.","startPage":"2589","endPage":"2603","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-045331","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":306610,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","city":"New Madrid","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.07989501953125,\n 36.93013456125331\n ],\n [\n -89.1815185546875,\n 36.9367208722872\n ],\n [\n -89.3682861328125,\n 36.89939091854292\n ],\n [\n -89.6484375,\n 36.87742358748459\n ],\n [\n -89.72808837890625,\n 36.79169061907076\n ],\n [\n -89.76654052734375,\n 36.39696752441779\n ],\n [\n -90.2801513671875,\n 36.39033486213652\n ],\n [\n -90.384521484375,\n 36.24870331653198\n ],\n [\n -90.47241210937499,\n 36.113471382052175\n ],\n [\n -90.4888916015625,\n 35.940212068887455\n ],\n [\n -90.50811767578125,\n 35.753199435570316\n ],\n [\n -90.50811767578125,\n 35.63720889099896\n ],\n [\n -90.49163818359375,\n 35.55010533588552\n ],\n [\n -90.31585693359375,\n 35.52104976129943\n ],\n [\n -89.88739013671874,\n 35.543401137387356\n ],\n [\n -89.813232421875,\n 35.54116627999815\n ],\n [\n -89.65118408203125,\n 35.808904044068626\n ],\n [\n -89.571533203125,\n 35.99800750540412\n ],\n [\n -89.50836181640625,\n 35.99578538642032\n ],\n [\n -89.4781494140625,\n 36.055760619006776\n ],\n [\n -89.55780029296875,\n 36.0824016199585\n ],\n [\n -89.46990966796875,\n 36.17779108329074\n ],\n [\n -89.23919677734375,\n 36.43896124085945\n ],\n [\n -89.09088134765625,\n 36.74548692469868\n ],\n [\n -89.07989501953125,\n 36.93013456125331\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"105","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-04","publicationStatus":"PW","scienceBaseUri":"55cc6024e4b08400b1fe0fbc","contributors":{"authors":[{"text":"Holzer, Thomas L. tholzer@usgs.gov","contributorId":2829,"corporation":false,"usgs":true,"family":"Holzer","given":"Thomas","email":"tholzer@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":548929,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Noce, Thomas E. tnoce@usgs.gov","contributorId":3174,"corporation":false,"usgs":true,"family":"Noce","given":"Thomas","email":"tnoce@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":548930,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bennett, Michael J. mjbennett@usgs.gov","contributorId":2783,"corporation":false,"usgs":true,"family":"Bennett","given":"Michael","email":"mjbennett@usgs.gov","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":548928,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155229,"text":"tm5A11 - 2015 - U.S. Geological Survey Noble Gas Laboratory’s standard operating procedures for the measurement of dissolved gas in water samples","interactions":[],"lastModifiedDate":"2015-08-12T16:00:22","indexId":"tm5A11","displayToPublicDate":"2015-08-12T13:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"5-A11","title":"U.S. Geological Survey Noble Gas Laboratory’s standard operating procedures for the measurement of dissolved gas in water samples","docAbstract":"This report addresses the standard operating procedures used by the U.S. Geological Survey’s Noble Gas Laboratory in Denver, Colorado, U.S.A., for the measurement of dissolved gases (methane, nitrogen, oxygen, and carbon dioxide) and noble gas isotopes (helium-3, helium-4, neon-20, neon-21, neon-22, argon-36, argon-38, argon-40, kryton-84, krypton-86, xenon-103, and xenon-132) dissolved in water. A synopsis of the instrumentation used, procedures followed, calibration practices, standards used, and a quality assurance and quality control program is presented. The report outlines the day-to-day operation of the Residual Gas Analyzer Model 200, Mass Analyzer Products Model 215–50, and ultralow vacuum extraction line along with the sample handling procedures, noble gas extraction and purification, instrument measurement procedures, instrumental data acquisition, and calculations for the conversion of raw data from the mass spectrometer into noble gas concentrations per unit mass of water analyzed. Techniques for the preparation of artificial dissolved gas standards are detailed and coupled to a quality assurance and quality control program to present the accuracy of the procedures used in the laboratory.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Water analysis in Book 5 Laboratory Analysis","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm5A11","usgsCitation":"Hunt, A.G., 2015, Noble Gas Laboratory’s standard operating procedures for the measurement of dissolved gas in water samples: U.S. Geological Survey Techniques and Methods, book 5, chap. A11, 22 p., https://dx.doi.org/10.3133/tm5A11.","productDescription":"vi, 21 p.","numberOfPages":"31","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-065997","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":306599,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/05/a11/coverthb.jpg"},{"id":306600,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/05/a11/tm5a11.pdf","text":"Report","size":"2.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 5-A11"}],"publicComments":"This report is Chapter 11 of Section A: Water analysis in Book 5 Laboratory Analysis.","contact":"Director, Crustal Geophysics and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS 964
Denver, CO 80225
http://crustal.usgs.gov/
In October 2012, the U.S. Geological Survey (USGS), in cooperation with the Central Texas Groundwater Conservation District, began an assessment to better understand if and where groundwater from the Ellenburger-San Saba aquifer is discharging to the Colorado River, and if and where Colorado River streamflow is recharging the Ellenburger-San Saba aquifer in the study area. Discharge measurements were made to determine if different reaches of the Colorado River in northwestern Burnet and southeastern San Saba Counties are gaining or losing streamflow, the locations and quantities of gains and losses, and whether the gains and losses can be attributed to interaction between the river and the Ellenbuger-San Saba aquifer. To assess streamflow gains and losses, two sets of synoptic gain-loss discharge measurements representing different streamflow conditions were completed. In the first gain-loss streamflow survey during December 3–6, 2012 (hereinafter the fall 2012 gain-loss survey), discharge measurements were made at low-flow conditions ranging from about 30 to 60 cubic feet per second (ft3/s) at seven locations along the Colorado River. In the second gain-loss streamflow survey during May 31–June 1, 2014 (hereinafter the spring 2014 gain-loss survey), discharge measurements were made at high-flow conditions ranging from about 660 to 900 ft3/s at 12 locations along the Colorado River.
\nDuring the fall 2012 gain-loss survey, verifiable gains or losses of streamflow were identified in 4 of 6 reaches (the difference in measured discharge between the upstream and downstream boundaries of the reach was larger than the sum of potential errors associated with the two discharge measurements). The two reaches with a verifiable gain in streamflow cross areas where the Ellenburger-San Saba aquifer crops out. The more upstream of the two reaches with verifiable losses crosses a small part of the Ellenburger-San Saba aquifer outcrop and confining units (Point Peak Member and Morgan Creek Limestone); it is possible streamflow losses in this reach are in the form of recharge to the Ellenburger-San Saba aquifer; little streamflow is likely lost to the underlying formations in the downstream part of the reach, which consists of relatively impermeable aquifer confining units exposed at land surface. The more downstream of the two reaches where a verifiable loss of streamflow was measured also flows across relatively impermeable confining units before crossing the Mid-Cambrian aquifer outcrop in the lower part of the reach; most of the streamflow losses in this reach were likely a result of water infiltrating into the subsurface from the streambed and providing recharge to the relatively permeable Mid-Cambrian aquifer.
\nDuring the spring 2014 gain-loss survey, 11 reaches were combined into 3 in an attempt to consolidate gains and losses as well as group reaches within the same hydrogeologic units. An unverifiable loss was measured in the reach farthest upstream, which crosses a combination of alluvium and Ellenburger-San Saba aquifer outcrop, whereas an unverifiable gain was measured in the middle reach, which crosses each of the different hydrogeologic units represented in the study area. The reach farthest downstream crosses an area where only the Ellenburger-San Saba aquifer crops out; a streamflow gain of 123 ft3/s was measured in this reach, exceeding the potential error of 93.9 ft3/s. The verifiable streamflow gain in this downstream reach implies the Ellenburger-San Saba aquifer was discharging groundwater to the Colorado River in this part of the study area under the hydrologic conditions of the spring 2014 gain-loss survey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155098","collaboration":"Prepared in cooperation with the Central Texas Groundwater Conservation District","usgsCitation":"Braun, C.L., and Grzyb, S.D., 2015, Streamflow gains and losses in the Colorado River in northwestern Burnet and southeastern San Saba Counties, Texas, 2012–14: U.S. Geological Survey Scientific Investigations Report 2015–5098, 32 p., https://dx.doi.org/10.3133/sir20155098.","productDescription":"v, 32 p.","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-062274","costCenters":[{"id":105,"text":"Alabama Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":306566,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5098/coverthb.jpg"},{"id":306567,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5098/sir20155098.pdf","text":"Report","size":"6.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5098"}],"country":"United States","state":"Texas","county":"Burnet County, San Saba County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.48075866699219,\n 30.918131046738022\n ],\n [\n -98.48075866699219,\n 31.0376384361344\n ],\n [\n -98.38085174560547,\n 31.0376384361344\n ],\n [\n -98.38085174560547,\n 30.918131046738022\n ],\n [\n -98.48075866699219,\n 30.918131046738022\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
http://tx.usgs.gov/
The North Platte Natural Resources District (NPNRD) has been actively collecting data and studying groundwater resources because of concerns about the future availability of the highly inter-connected surface-water and groundwater resources. This report, prepared by the U.S. Geological Survey in cooperation with the North Platte Natural Resources District, describes a groundwater-flow model of the North Platte River valley from Bridgeport, Nebraska, extending west to 6 miles into Wyoming. The model was built to improve the understanding of the interaction of surface-water and groundwater resources, and as an optimization tool, the model is able to analyze the effects of water-management options on the simulated stream base flow of the North Platte River. The groundwater system and related sources and sinks of water were simulated using a newton formulation of the U.S. Geological Survey modular three-dimensional groundwater model, referred to as MODFLOW–NWT, which provided an improved ability to solve nonlinear unconfined aquifer simulations with wetting and drying of cells. Using previously published aquifer-base-altitude contours in conjunction with newer test-hole and geophysical data, a new base-of-aquifer altitude map was generated because of the strong effect of the aquifer-base topography on groundwater-flow direction and magnitude. The largest inflow to groundwater is recharge originating from water leaking from canals, which is much larger than recharge originating from infiltration of precipitation. The largest component of groundwater discharge from the study area is to the North Platte River and its tributaries, with smaller amounts of discharge to evapotranspiration and groundwater withdrawals for irrigation. Recharge from infiltration of precipitation was estimated with a daily soil-water-balance model. Annual recharge from canal seepage was estimated using available records from the Bureau of Reclamation and then modified with canal-seepage potentials estimated using geophysical data. Groundwater withdrawals were estimated using land-cover data, precipitation data, and published crop water-use data. For fields irrigated with surface water and groundwater, surface-water deliveries were subtracted from the estimated net irrigation requirement, and groundwater withdrawal was assumed to be equal to any demand unmet by surface water.
\nThe groundwater-flow model was calibrated to measured groundwater levels and stream base flows estimated using the base-flow index method. The model was calibrated through automated adjustments using statistical techniques through parameter estimation using the parameter estimation suite of software (PEST). PEST was used to adjust 273 parameters, grouped as hydraulic conductivity of the aquifer, spatial multipliers to recharge, temporal multipliers to recharge, and two specific recharge parameters. Base flow of the North Platte River at Bridgeport, Nebraska, streamgage near the eastern, downstream end of the model was one of the primary calibration targets. Simulated base flow reasonably matched estimated base flow for this streamgage during 1950–2008, with an average difference of 15 percent. Overall, 1950–2008 simulated base flow followed the trend of the estimated base flow reasonably well, in cases with generally increasing or decreasing base flow from the start of the simulation to the end. Simulated base flow also matched estimated base flow reasonably well for most of the North Platte River tributaries with estimated base flow. Average simulated groundwater budgets during 1989–2008 were nearly three times larger for irrigation seasons than for non-irrigation seasons.
\nThe calibrated groundwater-flow model was used with the Groundwater-Management Process for the 2005 version of the U.S. Geological Survey modular three-dimensional groundwater model, MODFLOW–2005, to provide a tool for the NPNRD to better understand how water-management decisions could affect stream base flows of the North Platte River at Bridgeport, Nebr., streamgage in a future period from 2008 to 2019 under varying climatic conditions. The simulation-optimization model was constructed to analyze the maximum increase in simulated stream base flow that could be obtained with the minimum amount of reductions in groundwater withdrawals for irrigation. A second analysis extended the first to analyze the simulated base-flow benefit of groundwater withdrawals along with application of intentional recharge, that is, water from canals being released into rangeland areas with sandy soils. With optimized groundwater withdrawals and intentional recharge, the maximum simulated stream base flow was 15–23 cubic feet per second (ft3/s) greater than with no management at all, or 10–15 ft3/s larger than with managed groundwater withdrawals only. These results indicate not only the amount that simulated stream base flow can be increased by these management options, but also the locations where the management options provide the most or least benefit to the simulated stream base flow. For the analyses in this report, simulated base flow was best optimized by reductions in groundwater withdrawals north of the North Platte River and in the western half of the area. Intentional recharge sites selected by the optimization had a complex distribution but were more likely to be closer to the North Platte River or its tributaries. Future users of the simulation-optimization model will be able to modify the input files as to type, location, and timing of constraints, decision variables of groundwater withdrawals by zone, and other variables to explore other feasible management scenarios that may yield different increases in simulated future base flow of the North Platte River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155093","collaboration":"Prepared in cooperation with the North Platte Natural Resources District","usgsCitation":"Peterson, S.M, Flynn, A.T., Vrabel, Joseph, and Ryter, D.W., 2015, Simulation of groundwater flow and analysis of the effects of water-management options in the North Platte Natural Resources District, Nebraska: U.S. Geological Survey Scientific Investigations Report 2015–5093, 67 p., https://dx.doi.org/10.3133/sir20155093.","productDescription":"ix, 67 p.","numberOfPages":"82","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-055438","costCenters":[{"id":464,"text":"Nebraska Water Science 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U.S. Geological Survey
5231 South 19th Street
Lincoln, Nebraska 68512
http://ne.water.usgs.gov/
Hydraulically fractured shales are becoming an increasingly important source of natural gas production in the United States. This process has been known to create up to 420 gallons of produced water (PW) per day, but the volume varies depending on the formation, and the characteristics of individual hydraulic fracture. PW from hydraulic fracturing of shales are comprised of injected fracturing fluids and natural formation waters in proportions that change over time. Across the state of Pennsylvania, shale gas production is booming; therefore, it is important to assess the variability in PW chemistry and microbiology across this geographical span. We quantified the inorganic and organic chemical composition and microbial communities in PW samples from 13 shale gas wells in north central Pennsylvania. Microbial abundance was generally low (66–9400 cells/mL). Non-volatile dissolved organic carbon (NVDOC) was high (7–31 mg/L) relative to typical shallow groundwater, and the presence of organic acid anions (e.g., acetate, formate, and pyruvate) indicated microbial activity. Volatile organic compounds (VOCs) were detected in four samples (∼1 to 11.7 μg/L): benzene and toluene in the Burket sample, toluene in two Marcellus samples, and tetrachloroethylene (PCE) in one Marcellus sample. VOCs can be either naturally occurring or from industrial activity, making the source of VOCs unclear. Despite the addition of biocides during hydraulic fracturing, H2S-producing, fermenting, and methanogenic bacteria were cultured from PW samples. The presence of culturable bacteria was not associated with salinity or location; although organic compound concentrations and time in production were correlated with microbial activity. Interestingly, we found that unlike the inorganic chemistry, PW organic chemistry and microbial viability were highly variable across the 13 wells sampled, which can have important implications for the reuse and handling of these fluids
","language":"English","publisher":"Oxford","publisherLocation":"New York, NY","doi":"10.1016/j.apgeochem.2015.04.011","usgsCitation":"Akob, D.M., Cozzarelli, I.M., Dunlap, D.S., Rowan, E.L., and Lorah, M.M., 2015, Organic and inorganic composition and microbiology of produced waters from Pennsylvania shale gas wells: Applied Geochemistry, v. 60, p. 116-125, https://doi.org/10.1016/j.apgeochem.2015.04.011.","productDescription":"10 p.","startPage":"116","endPage":"125","numberOfPages":"10","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061928","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":306602,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","county":"Lycoming, 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erowan@usgs.gov","orcid":"https://orcid.org/0000-0001-5753-6189","contributorId":2075,"corporation":false,"usgs":true,"family":"Rowan","given":"Elisabeth","email":"erowan@usgs.gov","middleInitial":"L.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":566592,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lorah, Michelle M. 0000-0002-9236-587X mmlorah@usgs.gov","orcid":"https://orcid.org/0000-0002-9236-587X","contributorId":1437,"corporation":false,"usgs":true,"family":"Lorah","given":"Michelle","email":"mmlorah@usgs.gov","middleInitial":"M.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566593,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70155846,"text":"70155846 - 2015 - Normalization of stable isotope data for carbonate minerals: implementation of IUPAC guideline","interactions":[],"lastModifiedDate":"2015-08-12T08:49:16","indexId":"70155846","displayToPublicDate":"2015-08-12T08:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Normalization of stable isotope data for carbonate minerals: implementation of IUPAC guideline","docAbstract":"Carbonate minerals provide a rich source of geochemical information because their δ13C and δ18O values provide information about surface and subsurface Earth processes. However, a significant problem is that the same δ18O value is not reported for the identical carbonate sample when analyzed in different isotope laboratories in spite of the fact that the International Union of Pure and Applied Chemistry (IUPAC) has provided reporting guidelines for two decades. This issue arises because (1) the δ18O measurements are performed on CO2 evolved by reaction of carbonates with phosphoric acid, (2) the acid-liberated CO2 is isotopically fractionated (enriched in 18O) because it contains only two-thirds of the oxygen from the solid carbonate, (3) this oxygen isotopic fractionation factor is a function of mineralogy, temperature, concentration of the phosphoric acid, and δ18O value of water in the phosphoric acid, (4) researchers may use any one of an assortment of oxygen isotopic fractionation factors that have been published for various minerals at various reaction temperatures, and (5) it sometimes is not clear how one should calculate δ18OVPDB values on a scale normalized such that the δ18O value of SLAP reference water is −55.5 ‰ relative to VSMOW reference water.
\nTo enable researchers worldwide to publish the same δ18O value (within experimental uncertainty) for the same carbonate sample, we have re-evaluated reported acid fractionation factors for calcite at 25, 50, and 75 °C and propose a revised relation for the temperature dependence of oxygen isotopic acid fractionation factor, αCO2(ACID)-calciteαCO2(ACID)-calcite, of
\nwhere T is temperature in kelvin. At 25 °C, αCO2(ACID)-calcite=1.01025αCO2(ACID)-calcite=1.01025, the most commonly accepted value for this quantity. We propose a normalization protocol in which (1) the internationally distributed carbonate isotopic reference materials NBS 18 and NBS 19 are interspersed among carbonate samples analyzed by treatment with phosphoric acid, (2) the δ18O values of the calcite reference materials and the carbonate samples are calculated, respectively, by using the αCO2(ACID)-calciteαCO2(ACID)-calcite relation above and oxygen-isotope acid fractionation factors appropriate for the sample mineralogy and reaction temperature, (3) the δ18O values of solid carbonate samples are determined on the VPDB scale (δ18OVPDB) with IUPAC-recommended scale expansion such that the δ18O of SLAP reference water is −55.5 ‰ relative to VSMOW reference water by normalizing δ18O values of carbonate samples with 2014-IUPAC-recommended δ18O values of NBS 18 and NBS 19, and (4) δ18O values on the VPDB scale are converted to δ18O values on the VSMOW-SLAP scale by using IUPAC recommendations.
\nTo ease calculations in the protocol, a software application titled “Carbon and Oxygen Isotopic Normalization Tool for Carbonates” is available that relies upon IUPAC-recommended δ13C and δ18O values of carbonate isotopic reference materials
\n(http://isotopes.usgs.gov/research/topics/carbonatesnormalizationtool.html).
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