Wastewater disposal is generally accepted to be the primary cause of the increased seismicity rate in Oklahoma within the past decade, but no statewide analysis has investigated the contribution of hydraulic fracturing (HF) to the observed seismicity or the seismic hazard. Utilizing an enhanced seismicity catalog generated with multi‐station template matching from 2010‐2016 and all available hydraulic fracturing information, we identified 274 HF wells that are spatiotemporally correlated with bursts of seismicity. The majority of HF induced seismicity cases occurred in the SCOOP/STACK plays, but we also identified prominent cases in the Arkoma Basin as well as some more complex potential cases along the edge of the Anadarko Platform. For HF treatments where we have access to injection parameters, modeling suggests poroelastic stresses are likely responsible for seismicity, but we cannot rule out direct pore pressure effects as a contributing factor. In all of the 16 regions we identified, ≥75% of the seismicity correlated with reported HF wells. In some regions, >95% of seismicity correlated with HF wells and >50% of the HF wells correlated with seismicity. Overall, we found ~700 HF induced earthquakes with M ≥ 2.0, including 12 events with M 3.0‐3.5. These findings suggest state regulations implemented in 2018 that require operators in the SCOOP/STACK plays to take action if a M > 2 earthquake occurs could have a significant impact on future operations.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018JB016790","usgsCitation":"Skoumal, R.J., Ries, R., Brudzinski, M.R., Barbour, A.J., and Currie, B.S., 2018, Earthquakes induced by hydraulic fracturing are pervasive in Oklahoma: Journal of Geophysical Research B: Solid Earth, v. 123, no. 12, p. 10918-10935, https://doi.org/10.1029/2018JB016790.","productDescription":"18 p.","startPage":"10918","endPage":"10935","ipdsId":"IP-101853","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":468176,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018jb016790","text":"Publisher Index Page"},{"id":360647,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100,\n 33.75\n ],\n [\n -94.5,\n 33.75\n ],\n [\n -94.5,\n 37.5\n ],\n [\n -100,\n 37.5\n ],\n [\n -100,\n 33.75\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"123","issue":"12","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-27","publicationStatus":"PW","scienceBaseUri":"5c1cb85be4b0708288c83806","contributors":{"authors":[{"text":"Skoumal, Robert J. 0000-0002-5627-6239 rskoumal@usgs.gov","orcid":"https://orcid.org/0000-0002-5627-6239","contributorId":191213,"corporation":false,"usgs":true,"family":"Skoumal","given":"Robert","email":"rskoumal@usgs.gov","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":754825,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ries, Rosamiel","contributorId":211773,"corporation":false,"usgs":false,"family":"Ries","given":"Rosamiel","email":"","affiliations":[{"id":38316,"text":"Miami University, Oxford, Ohio","active":true,"usgs":false}],"preferred":false,"id":754826,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brudzinski, Michael R. 0000-0003-1869-0700","orcid":"https://orcid.org/0000-0003-1869-0700","contributorId":207880,"corporation":false,"usgs":false,"family":"Brudzinski","given":"Michael","email":"","middleInitial":"R.","affiliations":[{"id":16608,"text":"Miami University","active":true,"usgs":false}],"preferred":false,"id":754827,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barbour, Andrew J. 0000-0002-6890-2452 abarbour@usgs.gov","orcid":"https://orcid.org/0000-0002-6890-2452","contributorId":197158,"corporation":false,"usgs":true,"family":"Barbour","given":"Andrew","email":"abarbour@usgs.gov","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":754828,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Currie, Brian S.","contributorId":207881,"corporation":false,"usgs":false,"family":"Currie","given":"Brian","email":"","middleInitial":"S.","affiliations":[{"id":16608,"text":"Miami University","active":true,"usgs":false}],"preferred":false,"id":754829,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70201669,"text":"70201669 - 2018 - Microseismic events associated with the Oroville Dam spillway","interactions":[],"lastModifiedDate":"2019-02-11T14:41:01","indexId":"70201669","displayToPublicDate":"2018-12-20T15:25:29","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Microseismic events associated with the Oroville Dam spillway","docAbstract":"On 14 February 2017, two small (equivalent MD 0.8 and 1.0) seismic events occurred in proximity to the Oroville Dam in the Sierra Nevada foothills, California. To examine possible causal relationships between these events and reservoir operations, including the spillway failure starting prior to these events, we applied a new optimized template matching approach to seismic data between May 1993 - April 2018. We identified more than 19,000 smaller-magnitude events that were similar in character to the February 14 events. These events are located in proximity to the Oroville spillway and occurred in tight temporal clusters that strongly correlate with periods of spillway discharge. Seismic source inversion is inconclusive, but we suggest that these events might be induced by rapid changes in pore pressure along a fracture (or fractures) near the spillway. Cavitation cannot be ruled out, but it is unlikely to be the primary cause of the signals observed because these events are intermittent, impulsive and of short duration. The inferred repetitive opening and closing of the fracture(s) occurred long before any damage to the spillway and is thus probably not directly associated with spillway failure in February 2017. These events were not related to the 1975 ML 5.7 earthquake sequence that may have been induced by the filling of the Oroville reservoir.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120180255","usgsCitation":"Skoumal, R.J., Dawson, P.B., Hickman, S.H., and Kaven, J., 2018, Microseismic events associated with the Oroville Dam spillway: Bulletin of the Seismological Society of America, v. 109, no. 1, p. 387-394, https://doi.org/10.1785/0120180255.","productDescription":"8 p.","startPage":"387","endPage":"394","ipdsId":"IP-090910","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":360646,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Oroville Dam Spillway","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.9,\n 39.2\n ],\n [\n -121.4,\n 39.2\n ],\n [\n -121.4,\n 39.7\n ],\n [\n -121.9,\n 39.7\n ],\n [\n -121.9,\n 39.2\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"109","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-18","publicationStatus":"PW","scienceBaseUri":"5c1cb85ce4b0708288c83809","contributors":{"authors":[{"text":"Skoumal, Robert J. 0000-0002-5627-6239 rskoumal@usgs.gov","orcid":"https://orcid.org/0000-0002-5627-6239","contributorId":191213,"corporation":false,"usgs":true,"family":"Skoumal","given":"Robert","email":"rskoumal@usgs.gov","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":754818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dawson, Phillip B. 0000-0003-4065-0588 dawson@usgs.gov","orcid":"https://orcid.org/0000-0003-4065-0588","contributorId":206751,"corporation":false,"usgs":true,"family":"Dawson","given":"Phillip","email":"dawson@usgs.gov","middleInitial":"B.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":754819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hickman, Stephen H. 0000-0003-2075-9615 hickman@usgs.gov","orcid":"https://orcid.org/0000-0003-2075-9615","contributorId":2705,"corporation":false,"usgs":true,"family":"Hickman","given":"Stephen","email":"hickman@usgs.gov","middleInitial":"H.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":754820,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kaven, J. Ole 0000-0003-2625-2786 okaven@usgs.gov","orcid":"https://orcid.org/0000-0003-2625-2786","contributorId":3993,"corporation":false,"usgs":true,"family":"Kaven","given":"J. Ole","email":"okaven@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":754821,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70201668,"text":"70201668 - 2018 - Ground motions from induced earthquakes in Oklahoma and Kansas","interactions":[],"lastModifiedDate":"2019-01-28T08:20:01","indexId":"70201668","displayToPublicDate":"2018-12-20T15:19:50","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Ground motions from induced earthquakes in Oklahoma and Kansas","docAbstract":"Improved predictions of earthquake ground motions are critical to advancing seismic hazard analyses and earthquake response. The high seismicity rate from 2009 to 2016 in Oklahoma and Kansas provides an extensive data set for examining the ground motions from these events. We evaluate the ability of three suites of ground‐motion prediction equations (GMPEs)—appropriate for modeling tectonic earthquakes in active crustal and stable continental regions—to reproduce the observed ground motions. Mixed‐effects regressions are used to separate the ground‐motion residuals into bias, between‐event, and within‐event terms. Although the residuals depict differing accuracies in the ability of the three GMPE suites to predict the ground motions, some consistent trends emerge in the period, magnitude, and distance dependence. The trends suggest that aspects of the ground motions from these induced earthquakes are not well modeled by current tectonic GMPEs. Most important, we find evidence for relatively poor overall fit to the ground motions, by all of the GMPE suites, at periods less than about 0.2 s and above 3 s, greater‐than‐predicted magnitude scaling for small to moderate‐magnitude events (
The Upper Mississippi River Restoration (UMRR) Program Long Term Resource Monitoring (LTRM) element has been monitoring fish, water quality, and vegetation in six study pools in the Upper Mississippi River system for approximately 30 years. Geographic locations were recorded for all sampling points. All of this information has been made publicly available by way of data download and visualization tools (https://www.umesc.usgs.gov/ltrm-home.html), but it has not been available to decision makers, scientists, resource managers, and the public by way of an internet-based mapping and query application. Presenting the information in this way is vital to providing decision makers with the information and understanding needed to maintain the Upper Mississippi River system as a viable multiple-use river ecosystem, the primary mission of the UMRR LTRM.
Spatial data query tools have been developed that allow the query, display, mapping, and data extraction of the UMRR LTRM element component data by means of an easy-to-use graphical user interface. A separate spatial data query tool application was developed for each study pool within the Upper Mississippi River system and is accessible as individual links at the bottom of the website located at https://www.umesc.usgs.gov/ltrmp/spatial_data_query_tool.html. In addition to the UMRR LTRM element component data, the spatial data query tool also contains land cover and bathymetric (water depth) data collected by the UMRR LTRM element.
Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54602
Hydrograph analysis tools using a master recession curve (MRC) can produce many types of hydrologically important watershed-response quantifications, including aquifer recharge and stormflow characterization. An MRC is the relation between the value of a measured response R and its rate of change with time, dR/dt, occurring on the falling limb when there is no infiltration or other water input. We have developed MRC and episodic hydrograph-evaluation methods for multiple purposes, utilizing both water table and streamflow data. The determination of a parameterized MRC through a structured procedure provides a basis for quantification of hydrologic variables and characteristics that can be validly compared among different events, sites, and periods of time. Application of the MRC to needed hydrologic quantifications is done with our revised episodic master recession (EMR) method. Expert-guided iterative procedures are used to quantify parameters needed in applying the MRC and EMR methods to a given site. Hydrologic judgments such as the significance threshold for response magnitude, and the time window within which the precipitation is assumed to be the cause of an observed response, inherently involve some elements of subjectivity. Our structured iterative approach, however, affords much flexibility in formulating expert judgments and serves to confine them to statements and procedures that can be quantified and documented. Parallel application to streamflow and water table hydrographs can produce new hydrologic insights and understanding, not least in the role of unsaturated zone processes in controlling exchanges among components of the water cycle.
","language":"English","publisher":"ACSESS","doi":"10.2136/vzj2018.03.0050","usgsCitation":"Nimmo, J.R., and Perkins, K., 2018, Episodic master recession evaluation of groundwater and streamflow hydrographs for water-resource estimation: Vadose Zone Journal, v. 17, no. 1, p. 1-25, https://doi.org/10.2136/vzj2018.03.0050.","productDescription":"25 p.","startPage":"1","endPage":"25","ipdsId":"IP-096220","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":468177,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2136/vzj2018.03.0050","text":"Publisher Index Page"},{"id":360784,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":755294,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perkins, Kimberlie 0000-0001-8349-447X kperkins@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-447X","contributorId":138544,"corporation":false,"usgs":true,"family":"Perkins","given":"Kimberlie","email":"kperkins@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":755295,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70201614,"text":"70201614 - 2018 - Hyperspectral remote sensing of wetland vegetation","interactions":[],"lastModifiedDate":"2018-12-20T11:21:47","indexId":"70201614","displayToPublicDate":"2018-12-20T11:21:43","publicationYear":"2018","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Hyperspectral remote sensing of wetland vegetation","docAbstract":"Chapter 11 by Ramsey and Rangoonwala provides an overview of how hyperspectral imaging (HSI) advances the mapping of coastal wetlands that comprise a unique variety of plant species, forms, and associations. Each description begins by seeking to uncover the relationship between canopy hyperspectral reflectance and one or more of the aggregated biophysical properties of the wetland canopy: leaf spectral properties, canopy structure, or background reflectance. Examples incorporate the application of radiative transfer equations, direct measurements, and above the top of canopy reflectance and photography. First demonstrated is how HSI can elucidate relations observed with broadband mapping of mangroves as well as enhance change detection. Similar uses of HSI are suggested for the mapping of cypress and bottomland hardwood forests. Next, the mapping of invasive plants is used to demonstrate how HSI can be used to transfer high-spatial-resolution broadband information to spatial resolutions amenable to regional mapping. The demonstration also includes the fusion of HSI and broadband mapping for added-value regional risk assessment of invasive establishment. Illustrations and discussion then show that the proper interpretation of HSI canopy reflectance can require measurement of both the marsh canopy structure and its compositional biomass. The final HSI application demonstrates the capability to detect and track abnormal marsh change at the leaf and canopy levels within variable backgrounds. It further shows how necessary HSI spectral information can be identified and captured in a limited number of narrowbands for advancing operational mapping.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Advanced applications in remote sensing of agricultural crops and natural vegetation","language":"English","publisher":"CRC Press","usgsCitation":"Ramsey, E., and Rangoonwala, A., 2018, Hyperspectral remote sensing of wetland vegetation, chap. of Advanced applications in remote sensing of agricultural crops and natural vegetation, p. 219-248.","productDescription":"30 p.","startPage":"219","endPage":"248","ipdsId":"IP-091636","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":360621,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":360462,"type":{"id":15,"text":"Index Page"},"url":"https://www.taylorfrancis.com/books/9780429431166/chapters/10.1201/9780429431166-11"}],"publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c1cb85de4b0708288c83812","contributors":{"authors":[{"text":"Ramsey, Elijah III 0000-0002-4518-5796 ramseye@usgs.gov","orcid":"https://orcid.org/0000-0002-4518-5796","contributorId":195558,"corporation":false,"usgs":true,"family":"Ramsey","given":"Elijah","suffix":"III","email":"ramseye@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":754551,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rangoonwala, Amina 0000-0002-0556-0598 rangoonwalaa@usgs.gov","orcid":"https://orcid.org/0000-0002-0556-0598","contributorId":3455,"corporation":false,"usgs":true,"family":"Rangoonwala","given":"Amina","email":"rangoonwalaa@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":754552,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70201458,"text":"ofr20181192 - 2018 - Analysis of groundwater response to tidal fluctuations, Site 10 Naval Magazine Indian Island, Port Hadlock, Washington","interactions":[],"lastModifiedDate":"2018-12-21T10:28:59","indexId":"ofr20181192","displayToPublicDate":"2018-12-20T11:02:22","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1192","displayTitle":"Analysis of Groundwater Response to Tidal Fluctuations, Site 10 Naval Magazine Indian Island, Port Hadlock, Washington","title":"Analysis of groundwater response to tidal fluctuations, Site 10 Naval Magazine Indian Island, Port Hadlock, Washington","docAbstract":"Site 10 at Naval Magazine Indian Island is an approximately 3.7-acre inactive landfill. The site was used as the primary landfill for the island from about 1945 until the mid-1970s, receiving paints, batteries, trash, and materials. In a memorandum to Washington State Department of Ecology, Naval Facilities Engineering Command Northwest (NAVFAC NW) stipulated that a new tidal study would be conducted to recalculate tidal influence lag-time in each groundwater monitoring well at Site 10.
Groundwater levels and specific conductance in five monitoring wells, along with marine water-levels (tidal levels) in Port Townsend Bay, were monitored every 15 minutes during a 2-week period to better understand nearshore groundwater-seawater interactions at Site 10. Time series data were collected from April 17 to May 3, 2018, a period that included neap and spring tides.
Vertical profiles of specific conductance were measured once in the screened interval of each well prior to instrument deployment to determine if a freshwater/saltwater interface was present in the well prior to instrument deployment. Profiles where measured during an ebbing tide at approximately the top, middle, and bottom of the saturated thickness within the screened interval of each well. The landward-most well, MW10-8 and coastline wells MW10-10, MW10-11 and MW10-12R, had a uniform specific conductance in the range of fresh or brackish water. Landfill monitoring well MW10-6 showed the highest uniform specific conductance profile also in the range of brackish water.
Lag times between minimum spring-tide levels and minimum groundwater levels in wells ranged from about 0 to 4 hours. Results of lag times showed a logical increase in lag time as the distance increases from the shoreline to each monitoring well.
The specific-conductance time-series data showed minimal change in the screened interval of each well. Fluctuation of specific conductance in each well was unique but no sharp groundwater saltwater interface was observed. Increases in specific conductivity concurrent with spring low tides were measured in coastline wells, suggesting shoreward transport of high specific conductivity landfill leachate rather than seawater intrusion.
Based on all the data collected during this investigation, the optimal time for sampling monitoring wells at Site 10 would be during a 0–4-hour period following the predicted low-low tide.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181192","collaboration":"Prepared in cooperation with the Department of the Navy, Naval Facilities Engineering Command, Northwest","usgsCitation":"Opatz, C.C., and Dinicola, R.S., 2018, Analysis of groundwater response to tidal fluctuations, Site 10 Naval Magazine Indian Island, Port Hadlock, Washington: U.S. Geological Survey Open-File Report 2018-1192, 21 p., https://doi.org/10.3133/ofr20181192.","productDescription":"iv, 21 p.","onlineOnly":"Y","ipdsId":"IP-099654","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":360629,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1192/ofr20181192.pdf","text":"Report","size":"3.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1192"},{"id":360628,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1192/coverthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Naval Magazine Indian Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.73732662200928,\n 48.08149085582177\n ],\n [\n -122.72406578063963,\n 48.08149085582177\n ],\n [\n -122.72406578063963,\n 48.0877406628633\n ],\n [\n -122.73732662200928,\n 48.0877406628633\n ],\n [\n -122.73732662200928,\n 48.08149085582177\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Understanding the relationship between environmental factors and vital rates is an important step in predicting a species’ response to environmental change. Species associated with sea ice are of particular concern because sea ice is projected to decrease rapidly in polar environments with continued levels of greenhouse gas emissions. The relationship between sea ice and the vital rates of the Spectacled Eider, a threatened species that breeds in Alaska and Russia and winters in the Bering Sea, appears to be complex. While severe ice can impede foraging for benthic prey, ice also suppresses wave action and provides a platform on which eiders roost, thereby reducing thermoregulation costs. We analyzed a 23‐year mark‐recapture dataset for Spectacled Eiders nesting on Kigigak Island in western Alaska, and tested survival models containing different ice and weather‐related covariates. We found that much of the variation in eider survival could be explained by the number of days per year with >95% sea ice concentration at the Bering Sea core wintering area. Furthermore, the data supported a quadratic relationship with sea ice rather than a linear one, indicating that intermediate sea ice concentrations were optimal for survival. We then used matrix population models to project population trajectories using General Circulation Model (GCM) outputs of daily sea ice cover. GCMs projected reduced sea ice at the wintering area by year 2100 under a moderated emissions scenario (RCP 4.5) and nearly ice‐free conditions under an unabated emissions scenario (RCP 8.5). Under RCP 4.5, stochastic models projected an increase in population size until 2069 coincident with moderate ice conditions, followed by a decline in population size as ice conditions shifted from intermediate to mostly ice‐free. Under RCP 8.5, eider abundance increased until 2040 and then decreased to near extirpation toward the end of the century as the Bering Sea became ice‐free. Considerable uncertainty around parameter estimates for survival in years with minimal sea ice contributed to variation in stochastic projections of future population size, and this uncertainty could be reduced with additional survival data from low‐ice winters.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.4637","usgsCitation":"Christie, K.S., Hollmen, T.E., Flint, P.L., and Douglas, D., 2018, Non‐linear effect of sea ice: Spectacled Eider survival declines at both extremes of the ice spectrum: Ecology and Evolution, v. 8, no. 23, p. 11808-11818, https://doi.org/10.1002/ece3.4637.","productDescription":"11 p.","startPage":"11808","endPage":"11818","ipdsId":"IP-093761","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":468178,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.4637","text":"Publisher Index Page"},{"id":360610,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":" Kigigak Island","volume":"8","issue":"23","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-20","publicationStatus":"PW","scienceBaseUri":"5c1cb85de4b0708288c83817","contributors":{"authors":[{"text":"Christie, Katherine S.","contributorId":177114,"corporation":false,"usgs":false,"family":"Christie","given":"Katherine","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":754733,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hollmen, Tuula E.","contributorId":211728,"corporation":false,"usgs":false,"family":"Hollmen","given":"Tuula","email":"","middleInitial":"E.","affiliations":[{"id":16211,"text":"Alaska SeaLife Center","active":true,"usgs":false}],"preferred":false,"id":754734,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flint, Paul L. 0000-0002-8758-6993 pflint@usgs.gov","orcid":"https://orcid.org/0000-0002-8758-6993","contributorId":3284,"corporation":false,"usgs":true,"family":"Flint","given":"Paul","email":"pflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":754731,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":150115,"corporation":false,"usgs":true,"family":"Douglas","given":"David C.","email":"ddouglas@usgs.gov","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"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":754732,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70201657,"text":"70201657 - 2018 - Exposure of Alaska brown bears (Ursus arctos) to bacterial, viral, and parasitic agents varies spatiotemporally and may be influenced by age","interactions":[],"lastModifiedDate":"2019-08-15T11:45:00","indexId":"70201657","displayToPublicDate":"2018-12-20T10:34:30","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2507,"text":"Journal of Wildlife Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Exposure of Alaska brown bears (Ursus arctos) to bacterial, viral, and parasitic agents varies spatiotemporally and may be influenced by age","docAbstract":"We collected blood and serum from 155 brown bears (Ursus arctos) inhabiting five locations in Alaska during 2013–16 and tested samples for evidence of prior exposure to a suite of bacterial, viral, and parasitic agents. Antibody seroprevalence among Alaska brown bears was estimated to be 15% for Brucella spp., 10% for Francisella tularensis, 7% for Leptospira spp., 18% for canine adenovirus type 1 (CAV-1), 5% for canine distemper virus (CDV), 5% for canine parvovirus, 5% for influenza A virus (IAV), and 44% for Toxoplasma gondii. No samples were seropositive for antibodies to Trichinella spp. Point estimates of prior exposure to pathogens among brown bears at previously unsampled locations generally fell within the range of estimates for previously or contemporaneously sampled bears in Alaska. Statistical support was found for variation in antibody seroprevalence among bears by location or age cohort for CAV-1, CDV, IAV, and Toxoplasma gondii. There was limited concordance in comparisons between our results and previous serosurveys regarding spatial and age-related trends in antibody seroprevalence among Alaska brown bears suggestive of temporal variation. However, we found evidence that the seroprevalence of CAV-1 antibodies is consistently high in bears inhabiting SW Alaska and the cumulative probability of exposure may increase with age. We found evidence for seroconversion or seroreversion to six different infectious agents in one or more bears. Results of this study increase our collective understanding of disease risk to both Alaska brown bear populations and humans that utilize this resource.
","language":"English","publisher":"Wildlife Disease Association","doi":"10.7589/2018-07-173","usgsCitation":"Ramey, A.M., Cleveland, C.A., Hilderbrand, G., Joly, K., Gustine, D.D., Mangipane, B., Leacock, W.B., Crupi, A.P., Hill, D.E., Dubey, J.P., and Yabsley, M.J., 2018, Exposure of Alaska brown bears (Ursus arctos) to bacterial, viral, and parasitic agents varies spatiotemporally and may be influenced by age: Journal of Wildlife Diseases, v. 55, no. 3, p. 576-588, https://doi.org/10.7589/2018-07-173.","productDescription":"13 p.","startPage":"576","endPage":"588","ipdsId":"IP-099360","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":468179,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7589/2018-07-173","text":"Publisher Index Page"},{"id":437641,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94FFEUJ","text":"USGS data release","linkHelpText":"Brown Bear (Ursus acrtos) Captures and Serological Survey Results to Bacterial Viral and Parasitic Agents, Alaska, 2013-2016"},{"id":360609,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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WTEB","active":true,"usgs":true}],"preferred":false,"id":754737,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Joly, Kyle","contributorId":53117,"corporation":false,"usgs":false,"family":"Joly","given":"Kyle","email":"","affiliations":[{"id":12462,"text":"U.S. Department of the Interior, National Park Service","active":true,"usgs":false}],"preferred":false,"id":754738,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gustine, David D. dgustine@usgs.gov","contributorId":3776,"corporation":false,"usgs":true,"family":"Gustine","given":"David","email":"dgustine@usgs.gov","middleInitial":"D.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":754739,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mangipane, Buck","contributorId":211731,"corporation":false,"usgs":false,"family":"Mangipane","given":"Buck","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":754740,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Leacock, William B.","contributorId":211732,"corporation":false,"usgs":false,"family":"Leacock","given":"William","email":"","middleInitial":"B.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":754766,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Crupi, Anthony P.","contributorId":211733,"corporation":false,"usgs":false,"family":"Crupi","given":"Anthony","email":"","middleInitial":"P.","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":754741,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hill, Dolores E.","contributorId":211734,"corporation":false,"usgs":false,"family":"Hill","given":"Dolores","email":"","middleInitial":"E.","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":754742,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Dubey, Jitender P.","contributorId":206206,"corporation":false,"usgs":false,"family":"Dubey","given":"Jitender","email":"","middleInitial":"P.","affiliations":[{"id":37284,"text":"United States Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center, Animal Parasitic Diseases Laboratory, Building 1001, Beltsville, MD, 20705-2350, USA","active":true,"usgs":false}],"preferred":false,"id":754743,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Yabsley, Michael J.","contributorId":76985,"corporation":false,"usgs":false,"family":"Yabsley","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":13266,"text":"Warnell School of Forestry and Natural Resources, The University of Georgia","active":true,"usgs":false}],"preferred":false,"id":754744,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70200649,"text":"sim3419 - 2018 - Geologic map of the Pagosa Springs 7.5' quadrangle, Archuleta County, Colorado","interactions":[],"lastModifiedDate":"2018-12-20T16:33:56","indexId":"sim3419","displayToPublicDate":"2018-12-20T10:15:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3419","title":"Geologic map of the Pagosa Springs 7.5' quadrangle, Archuleta County, Colorado","docAbstract":"The geologic map of the Pagosa Springs 7.5’ quadrangle in southwestern Colorado includes the town of Pagosa Springs that is partly known for its hot springs. The quadrangle is southwest of the San Juan volcanic mountains (Oligocene) and north of the San Juan Basin. All bedrock units exposed in the map area are Upper Cretaceous in age except a minor canyon outcrop of Jurassic rock. Early Holocene deposits are mainly alluvial gravels and outwash on terraces. Structure is simple: shale and sandstone beds dip at low angles east-to-northeast as a broad limb of the north-northwest striking Archuleta anticline. Three geologic cross sections controlled by drill holes are included and depict Mesozoic bedrock and faults down to and including shallow Precambrian basement rock. A brief geologic history of the region is described.
Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS-980
Denver, CO 80225-0046
Digital flood-inundation maps for a 2.9-square-mile area of Ithaca, New York, were created in 2015–18 by the U.S. Geological Survey in cooperation with the City of Ithaca, New York, and the New York State Department of State. The flood-inundation maps depict estimates of the maximum areal extent and depth of flooding corresponding to selected flood frequencies for Cayuga Inlet, Sixmile Creek, Cascadilla Creek, and Fall Creek and selected water-surface elevations of Cayuga Lake.
Flood profiles for the stream reaches were computed by combining a one-dimensional step-backwater model for the stream channels and a two-dimensional model for the overbank areas. The resulting hydraulic model was calibrated by using water-surface profiles from five observed storm events. The model was then used to compute 15 water-surface profiles for 5 flood frequencies (50-, 10-, 2-, 1-, and 0.2-percent annual exceedance probabilities, or 2-, 10-, 50-, 100-, and 500-year recurrence intervals) and 3 lake levels (representing average conditions, a 2-year-high condition, and a 100-year-high condition). The simulated water-surface profiles were then combined with a digital elevation model (derived from light detection and ranging data having 0.31‑foot vertical accuracy and 3.3-foot horizontal resolution) to delineate the maximum area flooded at each water level.
Flood-inundation maps and geographic information system flood-extent polygons and depth grids are available in the data release associated with this report. These maps can provide emergency management personnel and residents with information that is critical for flood-management planning, flood-response activities, and postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185167","collaboration":"Prepared in cooperation with the City of Ithaca, New York, and the New York State Department of State","usgsCitation":"Nystrom, E.A., Lilienthal, A.G., III, and Coon, W.F., 2018, Flood-inundation maps for Cayuga Inlet, Sixmile Creek, Cascadilla Creek, and Fall Creek at Ithaca, New York: U.S. Geological Survey Scientific Investigations Report 2018–5167, 27 p., https://doi.org/10.3133/sir20185167. \n\n","productDescription":"Report: viii, 27 p.; Data release","ipdsId":"IP-097931","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":437643,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V8ED23","text":"USGS data release","linkHelpText":"Geospatial dataset of flood-inundation maps for Cayuga Inlet, Sixmile Creek, Cascadilla Creek, and Fall Creek at Ithaca, New York"},{"id":360375,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V8ED23 ","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial dataset of flood-inundation maps for Cayuga Inlet, Sixmile Creek, Cascadilla Creek, and Fall Creek at Ithaca, New York, 2018"},{"id":360373,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5167/coverthb.jpg"},{"id":360374,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5167/sir20185167.pdf","text":"Report","size":"6.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5167"}],"country":"United States","state":"New York","city":"Ithaca","otherGeospatial":"Cascadilla Creek, Cayuga Inlet, Fall Creek, Sixmile Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.53213500976562,\n 42.413825296776835\n ],\n [\n -76.48578643798827,\n 42.413825296776835\n ],\n [\n -76.48578643798827,\n 42.46994435756588\n ],\n [\n -76.53213500976562,\n 42.46994435756588\n ],\n [\n -76.53213500976562,\n 42.413825296776835\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180
Knowing how many manatees live in Florida is critical for conservation and management of this threatened species. Martin et al. (2015) flew aerial surveys in 2011–2012 and estimated abundance in those years using advanced techniques that incorporated multiple data sources. We flew additional aerial surveys in 2015–2016 to count manatees and again applied advanced statistical techniques to estimate their abundance. We also made several methodological advances over the earlier work, including accounting for how sea state (water surface conditions) and synchronous surfacing behavior affect the availability of manatees to be detected and incorporating all parts of Florida in the area of inference. We estimate that the number of manatees in Florida in 2015–2016 was 8,810 (95% Bayesian credible interval 7,520–10,280), of which 4,810 (3,820–6,010) were on the west coast of Florida and 4,000 (3,240–4,910) were on the east coast. These estimates and associated uncertainty, in addition to being of immediate value to wildlife managers, are essential new data for incorporation into integrated population models and population viability analyses.
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Wyoming","interactions":[],"lastModifiedDate":"2018-12-19T16:13:33","indexId":"ofr20181139","displayToPublicDate":"2018-12-19T16:04:26","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1139","title":"Map of sand and gravel mines, prospects, and occurrences, and the geologic units that host them in the Wyoming Landscape Conservation Initiative (WLCI) study area, southwestern Wyoming","docAbstract":"The Wyoming Landscape Conservation Initiative (WLCI) is a long-term science based effort to assess and enhance aquatic and terrestrial habitats at a landscape scale in southwest Wyoming, while facilitating responsible development through local collaboration and partnerships. The role of the U.S. Geological Survey is to build the scientifically defensible foundation on which WLCI planners, decisionmakers, and resource managers may base their activities. Understanding the distribution of mineral resources is integral to understanding where mineral development (mining) might be concentrated in the future and how that mining might affect habitats. This map and report focus on naturally-occurring sand and gravel, a form of construction aggregate.
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Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, Mail Stop 973
Denver, CO 80225
The ongoing restoration of more than 200 hectares of estuarine habitat at Willapa National Wildlife Refuge, southwestern Washington, is expected to benefit a variety of species, including salmonids that use estuarine and tidal marshes as rearing and feeding areas as well as migratory waterbirds. During March–June 2014 and 2015, U.S. Geological Survey Western Ecological Research Center (WERC) initiated a study to assess aquatic prey resources, in coordination with a separate but parallel fish study done by the Columbia River Estuary Study Taskforce. WERC collected data on environmental variables and invertebrate community structure, and the taskforce provided salmonid diet data at restored (Lewis Stream and Porter Point) and reference (Greenhead Slough and Ellsworth Creek) sites. We analyzed these data to determine the functional capacity of the estuary for supporting invertebrate prey resources for fish following restoration.
The results of our analyses were as follows:
Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Using a geology-based assessment methodology, the U.S. Geological Survey estimated means of 389 million barrels of oil and 1.8 trillion cubic feet of gas in the Niobrara interval of the Cody Shale in the Wind River Basin Province, Wyoming.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183076","usgsCitation":"Finn, T.M., Schenk, C.J., Mercier, T.J., Tennyson, M.E., Le, P.A., Brownfield, M.E., Marra, K.R., Leathers-Miller, H.M., Drake, R.M., II, Woodall, C.A., and Kinney, S.A., 2018, Assessment of continuous oil and gas resources in the Niobrara interval of the Cody Shale, Wind River Basin Province, Wyoming, 2018 (ver. 1.1, March 2020): U.S. Geological Survey Fact Sheet 2018–3076, 2 p., https://doi.org/10.3133/fs20183076.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-099340","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":372342,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":373009,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3076/coverthb2.jpg"},{"id":373010,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3076/fs20183076.pdf","text":"Report","size":"600 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2018–3076"},{"id":373011,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2018/3076/versionHist.txt","size":"2.36 kB","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"Wyoming","otherGeospatial":"Wind River Basin Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.97314453125,\n 42.27730877423709\n ],\n [\n -106.34765625,\n 42.27730877423709\n ],\n [\n -106.34765625,\n 43.78695837311561\n ],\n [\n -109.97314453125,\n 43.78695837311561\n ],\n [\n -109.97314453125,\n 42.27730877423709\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: March 2020; Version 1.0: December 2018","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
The Colorado Department of Transportation maintains roadways crossing over large streams and rivers where sediment transport and channel alignment changes can affect the structural stability of bridges. Structural stability during and immediately after peak streamflow can be assessed by measuring streambed scour; however, placing personnel or boats in the water during high-streamflow events using traditional methods can be difficult, hazardous, and time consuming. To address this need, the U.S. Geological Survey, in cooperation with Colorado Department of Transportation, installed instrumentation at two bridges in western Colorado to measure streambed elevations in real-time during snowmelt-runoff periods (May through June) in 2016 and 2017. The bridges include U.S. Highway 50 eastbound over the Gunnison River at milepost 70.0 (bridge I–04–K) and Colorado Highway 141 over the Gunnison River at milepost 153.7 (bridge I–03–A).
Bridge I–04–K was outfitted with two echosounders, each mounted on the north side of pier 3. Data collected during the 2016 snowmelt runoff did not indicate scour had occurred. Data collected during 2017 snowmelt runoff indicated minor scour and fill occurred under the downstream echosounder.
Bridge I–03–A was outfitted with two echosounders, each mounted on opposite sides of pier 4, at the transition of the upstream nose to the straight section of the pier wall. Data recorded during 2016 did not indicate any scour under the echosounders. Debris accumulation around the nose of the pier and under the echosounders resulted in inconsistent streambed elevation data. Data recorded during 2017 did not indicate any scour under the echosounders. Probing of the pier wall and streambed interface and underwater photographs obtained in 2016 revealed undermining along the length of the pier wall. The undermining extended side-to-side to a depth of about 2 feet. Underwater photographs were obtained again in 2017; no changes from the previous year were observed.
Cross-section surveys were completed at each bridge to measure and document changes in channel geometry during the study. Surveys were performed in spring 2016 before snowmelt runoff, spring 2017 before snowmelt runoff, and fall 2017. Streambed elevations from cross-section surveys at both bridges were evaluated using two-tailed, paired t-tests and Wilcoxon rank sum tests to identify significant changes between the surveys. Both tests indicated significant changes in mean streambed elevations for the cross-sections and around the monitored piers at bridges I–04–K and I–03–A during the 2-year study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185123","collaboration":"Prepared in cooperation with the Colorado Department of Transportation","usgsCitation":"Henneberg, M.F., 2018, Real-time streambed scour monitoring at two bridges over the Gunnison River in western Colorado, 2016–17: U.S. Geological Survey Scientific Investigation Report 2018–5123, 15 p., https://doi.org/10.3133/sir20185123.","productDescription":"Report: v, 15 p.; Data release","onlineOnly":"Y","ipdsId":"IP-093925","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":437646,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92EY47R","text":"USGS data release","linkHelpText":"Cross-Section Geometry at Two Bridges over the Gunnison River in Western Colorado, 2016-17"},{"id":360449,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5123/sir20185123.pdf","text":"Report","size":"5.90 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5123"},{"id":360530,"rank":3,"type":{"id":30,"text":"Data Release"},"url":" https://doi.org/10.5066/P92EY47R","text":"USGS data release","linkHelpText":"Cross-Section Geometry at Two Bridges over the Gunnison River in Western Colorado, 2016–17"},{"id":360448,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5123/coverthb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Gunnison River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.8333,\n 39.6667\n ],\n [\n -107.8333,\n 39.6667\n ],\n [\n -107.8333,\n 39.1667\n ],\n [\n -108.8333,\n 39.1667\n ],\n [\n -108.8333,\n 39.6667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS 415
Denver, CO 80225
A series of laboratory experiments was conducted to better understand the behavior of grass carp eggs and larvae in moving water in order to develop and implement new strategies for control and prediction of their dispersal and drift at early life stages. Settling velocity and density of a representative sample of eggs were estimated, and three trials of flume experiments with different flow conditions were conducted with live eggs in a temperature-controlled setting with a mobile sediment bed. In these trials, egg and larval stages were continuously analyzed over periods of 80 hours; and eggs and larvae interactions with the flow and sediment bed were monitored and characterized qualitatively and quantitatively. Survival rates were quantified after each trial, highlighting physical causes for increased mortality. Detailed flow analysis was correlated to the observed drifting and swimming behavior of eggs and larvae, to estimate distributions across the water depth, as well as traveling and swimming speeds. Evidence of the influence of mean and turbulent flow in the suspension and transport of eggs are reported, and swimming patterns of larvae at different developmental stages are described. These findings support the development of new strategies for monitoring the spread of grass carp eggs and larvae in rivers, and provide new inputs to predict conditions favorable for spawning and hatching, allowing for mitigation measures at early life stages, which are critical to control their dispersal.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0208326","usgsCitation":"Prada, A.F., George, A.E., Stahlschmidt, B.H., Chapman, D., and Tinoco, R.O., 2018, Survival and drifting patterns of grass carp eggs and larvae in response to interactions with flow and sediment in a laboratory flume: PLoS ONE, v. 13, no. 12, p. 1-19, https://doi.org/10.1371/journal.pone.0208326.","productDescription":"e0208326; 19 p.","startPage":"1","endPage":"19","ipdsId":"IP-098037","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":468180,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0208326","text":"Publisher Index Page"},{"id":360663,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"12","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-19","publicationStatus":"PW","scienceBaseUri":"5c1e0a2fe4b0708288cb020f","contributors":{"authors":[{"text":"Prada, Andres F.","contributorId":211778,"corporation":false,"usgs":false,"family":"Prada","given":"Andres","email":"","middleInitial":"F.","affiliations":[{"id":38317,"text":"Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL","active":true,"usgs":false}],"preferred":false,"id":754851,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"George, Amy E. 0000-0003-1150-8646 ageorge@usgs.gov","orcid":"https://orcid.org/0000-0003-1150-8646","contributorId":3950,"corporation":false,"usgs":true,"family":"George","given":"Amy","email":"ageorge@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":754850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stahlschmidt, Benjamin H. 0000-0001-6197-662X","orcid":"https://orcid.org/0000-0001-6197-662X","contributorId":211250,"corporation":false,"usgs":true,"family":"Stahlschmidt","given":"Benjamin","email":"","middleInitial":"H.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":754852,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chapman, Duane 0000-0002-1086-8853 dchapman@usgs.gov","orcid":"https://orcid.org/0000-0002-1086-8853","contributorId":1291,"corporation":false,"usgs":true,"family":"Chapman","given":"Duane","email":"dchapman@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":754853,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tinoco, Rafael O.","contributorId":211779,"corporation":false,"usgs":false,"family":"Tinoco","given":"Rafael","email":"","middleInitial":"O.","affiliations":[{"id":38317,"text":"Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL","active":true,"usgs":false}],"preferred":false,"id":754854,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70201545,"text":"ofr20181193 - 2018 - Groundwater, surface-water, and water-chemistry data, Black Mesa area, northeastern Arizona—2015–2016","interactions":[],"lastModifiedDate":"2021-03-22T15:31:22.456246","indexId":"ofr20181193","displayToPublicDate":"2018-12-19T09:11:47","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1193","displayTitle":"Groundwater, Surface-Water, and Water-Chemistry Data, Black Mesa Area, Northeastern Arizona—2015–2016","title":"Groundwater, surface-water, and water-chemistry data, Black Mesa area, northeastern Arizona—2015–2016","docAbstract":"The Navajo (N) aquifer is an extensive aquifer and the primary source of groundwater in the 5,400-square-mile Black Mesa area in northeastern Arizona. Availability of water is an important issue in the Black Mesa area because of continued water requirements for industrial and municipal use by a growing population and because of the arid climate. Precipitation in the area typically ranges from less than 6 to more than 16 inches per year depending on location.
The U.S. Geological Survey water-monitoring program in the Black Mesa area began in 1971 and provides information about the long-term effects of groundwater withdrawals from the N aquifer for industrial and municipal uses. This report presents results of data collected as part of the monitoring program in the Black Mesa area from November 2015 to December 2016. The monitoring program includes measurements of (1) groundwater withdrawals (pumping), (2) groundwater levels, (3) spring discharge, (4) surface-water discharge, and (5) groundwater chemistry.
In calendar year 2016, total groundwater withdrawals were 3,540 acre-ft, industrial withdrawals were 1,090 acre-ft, and municipal withdrawals were 2,450 acre-ft. Total withdrawals during 2016 were about 52 percent less than total withdrawals in 2005 because of Peabody Western Coal Company’s discontinued use of water to transport coal in a coal slurry pipeline.
From 2015 to 2016, annually measured water levels available for comparison in wells completed in the unconfined areas of the N aquifer within the Black Mesa area declined in 9 of 16 wells, and the median change was –0.1 feet. Water levels also declined in 8 of 16 wells measured in the confined area of the aquifer. The median change for the confined area of the aquifer was 0.0 feet. From the prestress period (prior to 1965) to 2016, the median water-level change for all 32 wells in both the confined and unconfined areas was –10.2 feet; the median water-level changes were –1.6 feet for the 16 wells measured in the unconfined areas and –36.1 feet for the 16 wells measured in the confined area.
Spring flow was measured at four springs in 2016. Flow fluctuated during the period of record for Burro Spring and Pasture Canyon Spring, but a decreasing trend was statistically significant (p<0.05) at Moenkopi School Spring and Unnamed Spring near Dennehotso. Discharge at Burro Spring has remained relatively constant since it was first measured in the 1980s and discharge at Pasture Canyon Spring has fluctuated for the period of record.
Continuous records of surface-water discharge in the Black Mesa area were collected from streamflow-gaging stations at the following sites: Moenkopi Wash at Moenkopi 09401260 (1976 to 2016), Dinnebito Wash near Sand Springs 09401110 (1993 to 2016), Polacca Wash near Second Mesa 09400568 (1994 to 2016), and Pasture Canyon Springs 09401265 (2004 to 2016). Median winter flows (November through February) of each water year were used as an index of the amount of groundwater discharge at the above-named sites. For the period of record, the median winter flows have generally remained constant at Dinnebito Wash and Polacca Wash, whereas a decreasing trend was indicated at Moenkopi Wash and Pasture Canyon Springs.
In 2016, water samples collected from three wells and four springs in the Black Mesa area were analyzed for selected chemical constituents, and the results were compared with previous analyses from the same wells and springs. Concentrations of dissolved solids, chloride, and sulfate have varied at all three wells for the period of record, but neither increasing nor decreasing trends over time were found. Dissolved solids, chloride, and sulfate concentrations increased at Moenkopi School Spring during the more than 25 years of record at that site. Concentrations of dissolved solids, chloride, and sulfate at Pasture Canyon Spring have not varied significantly (p>0.05) since the early 1980s, and there is no increasing or decreasing trend in those data. Concentrations of dissolved solids, chloride, and sulfate at Burro Spring and Unnamed Spring near Dennehotso have varied for the period of record, but there is no statistical trend in the data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181193","collaboration":"Prepared in cooperation with the Navajo Nation and the Arizona Department of Water Resources","usgsCitation":"Mason, J.P., and Macy, J.P., 2018, Groundwater, surface-water, and water-chemistry data, Black Mesa area, northeastern Arizona—2015–2016: U.S. Geological Survey Open-File Report 2018–1193, 60 p., https://doi.org/10.3133/ofr20181193.","productDescription":"vii, 60 p.","onlineOnly":"Y","ipdsId":"IP-097246","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":384544,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211124","text":"Open-File Report 2021-1124","linkHelpText":"- Groundwater, Surface-Water, and Water-Chemistry Data, Black Mesa Area, Northeastern Arizona—2016–2018"},{"id":360531,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1193/coverthb.jpg"},{"id":360532,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1193/ofr20181193.pdf","text":"Report","size":"6.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2018-1193"}],"country":"United States","state":"Arizona","otherGeospatial":"Black Mesa Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.5,\n 35.5\n ],\n [\n -109.5,\n 35.5\n ],\n [\n -109.5,\n 37\n ],\n [\n -111.5,\n 37\n ],\n [\n -111.5,\n 35.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
It is asserted that reduction or elimination of hatchery stocking will increase natural‐origin salmon Oncorhynchus spp. and steelhead O. mykiss production. We conducted an analysis of steelhead population census data (1958–2017) to determine whether elimination of summer steelhead stocking in the upper Clackamas River in 1998 increased the productivity of natural‐origin winter steelhead. A Bayesian state–space stock–recruitment model was fitted to the adult steelhead data set, and productivity was estimated as a function of hatchery‐origin spawner abundance as well as other environmental factors. When used as a predictive variable in our model, the abundance of hatchery summer steelhead spawners (1972–2001) did not have a negative effect on winter steelhead recruitment. However, spill at North Fork Dam (the gateway to the upper Clackamas River basin) and the Pacific Decadal Oscillation (an index of ocean conditions) were both negatively associated with winter steelhead recruitment. Moreover, winter steelhead abundance in the upper Clackamas River basin failed to rebound to abundances observed in years prior to the hatchery program, and fluctuations in winter steelhead abundance were correlated with those of other regional winter steelhead stocks. Our assessment underscores the need for studies that (1) directly quantify the effects of hatchery fish on the production of natural‐origin salmon and steelhead, (2) empirically test published theories about mechanisms of hatchery fish impacts on natural‐origin populations, and (3) document population responses to major changes in hatchery programs.
","language":"English","publisher":"American Fisheries Society ","doi":"10.1002/tafs.10140","usgsCitation":"Courter, I.I., Wyatt, G.J., Perry, R., Plumb, J., Carpenter, F.M., Ackerman, N.K., Lessard, R.B., and Galbreath, P., 2018, A natural‐origin steelhead population's response to exclusion of hatchery fish: Transactions of the American Fisheries Society, v. 148, no. 2, p. 339-351, https://doi.org/10.1002/tafs.10140.","productDescription":"13 p.","startPage":"339","endPage":"351","ipdsId":"IP-098370","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":468181,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10140","text":"Publisher Index Page"},{"id":363055,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":" Clackamas River ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.73376464843749,\n 45.0657615477031\n ],\n [\n -121.73263549804688,\n 45.0657615477031\n ],\n [\n -121.73263549804688,\n 45.45724086262233\n ],\n [\n -122.73376464843749,\n 45.45724086262233\n ],\n [\n -122.73376464843749,\n 45.0657615477031\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"148","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Courter, Ian I","contributorId":214903,"corporation":false,"usgs":false,"family":"Courter","given":"Ian","email":"","middleInitial":"I","affiliations":[{"id":39134,"text":"Mount Hood Environmental, P.O. Box 744, Boring, Oregon 97009","active":true,"usgs":false}],"preferred":false,"id":761132,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wyatt, Garth J","contributorId":214904,"corporation":false,"usgs":false,"family":"Wyatt","given":"Garth","email":"","middleInitial":"J","affiliations":[{"id":39135,"text":"Portland General Electric, 33831 Faraday Rd., Estacada, Oregon 97023","active":true,"usgs":false}],"preferred":false,"id":761133,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perry, Russell 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":214905,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":761134,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Plumb, John 0000-0003-4255-1612 jplumb@usgs.gov","orcid":"https://orcid.org/0000-0003-4255-1612","contributorId":214906,"corporation":false,"usgs":true,"family":"Plumb","given":"John","email":"jplumb@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":761135,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carpenter, Forrest M","contributorId":214907,"corporation":false,"usgs":false,"family":"Carpenter","given":"Forrest","email":"","middleInitial":"M","affiliations":[{"id":39134,"text":"Mount Hood Environmental, P.O. Box 744, Boring, Oregon 97009","active":true,"usgs":false}],"preferred":false,"id":761136,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ackerman, Nicklaus K","contributorId":214552,"corporation":false,"usgs":false,"family":"Ackerman","given":"Nicklaus","email":"","middleInitial":"K","affiliations":[{"id":39068,"text":"Portland General Electric, Estacada, OR","active":true,"usgs":false}],"preferred":false,"id":761137,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lessard, Robert B","contributorId":214908,"corporation":false,"usgs":false,"family":"Lessard","given":"Robert","email":"","middleInitial":"B","affiliations":[{"id":39136,"text":"Columbia River Intertribal Fish Commission, 700 NE Multnomah St., Portland, Oregon 97232","active":true,"usgs":false}],"preferred":false,"id":761138,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Galbreath, Peter F","contributorId":214909,"corporation":false,"usgs":false,"family":"Galbreath","given":"Peter F","affiliations":[{"id":39136,"text":"Columbia River Intertribal Fish Commission, 700 NE Multnomah St., Portland, Oregon 97232","active":true,"usgs":false}],"preferred":false,"id":761139,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70201105,"text":"sir20185162 - 2018 - Simulation of groundwater storage changes in the Quincy Basin, Washington","interactions":[],"lastModifiedDate":"2018-12-19T15:47:47","indexId":"sir20185162","displayToPublicDate":"2018-12-18T15:30:10","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5162","displayTitle":"Simulation of Groundwater Storage Changes in the Quincy Basin, Washington","title":"Simulation of groundwater storage changes in the Quincy Basin, Washington","docAbstract":"The Miocene Columbia River Basalt Group and younger sedimentary deposits of lacustrine, fluvial, eolian, and cataclysmic-flood origins compose the aquifer system of the Quincy Basin in eastern Washington. Irrigation return flow and canal leakage from the Columbia Basin Project have caused groundwater levels to rise substantially in some areas. Water resource managers are considering extraction of additional stored groundwater to supply increasing demand. To help address these concerns, the transient groundwater model of the Quincy Basin documented in this report was developed to quantify the changes in groundwater flow and storage.
The model based on the U.S. Geological Survey modular three-dimensional finite-difference numerical code MODFLOW uses a 1-kilometer finite-difference grid and is constrained by logs from 698 wells in the study area. Five model layers represent two sedimentary hydrogeologic units and underlying basalt formations. Head-dependent flux boundaries represent the Columbia River and other streams, lakes and reservoirs, underflow to and (or) from adjacent areas, and discharge to agricultural drains and springs. Specified flux boundaries represent recharge from precipitation and anthropogenic sources, including irrigation return flow and leakage from water-distribution canals and discharge through groundwater withdrawal wells. Transient conditions were simulated from 1920 to 2013 using annual stress periods. The model was calibrated with the parameter-estimation code PEST to a total of 4,064 water levels measured in 710 wells. Increased recharge since predevelopment resulted in an 11.5 million acre-feet increase in storage in the Quincy Groundwater Management Subarea of the Quincy Basin.
Four groundwater-management scenarios were formulated with input from project stakeholders and were simulated using the calibrated model to provide representative examples of how the model could be used to evaluate the effect on groundwater levels as a result of potential changes in recharge, groundwater withdrawals, or increased flow in Crab Creek. Decreased recharge and increased groundwater withdrawals both resulted in declines in groundwater levels over 2013 conditions, whereas increasing the flow in Crab Creek resulted in increased groundwater levels over 2013 conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185162","collaboration":"Prepared in cooperation with the Washington State Department of Ecology and the Bureau of Reclamation","usgsCitation":"Frans, L.M., Kahle, S.C., Tecca, A.E., and Olsen, T.D., 2018, Simulation of groundwater storage changes in the Quincy Basin, Washington: U.S. Geological Survey Scientific Investigations Report 2018-5162, 63 p., https://doi.org/10.3133/sir20185162.","productDescription":"Report: viii, 63 p.; Model archive","onlineOnly":"Y","ipdsId":"IP-098440","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":437647,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MCIR8M","text":"USGS data release","linkHelpText":"MODFLOW-NWT model used to simulate groundwater storage changes in the Quincy Basin, Washington"},{"id":360527,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5162/coverthb.jpg"},{"id":360528,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5162/sir20185162.pdf","text":"Report","size":"17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5162"},{"id":360529,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://doi.org/10.5066/P9MCIR8M","text":"USGS model archive —","description":"USGS Model Archive","linkHelpText":"MODFLOW-NWT model used in Simulation of Groundwater Storage Changes in the Quincy Basin, Washington"}],"country":"United States","state":"Washington","otherGeospatial":"Quincy Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.13275146484374,\n 46.61548796222358\n ],\n [\n -118.52874755859376,\n 46.61548796222358\n ],\n [\n -118.52874755859376,\n 47.615421267605434\n ],\n [\n -120.13275146484374,\n 47.615421267605434\n ],\n [\n -120.13275146484374,\n 46.61548796222358\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Animals co‐occurring in a region (sympatry) may use the same habitat (syntopy) within that region. A central aim in ecology is determining what factors drive species distributions (i.e., abiotic conditions, dispersal limitations, and/or biotic interactions). Assessing the degree of biotic interactions can be difficult for species with wide ranges at sea. This study investigated the spatial ecology of two sea turtle species that forage on benthic invertebrates in neritic GoM waters: Kemp's ridleys (Lepidochelys kempii) and loggerheads (Caretta caretta). We used satellite tracking and modeled behavioral modes, then calculated individual home ranges, compared foraging areas, and determined extent of co‐occurrence. Using six environmental variables and principal component analysis, we assessed similarity of chosen foraging sites. We predicted foraging location (eco‐region) based on species, nesting site, and turtle size. For 127 turtles (64 Kemp's ridleys, 63 loggerheads) tracked from 1989 to 2013, foraging home ranges were nine to ten times larger for Kemp's ridleys than for loggerheads. Species intersected off all U.S. coasts and the Yucatán Peninsula, but co‐occurrence areas were small compared to species' distributions. Kemp's ridley foraging home ranges were concentrated in the northern GoM, whereas those for loggerheads were concentrated in the eastern GoM. The two species were different in all habitat variables compared (latitude, longitude, distance to shore, net primary production, mean sea surface temperature, and bathymetry). Nesting site was the single dominant variable that dictated foraging ecoregion. Although Kemp's ridleys and loggerheads may compete for resources, the separation in foraging areas, significant differences in environmental conditions, and importance of nesting location on ecoregion selection (i.e., dispersal ability) indicate that adult females of these species do not interact greatly during foraging and that dispersal and environmental factors more strongly determine their distributions. These species show sympatry in this region but evidence for syntopy was rare.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.4691","usgsCitation":"Hart, K., Iverson, A., Fujisaki, I., Lamont, M.M., Bucklin, D.N., and Shaver, D.J., 2018, Sympatry or syntopy? Investigating drivers of distribution and co‐occurrence for two imperiled sea turtle species in Gulf of Mexico neritic waters: Ecology and Evolution, v. 8, no. 24, p. 12656-12669, https://doi.org/10.1002/ece3.4691.","productDescription":"14 p.","startPage":"12656","endPage":"12669","ipdsId":"IP-091384","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":468182,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.4691","text":"Publisher Index Page"},{"id":360480,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99,\n 18\n ],\n [\n -81,\n 18\n ],\n [\n -81,\n 32\n ],\n [\n -99,\n 32\n ],\n [\n -99,\n 18\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"24","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-26","publicationStatus":"PW","scienceBaseUri":"5c1a1530e4b0708288c23514","contributors":{"authors":[{"text":"Hart, Kristen M. 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":209782,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":754495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iverson, Autumn R. 0000-0002-8353-6745","orcid":"https://orcid.org/0000-0002-8353-6745","contributorId":173555,"corporation":false,"usgs":false,"family":"Iverson","given":"Autumn R.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":754496,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fujisaki, Ikuko","contributorId":38359,"corporation":false,"usgs":false,"family":"Fujisaki","given":"Ikuko","affiliations":[],"preferred":false,"id":754497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lamont, Margaret M. 0000-0001-7520-6669 mlamont@usgs.gov","orcid":"https://orcid.org/0000-0001-7520-6669","contributorId":4525,"corporation":false,"usgs":true,"family":"Lamont","given":"Margaret","email":"mlamont@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":754498,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bucklin, David N.","contributorId":175273,"corporation":false,"usgs":false,"family":"Bucklin","given":"David","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":754499,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shaver, Donna J.","contributorId":191186,"corporation":false,"usgs":false,"family":"Shaver","given":"Donna","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":754500,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70201248,"text":"fs20183078 - 2018 - Comparing groundwater quality in public-supply and shallow aquifers in the Monterey Bay and Salinas Valley Basins, California","interactions":[],"lastModifiedDate":"2018-12-18T16:15:02","indexId":"fs20183078","displayToPublicDate":"2018-12-18T13:46:42","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-3078","displayTitle":"Comparing Groundwater Quality in Public-Supply and Shallow Aquifers in the Monterey Bay and Salinas Valley Basins, California","title":"Comparing groundwater quality in public-supply and shallow aquifers in the Monterey Bay and Salinas Valley Basins, California","docAbstract":"Groundwater provides more than 40 percent of California’s drinking water. To protect this vital resource, the State of California created the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The Priority Basin Project of the GAMA Program (GAMA-PBP) provides a comprehensive assessment of the State’s groundwater quality and increases public access to groundwater-quality information.
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The Yankee Fork of the Salmon River is one of the larger watersheds in the upper Salmon River subbasin of central Idaho. Mining activities since the late 19th century, specifically placer mining and associated dredging from 1940 to 1953, have left the fluvial system in a highly altered and unnatural state. To improve aquatic and terrestrial habitat in the Yankee Fork, the Bureau of Reclamation and other stakeholders collaborated on the Dredge Tailings Restoration Project and Yankee Fork Rehabilitation Project. In conjunction with these rehabilitation efforts, the U.S. Geological Survey monitored suspended-sediment transport and discharge between 2012 and 2015 at three sites in the lower reaches of the Yankee Fork. Pseudo-hydrographs were developed for the Bonanza and Confluence sites using data from the streamgage site as a surrogate. Results showed a good fit between measured and calculated discharge with R2 values of 0.96 for the Bonanza site and 0.98 for the Confluence site. Both regressions have high hypothesis test statistics (t>23) and low probability values (p<0.0001), indicating a strong linear correlation. Suspended-sediment samples collected mostly during snowmelt runoff showed a positive correlation with stream discharge. Hysteresis in the sample results indicates a supply-limited suspended-sediment transport regime. Percent sand by weight of suspended-sediment samples identified a possible discharge threshold for sand suspension at about 400 cubic feet per second (ft3/s) at the Bonanza site and about 1,000 ft3/s at the Confluence and Gage sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181189","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Johnsen, J.W., 2018, Sediment transport monitoring of the Yankee Fork of the Salmon River near Stanley, Idaho, 2012–15: U.S. Geological Survey Open-File Report 2018-1189, 17 p., https://doi.org/10.3133/ofr20181189.","productDescription":"iv, 17 p.","onlineOnly":"Y","ipdsId":"IP-090600","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":360434,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1189/coverthb.jpg"},{"id":360435,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1189/ofr20181189.pdf","text":"Report","size":"12.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1189"}],"country":"United States","state":"Idaho","city":"Stanley","otherGeospatial":"Salmon River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.4892,\n 43.5222\n ],\n [\n -114.2392,\n 43.5222\n ],\n [\n -114.2392,\n 44.7725\n ],\n [\n -115.4892,\n 44.7725\n ],\n [\n -115.4892,\n 43.5222\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
Researchers from the U.S. Geological Survey (USGS) conducted a long-term coastal morphologic-change study at Fire Island, New York, prior to and after Hurricane Sandy impacted the area in October 2012. The Fire Island Coastal Change project objectives include understanding the morphologic evolution of the barrier island system on a variety of time scales (months to centuries) and resolving storm-related effects, post-storm beach response, and recovery. In April 2016, scientists from the USGS St. Petersburg Coastal and Marine Science Center conducted sediment sampling and geophysical surveys on Fire Island to characterize and quantify spatial variability in the subaerial geology with the goal of subsequently integrating onshore geology with other surf zone and nearshore datasets.
This report, along with the accompanying USGS data release, serves as an archive of sediment data from 14 vibracores collected on April 10 and 11, 2016 (USGS Field Activity Number 2016–322–FA), along 6 transects that extend from the upper to lower subaerial shoreface at Fire Island. Sedimentologic and stratigraphic metrics (for example, sediment texture or unit thicknesses) derived from these data can be used to assess spatial and temporal trends and may aid in understanding beach evolution. Data products include sample location tables, descriptive core logs, core photographs, results of sediment grain-size analyses, and geographic information system data files with accompanying formal Federal Geographic Data Committee metadata.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1100","usgsCitation":"Buster, N.A., Bernier, J.C., Brenner, O.T., Kelso, K.W., Tuten, T.M., and Miselis, J.L., 2018, Sediment data from vibracores collected in 2016 from Fire Island, New York: U.S. Geological Survey Data Series 1100, https://doi.org/10.3133/ds1100.","productDescription":"HTML Document; Data Release","ipdsId":"IP-099448","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":437649,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91P1T88","text":"USGS data release","linkHelpText":"Sediment Data From Vibracores and Sand Augers Collected in 2021 and 2022 From Fire Island, New York"},{"id":359473,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1100/coverthb.jpg"},{"id":359475,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FN15GX","text":"USGS data release","description":"USGS data release"},{"id":359474,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1100/","text":"Report HTML","linkFileType":{"id":5,"text":"html"},"description":"DS 1100"}],"country":"United States","state":"New York","otherGeospatial":"Fire Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.32069396972655,\n 40.60926110386811\n ],\n [\n -72.74871826171875,\n 40.60926110386811\n ],\n [\n -72.74871826171875,\n 40.77534183237267\n ],\n [\n -73.32069396972655,\n 40.77534183237267\n ],\n [\n -73.32069396972655,\n 40.60926110386811\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701