In this dataset description paper we introduce the GNIS-LD, an authoritative and public domain Linked Dataset derived from the Geographic Names Information System (GNIS) which was developed by the U.S. Geological Survey (USGS) and the U.S. Board on Geographic Names. GNIS provides data about current, as well as historical, physical, and cultural geographic features in the United States. We describe the dataset, introduce an ontology for geographic feature types, and demonstrate the utility of recent linked geographic data contributions made in conjunction with the development of this resource. Co-reference resolution links to GeoNames.org and DBpedia are provided in the form of owl:sameAs relations. Finally, we point out how the adapted workflow is foundational for complex Digital Line Graph (DLG) data from the USGS National Map and how the GNIS-LD data can be integrated with DLG and other data sources such as sensor observations.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The semantic web; 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, Proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"15th International Conference, ESWC 2018","conferenceDate":"Heraklion, Crete, Greece","conferenceLocation":"June 3–7, 2018","language":"English","publisher":"Springer","doi":"10.1007/978-3-319-93417-4_34","usgsCitation":"Regalia, B., Janowicz, K., Mai, G., Varanka, D.E., and Usery, E., 2018, GNIS-LD: Serving and visualizing the Geographic Names Information System Gazetteer as linked data, in The semantic web; 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, Proceedings, June 3–7, 2018, Heraklion, Crete, Greece, p. 528-540, https://doi.org/10.1007/978-3-319-93417-4_34.","productDescription":"13 p.","startPage":"528","endPage":"540","ipdsId":"IP-087285","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":360158,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-03","publicationStatus":"PW","scienceBaseUri":"5c10a8e5e4b034bf6a7e4dd8","contributors":{"authors":[{"text":"Regalia, Blake","contributorId":211369,"corporation":false,"usgs":false,"family":"Regalia","given":"Blake","email":"","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":753807,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Janowicz, Krzysztof","contributorId":149671,"corporation":false,"usgs":false,"family":"Janowicz","given":"Krzysztof","email":"","affiliations":[],"preferred":false,"id":753808,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mai, Gengchen","contributorId":211370,"corporation":false,"usgs":false,"family":"Mai","given":"Gengchen","email":"","affiliations":[],"preferred":false,"id":753810,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Varanka, Dalia E. 0000-0003-2857-9600 dvaranka@usgs.gov","orcid":"https://orcid.org/0000-0003-2857-9600","contributorId":1296,"corporation":false,"usgs":true,"family":"Varanka","given":"Dalia","email":"dvaranka@usgs.gov","middleInitial":"E.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":753806,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Usery, E. Lynn 0000-0002-2766-2173","orcid":"https://orcid.org/0000-0002-2766-2173","contributorId":204684,"corporation":false,"usgs":true,"family":"Usery","given":"E. Lynn","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":753809,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70201572,"text":"70201572 - 2018 - The National Elevation Dataset","interactions":[],"lastModifiedDate":"2018-12-20T11:11:14","indexId":"70201572","displayToPublicDate":"2018-12-01T11:11:10","publicationYear":"2018","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"The National Elevation Dataset","docAbstract":"The National Elevation Dataset (NED) is a primary elevation data product that has been produced and distributed by the U.S. Geological Survey (USGS). Since its inception, the USGS has compiled and published topographic information in many forms, and the NED is a significant development in this long line of products that describe the land surface. The NED provides seamless raster elevation data of the conterminous United States (CONUS), Alaska, Hawaii, U.S. island territories, Mexico, and Canada. The NED is derived from diverse source datasets that are processed to a specification with consistent resolutions, coordinate system, elevation units, and horizontal and vertical datums. The NED was developed as the logical result of the maturation of the long-standing USGS elevation program, which for many years concentrated on production of quadrangle-based digital elevation models (DEM). The NED contributes to the elevation layer of The National Map, and it provides basic elevation information for earth science studies and mapping applications in the U.S. and most of North America.\n For over 15 years (1999–2014), the NED served as the flagship elevation product of the USGS. In 2015, the 3D Elevation Program (3DEP) was initiated. When the 3DEP initiative became operational, the name “National Elevation Dataset” (and the abbreviation “NED”) were retired as the USGS elevation activities and data were rebranded under the 3DEP banner. However, elevation data produced and distributed as part of the NED are still widely used (and distributed by other entities), so there is a continuing need for detailed documentation, including how it was produced, its accuracy, and how it is used. This chapter directly addresses that need for detailed information about the NED. The most recent detailed description of the NED appeared in the 2nd edition of the DEM Users Manual (2007), and because NED production continued through 2014, the details reported herein provide valuable information for data accessed by the user community from 2007 through 2014. The NED has been widely used in operational applications and research studies and is extensively cited in reports on those activities, so it is important for the user community to have access to information about the NED to better judge how its qualities and characteristics might affect results derived from its use as the elevation data source. Additionally, the NED seamless layers serve as one of the input data sources for the current 3DEP elevation production system, so, as with any input data source, an understanding of the data characteristics is critical.","language":"English","publisher":"American Society for Photogrammetry and Remote Sensing","usgsCitation":"Gesch, D.B., Evans, G.A., Oimoen, M., and Arundel, S., 2018, The National Elevation Dataset, p. 83-110.","productDescription":"28 p.","startPage":"83","endPage":"110","ipdsId":"IP-051285","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":360618,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":360617,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.asprs.org/dem"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c1cb860e4b0708288c8382d","contributors":{"authors":[{"text":"Gesch, Dean B. 0000-0002-8992-4933 gesch@usgs.gov","orcid":"https://orcid.org/0000-0002-8992-4933","contributorId":2956,"corporation":false,"usgs":true,"family":"Gesch","given":"Dean","email":"gesch@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":754462,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evans, Gayla A. 0000-0001-5072-4232 gevans@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-4232","contributorId":3125,"corporation":false,"usgs":true,"family":"Evans","given":"Gayla","email":"gevans@usgs.gov","middleInitial":"A.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":754463,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oimoen, Michael J. 0000-0003-3611-6227","orcid":"https://orcid.org/0000-0003-3611-6227","contributorId":211599,"corporation":false,"usgs":true,"family":"Oimoen","given":"Michael J.","affiliations":[{"id":38270,"text":"SGT Inc., contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":754464,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arundel, Samantha T. 0000-0002-4863-0138 sarundel@usgs.gov","orcid":"https://orcid.org/0000-0002-4863-0138","contributorId":192598,"corporation":false,"usgs":true,"family":"Arundel","given":"Samantha","email":"sarundel@usgs.gov","middleInitial":"T.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":754465,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70202188,"text":"70202188 - 2018 - Analysis of population change and movement using robust design removal data","interactions":[],"lastModifiedDate":"2019-02-13T11:07:05","indexId":"70202188","displayToPublicDate":"2018-12-01T11:06:54","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2151,"text":"Journal of Agricultural, Biological, and Environmental Statistics","active":true,"publicationSubtype":{"id":10}},"title":"Analysis of population change and movement using robust design removal data","docAbstract":"In capture-mark-reencounter studies, Pollock’s robust design combines methods for open populations with methods for closed populations. Open population features of the robust design allow for estimation of rates of death or permanent emigration, and closed population features enhance estimation of population sizes. We describe a similar design, but for use with removal data. Data collection occurs on secondary sampling occasions clustered within primary sampling periods. Primary sampling periods are intervals of brief enough duration that it can be safely assumed that the population is unchanged by births, deaths, immigration or emigration during them; all population change and movement occurs between primary sampling periods. Our model provides a basis for inference about population size, changes in population size, and movement rates among sample locations between primary sampling periods. Movement rates are modeled as functions of distance and time. Capture probabilities are modeled as a function of effort. We apply the model to data obtained in attempting to eradicate an introduced population of veiled chameleons (Chamaeleo calyptratus) on the island of Maui in Hawaii.
","language":"English","publisher":"Springer","doi":"10.1007/s13253-018-0335-8","usgsCitation":"Link, W.A., Converse, S.J., Yackel Adams, A.A., and Hostetter, N.J., 2018, Analysis of population change and movement using robust design removal data: Journal of Agricultural, Biological, and Environmental Statistics, v. 23, no. 4, p. 463-477, https://doi.org/10.1007/s13253-018-0335-8.","productDescription":"15 p.","startPage":"463","endPage":"477","ipdsId":"IP-087462","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":437666,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Z31X54","text":"USGS data release","linkHelpText":"Removal count data of Veiled Chameleons on Maui, 2002-2012"},{"id":361226,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"4","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Link, William A. 0000-0002-9913-0256 wlink@usgs.gov","orcid":"https://orcid.org/0000-0002-9913-0256","contributorId":146920,"corporation":false,"usgs":true,"family":"Link","given":"William","email":"wlink@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":757150,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":757151,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":757152,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hostetter, Nathan J. 0000-0001-6075-2157 nhostetter@usgs.gov","orcid":"https://orcid.org/0000-0001-6075-2157","contributorId":198843,"corporation":false,"usgs":true,"family":"Hostetter","given":"Nathan","email":"nhostetter@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":757153,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70201477,"text":"70201477 - 2018 - Hydrogeochemical controls on brook trout spawning habitats in a coastal stream","interactions":[],"lastModifiedDate":"2018-12-14T10:48:49","indexId":"70201477","displayToPublicDate":"2018-12-01T10:48:42","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Hydrogeochemical controls on brook trout spawning habitats in a coastal stream","docAbstract":"Brook trout (Salvelinus fontinalis) spawn in fall and overwintering egg development can benefit from stable, relatively warm temperatures in groundwater-seepage zones. However, eggs are also sensitive to dissolved oxygen concentration, which may be reduced in discharging groundwater (i.e., seepage). We investigated a 2 km reach of the coastal Quashnet River in Cape Cod, Massachusetts, USA, to relate preferred fish spawning habitats to geology, geomorphology, and discharging groundwater geochemistry. Thermal reconnaissance methods were used to locate zones of rapid groundwater discharge, which were predominantly found along the central channel of a wider stream valley section. Pore-water chemistry and temporal vertical groundwater flux were measured at a subset of these zones during field campaigns over several seasons. Seepage zones in open-valley sub-reaches generally showed suboxic conditions and higher dissolved solutes compared to the underlying glacial outwash aquifer. These discharge zones were cross-referenced with preferred brook trout redds and evaluated during 10 years of observation, all of which were associated with discrete alcove features in steep cutbanks, where stream meander bends intersect the glacial valley walls. Seepage in these repeat spawning zones was generally stronger and more variable than in open-valley sites, with higher dissolved oxygen and reduced solute concentrations. The combined evidence indicates that regional groundwater discharge along the broader valley bottom is predominantly suboxic due to the influence of near-stream organic deposits; trout show no obvious preference for these zones when spawning. However, the meander bends that cut into sandy deposits near the valley walls generate strong oxic seepage zones that are utilized routinely for redd construction and the overwintering of trout eggs. Stable water isotopic data support the conclusion that repeat spawning zones are located directly on preferential discharges of more localized groundwater. In similar coastal systems with extensive valley peat deposits, the specific use of groundwater-discharge points by brook trout may be limited to morphologies such as cutbanks, where groundwater flow paths do not encounter substantial buried organic material and remain oxygen-rich.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/hess-22-6383-2018","usgsCitation":"Briggs, M.A., Harvey, J.W., Hurley, S., Rosenberry, D.O., McCobb, T., Werkema, D.D., and Lane, J., 2018, Hydrogeochemical controls on brook trout spawning habitats in a coastal stream: Hydrology and Earth System Sciences, v. 22, p. 6383-6398, https://doi.org/10.5194/hess-22-6383-2018.","productDescription":"16 p.","startPage":"6383","endPage":"6398","ipdsId":"IP-090873","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":468222,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-22-6383-2018","text":"Publisher Index Page"},{"id":360296,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-10","publicationStatus":"PW","scienceBaseUri":"5c14cfb7e4b006c4f8545d34","contributors":{"authors":[{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":754264,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harvey, Judson W. 0000-0002-2654-9873 jwharvey@usgs.gov","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":1796,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","email":"jwharvey@usgs.gov","middleInitial":"W.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":754265,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hurley, Stephen T.","contributorId":108214,"corporation":false,"usgs":true,"family":"Hurley","given":"Stephen T.","affiliations":[],"preferred":false,"id":754266,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rosenberry, Donald O. 0000-0003-0681-5641 rosenber@usgs.gov","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":1312,"corporation":false,"usgs":true,"family":"Rosenberry","given":"Donald","email":"rosenber@usgs.gov","middleInitial":"O.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":754267,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCobb, Timothy D. 0000-0003-1533-847X","orcid":"https://orcid.org/0000-0003-1533-847X","contributorId":203069,"corporation":false,"usgs":true,"family":"McCobb","given":"Timothy D.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":754268,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Werkema, Dale D.","contributorId":190401,"corporation":false,"usgs":false,"family":"Werkema","given":"Dale","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":754269,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lane, John W. Jr. 0000-0002-3558-243X","orcid":"https://orcid.org/0000-0002-3558-243X","contributorId":210076,"corporation":false,"usgs":true,"family":"Lane","given":"John W.","suffix":"Jr.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":754270,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70195184,"text":"70195184 - 2018 - Zone identification and oil saturation prediction in a waterflooded field: Residual oil zone, East Seminole Field, Texas, Permian Basin","interactions":[],"lastModifiedDate":"2019-03-27T10:18:50","indexId":"70195184","displayToPublicDate":"2018-12-01T10:18:41","publicationYear":"2018","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":18,"text":"Abstract or summary"},"displayTitle":"Zone Identification and Oil Saturation Prediction in a Waterflooded Field: Residual Oil Zone, East Seminole Field, Texas, Permian Basin","title":"Zone identification and oil saturation prediction in a waterflooded field: Residual oil zone, East Seminole Field, Texas, Permian Basin","docAbstract":"Recently, the miscible CO2-EOR tertiary process used in the main pay zone (MP) of suitable reservoirs has broadened to include exploitation of the underlying residual oil zone (ROZ) where a significant amount of oil may remain. The objective of this study is to identify the ROZ and to assess the remaining oil in a brownfield ROZ by using core data and conventional well logs with probabilistic and predictive methods.
Core and log data from three wells located in the East Seminole Field in Gaines County, Texas, were used to identify the MP and ROZ in the San Andres Limestone, and to predict oil saturations. The core measurements were used to calculate probabilistic in-situ oil saturations within the MP and the ROZ as a function of depth. Well logs, in combination with core data and calculated saturations, on the other hand, were used to develop two expert systems using artificial neural networks (ANN); one to identify the ROZ and MP, and the other to predict oil saturation. These systems were also supported by a classification and regression tree (CART) analysis to delineate the rules that lead to classifications of zones.
Results showed that expert systems developed and calibrated by combining core and well log data can identify MP and ROZ with a success score of more than 90%. Saturations within these zones can be predicted with a correlation coefficient of around 0.6 for testing and 0.8 for training data. The analyses showed that neutron porosity and density well log readings are the most influential ones to identify zones in this field and to predict oil saturations in the MP and ROZ. To explain the relationships of input data with the results, a rule-based system was also applied, which revealed the underlying petrophysical differences between MP and ROZ.
This new predictive approach using machine learning techniques, could potentially address the challenges that previous studies have come up against in defining the ROZ within the formation and quantifying remaining oil saturations. The method can potentially be applied to additional fields and help reliably identify the ROZ and estimate saturations for future resource evaluations.
A primary goal for Chesapeake Bay watershed restoration is to improve stream health and function in 10% of stream miles by 2025. Predictive spatial modeling of stream conditions, when accurate, is one method to fill gaps in monitoring coverage and estimate baseline conditions for restoration goals. Predictive modeling can also monitor progress as additional data become available. We developed a random forests model to predict biological condition of small streams (<200 km2 in drainage) in the Chesapeake Bay watershed. Biological condition was measured with the Chesapeake Bay Basin-wide Index of Biotic Integrity (Chessie BIBI), a stream macroinvertebrate index. Our goal was to predict biological condition in all unsurveyed small streams present in a 1:24,000 scale catchment layer as a 2004–2008 baseline. We reclassified the 5-category Chessie BIBI ratings into two categories, poor and fair/good, to align with management goals of the Chesapeake Bay Program. The model included 12 geospatial predictor variables including measures on spatial location, bioregion, land cover, soil, precipitation, and number of dams in local catchments. We trained the model with a random 75% subset of Chessie BIBI data (n = 1449), and used the remaining 25% of Chessie BIBI data (n = 484) as test data. The model performed well, correctly predicting 72% of samples in training data and 73% of samples in test data, but model accuracy varied among bioregions. We performed uncertainty analyses by adding bands of either ±0.05 or ±0.10 BIBI units to the cutoff between poor and fair/good. These uncertainty analyses resulted in 14.5% (±0.05 band) and 24.8% (±0.10 band) of samples in test data being classified as in uncertain condition. For 95,877 small stream reaches in the Chesapeake Bay watershed, the model predicted 64% in fair/good condition, the ±0.05 uncertainty analyses predicted 57% in fair/good condition, and the ±0.10 uncertainty analysis predicted 50% in fair/good condition. These reported values have different implications for the number of improved stream miles required to meet the goal of improving 10%. Incorporating uncertainty provides an assessment of model strength as well as confidence in predictions. We, therefore, suggest increased reporting of uncertainty in studies that spatially predict stream conditions.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/700701","usgsCitation":"Maloney, K.O., Smith, Z.M., Buchanan, C., Nagel, A., and Young, J.A., 2018, Predicting biological conditions for small headwater streams in the Chesapeake Bay watershed: Freshwater Science, v. 4, no. 37, p. 795-809, https://doi.org/10.1086/700701.","productDescription":"15 p.","startPage":"795","endPage":"809","ipdsId":"IP-094122","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":460801,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1086/700701","text":"Publisher Index Page"},{"id":362172,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n 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The initial implementation of the database contains data from California. Currently, our prototype data model consists of JavaScript Object Notation (JSON) format files for storing metadata and data. For a site to be included in the database, the minimum metadata requirements are geodetic coordinates and elevation values, and the minimum data requirement is a shear-wave velocity profile. The JSON files are structured in a hierarchal manner to store metadata and data using a nested structure consisting of location, velocity profiles, dispersion curve data (for surface-wave methods), geotechnical data, and horizontal-to-vertical spectral ratios. The database schema at the current stage of the project, and as we continue to develop the data model we will consider including other relevant data, as well as evaluate other file formats to increase the efficiency of data storage and querying. In the current data model, location information includes site geodetic values (latitude, longitude, and elevation) and various site descriptors related to surface geology, geomorphic terrain category, slope gradient at various resolutions, and a geotechnical site category. Velocity data include the geophysical method(s) used to obtain the shear-wave velocity profile, type of data recorded, modeled primary- and shear-wave velocity as a function of depth, modeled profile maximum depth, and the calculated VS30 value. In the case of surface-wave based data, dispersion curve data can be recorded in data structure as phase velocity versus either wavelength or frequency. Geotechnical data includes boring logs penetration resistance, cone penetration test sounding logs, and laboratory index test results. Horizontal-to-vertical spectral ratio plots are given as a function of frequency.
","conferenceTitle":"11th United States National Conference on Earthquake Engineering","conferenceDate":"June 25-29, 2018","conferenceLocation":"Los Angeles, CA","language":"English","publisher":"Earthquake Engineering Research Institute","usgsCitation":"Sadiq, S., Ilkan, O., Ahdi, S.K., Bozorgina, Y., Hashash, Y., Kwak, D., Park, D., Yong, A., and Stewart, J., 2018, A proposed seismic velocity profile database model, 11th United States National Conference on Earthquake Engineering, Los Angeles, CA, June 25-29, 2018, 9 p.","productDescription":"9 p.","ipdsId":"IP-092618","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":481974,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sadiq, Shamsher","contributorId":350844,"corporation":false,"usgs":false,"family":"Sadiq","given":"Shamsher","affiliations":[{"id":83848,"text":"Dept. Civil & Env. Eng., Hanyang Univ.; email: shamshersadi@hanyang.ac.kr","active":true,"usgs":false}],"preferred":false,"id":927112,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ilkan, Okan","contributorId":350848,"corporation":false,"usgs":false,"family":"Ilkan","given":"Okan","affiliations":[{"id":83854,"text":"University of Illinois, Urbana, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":927113,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ahdi, Sean K","contributorId":217355,"corporation":false,"usgs":false,"family":"Ahdi","given":"Sean","email":"","middleInitial":"K","affiliations":[{"id":39605,"text":"Exponent, Inc. and UCLA","active":true,"usgs":false}],"preferred":false,"id":927114,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bozorgina, Yousef","contributorId":271024,"corporation":false,"usgs":false,"family":"Bozorgina","given":"Yousef","email":"","affiliations":[{"id":56148,"text":"University of California, Los Angeles, CA 90095","active":true,"usgs":false}],"preferred":false,"id":927115,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hashash, Youssef M.A.","contributorId":350851,"corporation":false,"usgs":false,"family":"Hashash","given":"Youssef M.A.","affiliations":[{"id":83854,"text":"University of Illinois, Urbana, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":927116,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kwak, Dong Youp","contributorId":350845,"corporation":false,"usgs":false,"family":"Kwak","given":"Dong Youp","affiliations":[{"id":83850,"text":"RMS, Inc., Newark, CA; email: Dongyoup.Kwak@rms.com","active":true,"usgs":false}],"preferred":false,"id":927117,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Park, Duhee","contributorId":350846,"corporation":false,"usgs":false,"family":"Park","given":"Duhee","affiliations":[{"id":83851,"text":"Dept. Civil & Env. Eng., Hanyang Univ.; email: dpark@hanyang.ac.kr","active":true,"usgs":false}],"preferred":false,"id":927118,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yong, Alan 0000-0003-1807-5847","orcid":"https://orcid.org/0000-0003-1807-5847","contributorId":204730,"corporation":false,"usgs":true,"family":"Yong","given":"Alan","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927119,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stewart, Jonathan P.","contributorId":350854,"corporation":false,"usgs":false,"family":"Stewart","given":"Jonathan P.","affiliations":[{"id":83855,"text":"University of California, Los Angeles, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":927120,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70259533,"text":"70259533 - 2018 - Geothermal potential of the Umatilla Indian Reservation, Oregon: Evidence from detailed geophysical investigations","interactions":[],"lastModifiedDate":"2024-10-11T13:36:56.07466","indexId":"70259533","displayToPublicDate":"2018-12-01T08:18:40","publicationYear":"2018","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Geothermal potential of the Umatilla Indian Reservation, Oregon: Evidence from detailed geophysical investigations","docAbstract":"Recent geologic and geophysical investigations were undertaken in northeastern Oregon to better assess earthquake hazards in the region and determine relative favorability for geothermal energy development on lands of the Confederated Tribes of the Umatilla Indian Reservation (CTUIR). This work was funded in part by a Bureau of Indian Affairs grant awarded to the CTUIR to identify areas most suitable for further exploration of geothermal resources. Results from this work were utilized as inputs to a geothermal favorability modeling process that led to the identification of target sites for further geothermal investigation. Beyond the geothermal aspect of this project, the region is of great tectonic significance as it marks the intersection of two major physiographic and geophysical features, the Klamath-Blue Mountain lineament (KBL) and Olympic-Wallowa lineament (OWL), inferred to represent major basement boundaries. The Thorn Hollow and Hite faults, which appear to be major linkages between the KBL and OWL, run through the study area.\nNew aeromagnetic, gravity and magnetotelluric (MT) surveying, along with fault analyses, were conducted as part of this effort. Detailed geophysical exploration resulted in the collection of 1,380 new gravity stations, 34,524-line kilometers of aeromagnetic data (covering 12,524 km2) and measurements from 36 MT stations. Two-dimensional (2D) forward-modeling was executed along several profiles using gravity and aeromagnetic data, combined with existing geologic mapping and rock property constraints measured from hand samples and outcrops. These models are used to define lithologic contacts in the subsurface in the absence of well logs or borehole data to determine possible geothermal fluid reservoirs, as well as identify major structures that may act as conduits for the upward migration of geothermal fluids.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Geothermal's role in today's energy market","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Geothermal Resources Council","usgsCitation":"Ritzinger, B., Glen, J.M., Peacock, J., Blakely, R.J., Mills, P., Staisch, L.M., Bennett, S.E., and Sherrod, B.L., 2018, Geothermal potential of the Umatilla Indian Reservation, Oregon: Evidence from detailed geophysical investigations, in Geothermal's role in today's energy market, v. 42, p. 925-942.","productDescription":"18 p.","startPage":"925","endPage":"942","ipdsId":"IP-098936","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science 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0000-0002-5379-1390 britzinger@usgs.gov","orcid":"https://orcid.org/0000-0002-5379-1390","contributorId":216213,"corporation":false,"usgs":true,"family":"Ritzinger","given":"Brent","email":"britzinger@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":915635,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glen, Jonathan M.G. 0000-0002-3502-3355 jglen@usgs.gov","orcid":"https://orcid.org/0000-0002-3502-3355","contributorId":176530,"corporation":false,"usgs":true,"family":"Glen","given":"Jonathan","email":"jglen@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":915636,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":915637,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blakely, Richard J. 0000-0003-1701-5236 blakely@usgs.gov","orcid":"https://orcid.org/0000-0003-1701-5236","contributorId":1540,"corporation":false,"usgs":true,"family":"Blakely","given":"Richard","email":"blakely@usgs.gov","middleInitial":"J.","affiliations":[{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":915638,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mills, Patrick","contributorId":345096,"corporation":false,"usgs":false,"family":"Mills","given":"Patrick","email":"","affiliations":[{"id":13345,"text":"Confederated Tribes of the Umatilla Indian Reservation","active":true,"usgs":false}],"preferred":false,"id":915639,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Staisch, Lydia M. 0000-0002-1414-5994 lstaisch@usgs.gov","orcid":"https://orcid.org/0000-0002-1414-5994","contributorId":167068,"corporation":false,"usgs":true,"family":"Staisch","given":"Lydia","email":"lstaisch@usgs.gov","middleInitial":"M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":915640,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bennett, Scott E.K. 0000-0002-9772-4122 sekbennett@usgs.gov","orcid":"https://orcid.org/0000-0002-9772-4122","contributorId":5340,"corporation":false,"usgs":true,"family":"Bennett","given":"Scott","email":"sekbennett@usgs.gov","middleInitial":"E.K.","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},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":915641,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sherrod, Brian L. 0000-0002-4492-8631 bsherrod@usgs.gov","orcid":"https://orcid.org/0000-0002-4492-8631","contributorId":2834,"corporation":false,"usgs":true,"family":"Sherrod","given":"Brian","email":"bsherrod@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":915642,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70199070,"text":"sir20185116 - 2018 - Estimating metal concentrations with regression analysis and water-quality surrogates at nine sites on the Animas and San Juan Rivers, Colorado, New Mexico, and Utah","interactions":[],"lastModifiedDate":"2018-12-03T14:33:08","indexId":"sir20185116","displayToPublicDate":"2018-11-30T17:15:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5116","title":"Estimating metal concentrations with regression analysis and water-quality surrogates at nine sites on the Animas and San Juan Rivers, Colorado, New Mexico, and Utah","docAbstract":"The purpose of this report is to evaluate the use of site-specific regression models to estimate metal concentrations at nine U.S. Geological Survey streamflow-gaging stations on the Animas and San Juan Rivers in Colorado, New Mexico, and Utah. Downstream users could use these regression models to determine if metal concentrations are elevated and pose a risk to water supplies, agriculture, and recreation. Multiple linear-regression models were developed by relating metal concentrations in discrete water-quality samples to continuously monitored streamflow and surrogate parameters (specific conductance, pH, turbidity, and water temperature) collected at the U.S. Geological Survey stations. Models were developed for dissolved and total concentrations of aluminum, arsenic, cadmium, copper, iron, lead, manganese, and zinc using water-quality samples collected from 2005 to 2017 by several Federal, State, Tribal, and local agencies using different collection methods and analytical laboratories. Model performance varied but, in general, models for dissolved metals did not perform as well as those for total metals. Dissolved metals generally were correlated to specific conductance or streamflow and total metals generally were better correlated with turbidity.
Explanatory variables in the models reflected hydrologic and geochemical processes within the basin. A larger number of regression models were statistically significant for the most upstream sites, where metal concentrations were elevated by drainage from abandoned mines and mineralized bedrock. Models generally did not perform as well at downstream sites, especially for dissolved metals, which occurred at lower concentrations than at the upstream sites. In the lower reaches of the rivers, the input of more alkaline water from tributaries and groundwater reduced metal solubility and diluted metal concentrations. The number and distribution of samples in the calibration datasets also may have been a factor in model development. At some sites on the San Juan River, calibration datasets were more limited and did not represent the full range of observed hydrologic and water-quality conditions, especially during storm events in summer and fall. Recommendations for model use are given based on estimates of model precision, biases, and adequacy of the calibration datasets in terms of the number of samples and representativeness of the observed range of streamflow and water-quality conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185116","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Mast, M.A., 2018, Estimating metal concentrations with regression analysis and water-quality surrogates at nine sites on the Animas and San Juan Rivers, Colorado, New Mexico, and Utah: U.S. Geological Survey Scientific Investigations Report 2018–5116, 68 p., https://doi.org/10.3133/sir20185116.","productDescription":"Report: vii, 68 p.; Data release","onlineOnly":"Y","ipdsId":"IP-095270","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":359772,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5116/ofr20185116.pdf","text":"Report","size":"77.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5116"},{"id":359771,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5116/coverthb.jpg"},{"id":359773,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9THSFE0","text":"USGS data release","linkHelpText":"Calibration datasets and model archive summaries for regression models developed to estimate metal concentrations at nine sites on the Animas and San Juan Rivers, Colorado, New Mexico, and Utah"}],"country":"United States","state":"Colorado, New Mexico, Utah","otherGeospatial":"Animas River, San Juan River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110,\n 36.5\n ],\n [\n -107.5,\n 36.5\n ],\n [\n -107.5,\n 38\n ],\n [\n -110,\n 38\n ],\n [\n -110,\n 36.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046
The explosive growth of geospatial data has stimulated the development of many standards aimed at decreasing data heterogeneity and enhancing data use. Well-established design methods for geospatial data standards typically involve the creation of two schemas for data structure, designated here as logical and physical, but this can lead to conceptual inconsistencies and modelling inefficiencies. In this paper we describe a design method that overcomes these issues by incorporating an additional schema – the conceptual schema – and demonstrate its application to the design of GroundWaterML2 (GWML2), a new international standard for groundwater data. Results include not only a new data standard, robustly constructed and tested, but also an enhanced method for geospatial data standard design.
","language":"English","publisher":"Springer","doi":"10.1186/s40965-018-0058-3","usgsCitation":"Brodaric, B., Boisvert, E., Dahlhaus, P., Grellet, S., Kmoch, A., Letourneau, F., Lucido, J., Simons, B., and Wagner, B., 2018, The conceptual schema in geospatial data standard design with application to GroundWaterML2: Open Geospatial Data, Software and Standards, v. 3, p. 1-15, https://doi.org/10.1186/s40965-018-0058-3.","productDescription":"15 p.","startPage":"1","endPage":"15","ipdsId":"IP-104677","costCenters":[{"id":39013,"text":"WMA - Project Management Office","active":true,"usgs":true}],"links":[{"id":468224,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40965-018-0058-3","text":"Publisher Index Page"},{"id":362256,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Brodaric, Boyan","contributorId":214353,"corporation":false,"usgs":false,"family":"Brodaric","given":"Boyan","email":"","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":759704,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boisvert, Eric","contributorId":167613,"corporation":false,"usgs":false,"family":"Boisvert","given":"Eric","email":"","affiliations":[{"id":24780,"text":"Geological Survey of Canada, Quebec, QC, Canada","active":true,"usgs":false}],"preferred":false,"id":759705,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dahlhaus, Peter","contributorId":214354,"corporation":false,"usgs":false,"family":"Dahlhaus","given":"Peter","email":"","affiliations":[{"id":39014,"text":"Federation University Australia","active":true,"usgs":false}],"preferred":false,"id":759706,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grellet, Sylvain","contributorId":214355,"corporation":false,"usgs":false,"family":"Grellet","given":"Sylvain","email":"","affiliations":[{"id":39015,"text":"Bureau de Recherche Géologiques et Minières (BRGM)","active":true,"usgs":false}],"preferred":false,"id":759707,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kmoch, Alexander","contributorId":214356,"corporation":false,"usgs":false,"family":"Kmoch","given":"Alexander","email":"","affiliations":[{"id":39016,"text":"University of Salzburg (Z_GIS)","active":true,"usgs":false}],"preferred":false,"id":759708,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Letourneau, Francois","contributorId":214357,"corporation":false,"usgs":false,"family":"Letourneau","given":"Francois","email":"","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":759709,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lucido, Jessica 0000-0003-2249-4796 jlucido@usgs.gov","orcid":"https://orcid.org/0000-0003-2249-4796","contributorId":214352,"corporation":false,"usgs":true,"family":"Lucido","given":"Jessica","email":"jlucido@usgs.gov","affiliations":[{"id":39013,"text":"WMA - Project Management Office","active":true,"usgs":true}],"preferred":true,"id":759703,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Simons, Bruce","contributorId":214358,"corporation":false,"usgs":false,"family":"Simons","given":"Bruce","email":"","affiliations":[{"id":39017,"text":"CSIRO Land and Water","active":true,"usgs":false}],"preferred":false,"id":759710,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wagner, Bernhard","contributorId":214359,"corporation":false,"usgs":false,"family":"Wagner","given":"Bernhard","email":"","affiliations":[{"id":39018,"text":"Geological Survey of Bavaria","active":true,"usgs":false}],"preferred":false,"id":759711,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70200505,"text":"ofr20181170 - 2018 - Assessing the impact of open-ocean and back-barrier shoreline change on Dauphin Island, Alabama, at multiple time scales over the last 75 years","interactions":[],"lastModifiedDate":"2025-05-13T16:19:30.858259","indexId":"ofr20181170","displayToPublicDate":"2018-11-30T15:30:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1170","displayTitle":"Assessing the Impact of Open-Ocean and Back-Barrier Shoreline Change on Dauphin Island, Alabama, at Multiple Time Scales Over the Last 75 Years","title":"Assessing the impact of open-ocean and back-barrier shoreline change on Dauphin Island, Alabama, at multiple time scales over the last 75 years","docAbstract":"Dauphin Island and Little Dauphin Island, collectively, make up a geomorphically complex barrier island system located along Alabama’s southern coast, separating Mississippi Sound from the Gulf of Mexico and Mobile Bay. The barrier island system provides numerous economical (tourism, fisheries) and natural (habitat for migratory birds, natural protection of inland and coastal areas from storms) benefits to the State of Alabama. The complex geomorphology of Dauphin Island is partly a response to temporal variations in the direction and magnitude of sediment transport along and across the barrier island system. In this report, we present open-ocean and back-barrier shoreline change rates at different time scales to evaluate the island’s dominant behavior (expansion or widening and contraction or narrowing) over the last 75 years. The spatial and temporal variability of barrier island width provides baseline and historical context for potential restoration alternatives being considered as part of the Alabama Barrier Island Restoration Feasibility Study. Open-ocean shorelines have eroded continuously over the last 75 years, with rates ranging from 1.5 to 4 meters per year. Back-barrier shorelines are less uniform than open-ocean shorelines, but are, on average, also eroding over the same period. Periods of back-barrier progradation are observed but generally occur during discrete, large altering events like hurricanes that overwash or breach narrow sections of the barrier island. Because both shorelines are eroding, the width of the island has decreased during the last 75 years. The section to the west of a breach that opened during Hurricanes Ivan and Katrina (known as Katrina Cut) exhibits a steady, rapid decrease in width while the section to the east of the breach has gone through periods of expansion and contraction and has only recently begun slowly narrowing. Although the recent trends indicate declining widths, the back-barrier progradation rates in this area were the highest compared to other time periods, which abated extreme narrowing caused by increased open-ocean shoreline erosion. These data and the interpreted results indicate that both short-term (annual) and long-term (decadal) cross-barrier sediment exchange is a key component of sustaining barrier island width. Therefore, any mechanisms that influence this exchange, whether from natural processes (overwash, breaching, or inlet dynamics) or human activities (development, post-storm recovery, restoration), should be considered when evaluating the long-term sustainability of barrier island systems.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181170","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Mobile District","usgsCitation":"Smith, C.G., Long, J.W., Henderson, R.E., and Nelson, P.R., 2018, Assessing the impact of open-ocean and back-barrier shoreline change on Dauphin Island, Alabama, at multiple time scales over the last 75 years: U.S. Geological Survey Open-File Report 2018–1170, 20 p., https://doi.org/10.3133/ofr20181170.","productDescription":"vii, 20 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-092796","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":359767,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1170/ofr20181170.pdf","text":"Report","size":"949 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1170"},{"id":359766,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1170/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.3626937866211,\n 30.1667\n ],\n [\n -88.03447723388672,\n 30.1667\n ],\n [\n -88.03447723388672,\n 30.3333\n ],\n [\n -88.3626937866211,\n 30.3333\n ],\n [\n -88.3626937866211,\n 30.1667\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
Landscape carbon (C) flux estimates are necessary for assessing the ability of terrestrial ecosystems to buffer further increases in anthropogenic carbon dioxide (CO2) emissions. Advances in remote sensing have allowed for coarse-scale estimates of gross primary productivity (GPP) (e.g., MODIS 17), yet efforts to assess spatial patterns in respiration lag behind those of GPP. Here, we demonstrate a method to predict growing season soil respiration at a regional scale in a forested ecosystem. We related field measurements (n=144) of growing season soil respiration across subalpine forests in the Southern Rocky Mountains ecoregion to a suite of biophysical predictors with a Random Forest model (30 m pixel size). We found that Landsat Enhanced Vegetation Index (EVI), growing season AI, temperature, precipitation, elevation, and slope aspect explained spatiotemporal variability in soil respiration. Our model had a psuedo-r2 of 0.45 and root mean squared error (RMSE) of roughly one-quarter of the mean value of respiration. Predicted growing season soil respiration across the region was remarkably consistent across 2004, 2005 and 2006 (150-d averages of 542.8, 544.3, and 536.5 g C m-2, respectively). Yet, we observed substantial variability in spatial patterns of soil respiration predictions that varied between years, suggesting that our method is sensitive to changes in respiration drivers. We compared our estimates to MODIS GPP and nocturnal net ecosystem exchange (NEE) derived from eddy covariance towers as a proxy for ecosystem respiration. Averaged across the predictive region, mean predicted growing season soil respiration was 73% of MODIS GPP, while predicted soil respiration was generally within 20% of nocturnal NEE from eddy covariance towers. This study demonstrated that geospatial and remotely-sensed datasets can be used in a statistical modeling framework to estimate soil respiration at landscape scales.
","language":"English","publisher":"AGU","doi":"10.1029/2018JG004613","usgsCitation":"Berryman, E.M., Vanderhoof, M.K., Bradford, J.B., Hawbaker, T., Henne, P., Burns, S.P., Frank, J.M., Birdsey, R.A., and Ryan, M.G., 2018, Estimating soil respiration in a subalpine landscape using point, terrain, climate and greenness data: Journal of Geophysical Research G: Biogeosciences, v. 123, no. 10, p. 3231-3249, https://doi.org/10.1029/2018JG004613.","productDescription":"19 p.","startPage":"3231","endPage":"3249","ipdsId":"IP-097679","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":437667,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99TRHPB","text":"USGS data release","linkHelpText":"Data release for estimating soil respiration in a subalpine landscape using point, terrain, climate and greenness data"},{"id":360845,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108,\n 39\n ],\n [\n -104,\n 39\n ],\n [\n -104,\n 41.5\n ],\n [\n -108,\n 41.5\n ],\n [\n -108,\n 39\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"123","issue":"10","noUsgsAuthors":false,"publicationDate":"2018-10-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Berryman, Erin Michele 0000-0001-8699-2474 eberryman@usgs.gov","orcid":"https://orcid.org/0000-0001-8699-2474","contributorId":5765,"corporation":false,"usgs":true,"family":"Berryman","given":"Erin","email":"eberryman@usgs.gov","middleInitial":"Michele","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":755441,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":755442,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradford, John B. 0000-0001-9257-6303 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":755443,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":755444,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Henne, Paul D. 0000-0003-1211-5545 phenne@usgs.gov","orcid":"https://orcid.org/0000-0003-1211-5545","contributorId":169166,"corporation":false,"usgs":true,"family":"Henne","given":"Paul D.","email":"phenne@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":755445,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Burns, Sean P.","contributorId":98921,"corporation":false,"usgs":true,"family":"Burns","given":"Sean","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":755446,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Frank, John M.","contributorId":11969,"corporation":false,"usgs":true,"family":"Frank","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":755447,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Birdsey, Richard A.","contributorId":17751,"corporation":false,"usgs":true,"family":"Birdsey","given":"Richard","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":755448,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ryan, Michael G.","contributorId":202371,"corporation":false,"usgs":false,"family":"Ryan","given":"Michael","email":"","middleInitial":"G.","affiliations":[{"id":33176,"text":"Rocky Mountain Research Station, USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":755449,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70202549,"text":"70202549 - 2018 - A video surveillance system to monitor breeding colonies of common terns (Sterna Hirundo)","interactions":[],"lastModifiedDate":"2019-03-08T15:03:15","indexId":"70202549","displayToPublicDate":"2018-11-30T14:52:35","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2498,"text":"Journal of Visualized Experiments","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A video surveillance system to monitor breeding colonies of common terns (Sterna Hirundo)","title":"A video surveillance system to monitor breeding colonies of common terns (Sterna Hirundo)","docAbstract":"Many waterbird populations have faced declines over the last century, including the common tern (Sterna hirundo), a waterbird species with a widespread breeding distribution, that has been recently listed as endangered in some habitats of its range. Waterbird monitoring programs exist to track populations through time; however, some of the more intensive approaches require entering colonies and can be disruptive to nesting populations. This paper describes a protocol that utilizes a minimally invasive surveillance system to continuously monitor common tern nesting behavior in typical ground-nesting colonies. The video monitoring system utilizes wireless cameras focused on individual nests as well as over the colony as a whole, and allows for observation without entering the colony. The video system is powered with several 12 V car batteries that are continuously recharged using solar panels. Footage is recorded using a digital video recorder (DVR) connected to a hard drive, which can be replaced when full. The DVR may be placed outside of the colony to reduce disturbance. In this study, 3,624 h of footage recorded over 63 days in weather conditions ranging from 12.8 °C to 35.0 °C produced 3,006 h (83%) of usable behavioral data. The types of data retrieved from the recorded video can vary; we used it to detect external disturbances and measure nesting behavior during incubation. Although the protocol detailed here was designed for ground-nesting waterbirds, the principal system could easily be modified to accommodate alternative scenarios, such as colonial arboreal nesting species, making it widely applicable to a variety of research needs.
","language":"English","doi":"10.3791/57928","usgsCitation":"Wall, J., Marban, P., Brinker, D., Sullivan, J., Zimnik, M., Murrow, J., McGowan, P.C., Callahan, C.R., and Prosser, D.J., 2018, A video surveillance system to monitor breeding colonies of common terns (Sterna Hirundo): Journal of Visualized Experiments, v. 137, e57928, https://doi.org/10.3791/57928.","productDescription":"e57928","ipdsId":"IP-093212","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":468227,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.3791/57928","text":"External Repository"},{"id":361902,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"137","noUsgsAuthors":false,"publicationDate":"2018-07-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Wall, J.L.","contributorId":214070,"corporation":false,"usgs":false,"family":"Wall","given":"J.L.","email":"","affiliations":[],"preferred":false,"id":759063,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marban, Paul 0000-0002-4910-6565 pmarban@usgs.gov","orcid":"https://orcid.org/0000-0002-4910-6565","contributorId":196581,"corporation":false,"usgs":true,"family":"Marban","given":"Paul","email":"pmarban@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":759064,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brinker, D.F.","contributorId":10523,"corporation":false,"usgs":true,"family":"Brinker","given":"D.F.","email":"","affiliations":[],"preferred":false,"id":759065,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sullivan, J.D.","contributorId":214071,"corporation":false,"usgs":false,"family":"Sullivan","given":"J.D.","email":"","affiliations":[],"preferred":false,"id":759066,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zimnik, M.","contributorId":214072,"corporation":false,"usgs":false,"family":"Zimnik","given":"M.","affiliations":[],"preferred":false,"id":759067,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Murrow, J.L.","contributorId":101490,"corporation":false,"usgs":true,"family":"Murrow","given":"J.L.","affiliations":[],"preferred":false,"id":759068,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McGowan, P. C.","contributorId":67191,"corporation":false,"usgs":false,"family":"McGowan","given":"P.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":759069,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Callahan, Carl R.","contributorId":205289,"corporation":false,"usgs":false,"family":"Callahan","given":"Carl","email":"","middleInitial":"R.","affiliations":[{"id":37073,"text":"USFWS, Annapolis MD","active":true,"usgs":false}],"preferred":false,"id":759070,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Prosser, Diann J. 0000-0002-5251-1799 dprosser@usgs.gov","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":2389,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","email":"dprosser@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":759071,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70200652,"text":"sir20185144 - 2018 - Land subsidence along the California Aqueduct in west-central San Joaquin Valley, California, 2003–10","interactions":[],"lastModifiedDate":"2018-11-30T13:15:16","indexId":"sir20185144","displayToPublicDate":"2018-11-29T14:00:39","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":"2018-5144","displayTitle":"Land Subsidence Along the California Aqueduct in West-Central San Joaquin Valley, California, 2003–10","title":"Land subsidence along the California Aqueduct in west-central San Joaquin Valley, California, 2003–10","docAbstract":"Extensive groundwater withdrawal from the unconsolidated deposits in the San Joaquin Valley caused widespread aquifer-system compaction and resultant land subsidence from 1926 to 1970—locally exceeding 8.5 meters. The importation of surface water beginning in the early 1950s through the Delta-Mendota Canal and in the early 1970s through the California Aqueduct resulted in decreased groundwater pumping, recovery of water levels, and a reduced rate of compaction in some areas of the San Joaquin Valley. However, drought conditions during 1976–77, 1987–92, and drought conditions and operational reductions in surface-water deliveries during 2007–10 decreased surface-water availability, causing pumping to increase, water levels to decline, and renewed compaction. Land subsidence from this compaction has reduced freeboard and flow capacity of the California Aqueduct, Delta-Mendota Canal, and other canals that deliver irrigation water and transport floodwater.
The U.S. Geological Survey, in cooperation with the California Department of Water Resources, assessed more recent land subsidence near a 145-kilometer reach of the California Aqueduct in the west-central part of the San Joaquin Valley as part of an effort to minimize future subsidence-related damages to the California Aqueduct. The location, magnitude, and stress regime of land-surface deformation during 2003–10 were determined by using data and analyses associated with extensometers, Global Positioning System surveys, Interferometric Synthetic Aperture Radar, spirit-leveling surveys, and groundwater wells. Comparison of continuous Global Positioning System, shallow-extensometer, and groundwater-level data indicated that most of the compaction in this area took place beneath the Corcoran Clay, the primary regional confining unit. The integration of measurements strengthens confidence in individual measurement methods and provides the information at spatial and temporal scales that water managers need to design and implement groundwater sustainability plans in compliance with California’s Sustainable Groundwater Management Act.
Measurements of land-surface deformation during 2003–10 indicated that the parts of the California Aqueduct closest to the Coast Ranges in the west-central part of the San Joaquin Valley were fairly stable or minimally subsiding on an annual basis; some areas show seasonal periods of subsidence and uplift that resulted in little or no longer-term elevation loss. Many groundwater levels in these areas did not reach historical lows during 2003–10, indicating that deformation nearest the Coast Ranges was likely primarily elastic.
Land-surface deformation measurements indicated that some parts of the California Aqueduct that traverse farther from the Coast Ranges toward the valley center subsided. Some parts of the California Aqueduct subsided locally, but generally the California Aqueduct is within part of a 12,000-square-kilometer area affected by 25 millimeters or more of subsidence during 2008–10, with maxima in Madera County, south of the town of El Nido near the San Joaquin River and the Eastside Bypass (540 millimeters), and in Tulare County, west of the town of Pixley (345 millimeters). Interferometric Synthetic Aperture Radar-derived subsidence maps for various periods during 2003–10 show that the area of maximum active subsidence (that is, the largest rates of subsidence) shifted from its historical (1926–70) location southwest of the town of Mendota to these areas nearer the valley center. Calculations indicated that the subsidence rate doubled in 2008 in parts of the study area. Water levels declined during 2007–10 in many shallow and deep wells in the most rapidly subsiding areas, where water levels in many deep wells reached their historical lows, indicating that subsidence measured during this period was largely inelastic.
Continued groundwater-level and land-subsidence monitoring in the San Joaquin Valley is important because (1) operational- and drought-related reductions in surface-water deliveries since 1976 have resulted in increased groundwater pumping and associated water-level declines and land subsidence, (2) land use and associated pumping continue to change throughout the valley, and (3) subsidence management is stipulated in the Sustainable Groundwater Management Act. The availability of surface water remains uncertain; even during record-setting precipitation years, such as 2010–11, water deliveries fell short of requests and groundwater pumping was required to meet the irrigation demand. In some areas, the infrastructure is not available to supply surface water, and groundwater is the only source of water. Because of the expected continued demand for water and the limitations and uncertainty of surface-water supplies, groundwater pumping and associated land subsidence remains a concern. Spatially detailed information on land subsidence is needed to minimize future subsidence-related damages to the California Aqueduct and other infrastructure in the San Joaquin Valley, as well as alterations to natural resources such as stream gradients, water depths, and water temperatures. The integration of data on land-surface elevation, subsurface deformation, and water levels—particularly continuous measurements—enables the analysis of aquifer-system response to groundwater pumping, which in turn, enables estimation of the preconsolidation head and calculation of aquifer-system storage properties. This information can be used to improve numerical model simulations of groundwater flow and aquifer-system compaction and allow for consideration of land subsidence in the evaluation of water resource management alternatives and compliance with the Sustainable Groundwater Management Act.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185144","collaboration":"Prepared in cooperation with the California Department of Water Resources","usgsCitation":"Sneed, M., Brandt, J.T., and Solt, M., 2018, Land subsidence along the California Aqueduct in west-central San Joaquin Valley, California, 2003–10: U.S. Geological Survey Scientific Investigations Report 2018–5144, 67 p., https://doi.org/10.3133/sir20185144. ","productDescription":"x, 67 p.","onlineOnly":"Y","ipdsId":"IP-044802","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":437670,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NC9LLL","text":"USGS data release","linkHelpText":"Interferometric Synthetic Aperture Radar-Derived Subsidence Contours for the West-Central San Joaquin Valley, California, 2008-10"},{"id":359739,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5144/sir20185144.pdf","text":"Report","size":"16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Scientfic Investigations Report 2018-5144"},{"id":359738,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5144/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Joaquin Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.5,\n 35.75\n ],\n [\n -119.5,\n 35.75\n ],\n [\n -119.5,\n 37.5\n ],\n [\n -121.5,\n 37.5\n ],\n [\n -121.5,\n 35.75]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The U.S. Geological Survey monitors groundwater-storage change and land-surface elevation change caused by groundwater withdrawal in Tucson Basin and Avra Valley—the two most populated alluvial basins within the Tucson Active Management Area. The Tucson Active Management Area is one of five active management areas in Arizona established by the 1980 Groundwater Management Act and governed by the Arizona Department of Water Resources. Gravity and land-surface elevation change were monitored every 1 to 3 years at wells and benchmarks in Tucson Basin and Avra Valley from 2003 to 2016. Monitoring resulted in estimates of land-surface elevation change and groundwater-storage change. Interferometric synthetic aperture radar (InSAR) interferograms showing land-surface elevation change were constructed for the Tucson metropolitan area from (1) May 2003 to July 2006, (2) July 2006 to June 2008, (3) June 2008 to April 2011, (4) April 2011 to November 2014, and (5) November 2014 to March 2016. For the Tucson metropolitan area, maximum subsidence of about 2 inches occurred during May 2003 to July 2006. From July 2006 to June 2008, maximum subsidence of approximately 0.8 inches occurred in two regions in the Tucson metropolitan area. From June 2008 to April 2011, about 0.8 inches of subsidence also occurred in two regions. Additionally, for the period April 2011 to November 2014, a maximum of about 0.9 inches of subsidence occurred in the same two regions of Tucson Basin. For the entire monitoring period from May 2003 to March 2016, maximum subsidence of as much as 5.3 inches occurred in the Tucson metropolitan area south of Irvington Road between south 12th Avenue and south Park Avenue, and as much as 4 inches in central Tucson south of Broadway between Country Club Road and Craycroft Road. The InSAR data indicated that there was no significant land-surface deformation from 2003 to 2016 in Avra Valley, and no change in either basin from 2014 to 2016.
The volume of stored groundwater in the monitored part of Tucson Basin showed net zero change from spring 2003 to summer 2006. From summer 2006 to summer 2008 the volume of stored groundwater in the monitored part of Tucson Basin increased approximately 50,000 acre-feet; however, overdraft conditions resumed from summer 2008 to spring 2011, resulting in decreased storage of approximately 178,000 acre-feet. From spring 2011 to fall 2014, the volume of stored groundwater in Tucson Basin decreased about 200,000 acre-feet, following a period of lower than average rainfall in 2012 and 2013. The volume of stored groundwater in the monitored part of Tucson Basin increased approximately 167,000 acre-feet from fall 2014 to spring 2016.
Groundwater storage in Avra Valley increased during the entire monitoring period from spring 2003 to spring 2016, largely as a result of managed recharge of Central Arizona Project water in the monitored region. From 2003 to 2016, artificial recharge in Avra Valley totaled approximately 1,788,000 acre-feet, and in Tucson Basin artificial recharge for the entire period was about 636,790 acre-feet. Artificial recharge exceeded pumping in Avra Valley for each time interval. Pumping in Tucson Basin exceeded artificial recharge for every period except 2014 to 2016. Overall, long-term water-level declines have stabilized or reversed since 2000 at most areas in Tucson Basin and Avra Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185154","collaboration":"Prepared in cooperation with the Arizona Department of Water Resources, Pima County, Tucson Water, the Town of Oro Valley, the Town of Marana, and the Metropolitan Domestic Water Improvement District","usgsCitation":"Carruth, R.L., Kahler, L.M., and Conway, B.D., 2018, Groundwater-storage change and land-surface elevation change in Tucson Basin and Avra Valley, south-central Arizona—2003–2016: U.S. Geological Survey Scientific Investigations Report 2018–5154, 34 p., https://doi.org/10.3133/sir20185154.","productDescription":"vii, 34 p.","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-019853","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":359796,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5154/coverthb.jpg"},{"id":359797,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5154/sir20185154.pdf","text":"Report","size":"26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5154"}],"country":"United States","state":"Arizona","otherGeospatial":"Avra Valley, Tucson Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.5936279296875,\n 31.33311153820117\n ],\n [\n -110.44281005859375,\n 31.33311153820117\n ],\n [\n -110.44281005859375,\n 32.90726224488304\n ],\n [\n -111.5936279296875,\n 32.90726224488304\n ],\n [\n -111.5936279296875,\n 31.33311153820117\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Explore U.S. Geological Survey (USGS) water-use websites to learn how and where the Nation's water use has changed over time! Learn how to find and access USGS water-use data shown in maps, graphs, visualizations, and information products. Gain a better understanding of water-use terms and USGS educational resources. Learn how to find and use USGS visualizations to see how water use has changed in each State, and explore county water withdrawals during 2015 to see which areas withdrew the most or least water.
National Water-Use Science Project Team
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The northern diamondback terrapin (Malaclemys terrapin terrapin) is a saltmarsh-dependent turtle that occupies coastal habitats throughout much of the Atlantic coast of North America. We used a novel application of acoustic telemetry to quantify both mobility and occupancy of terrapins within a dredged harbor and surrounding habitats, and used these metrics to quantify relative risk to individuals posed by harbor dredging. Terrapins showed strong fidelity to brumating areas within subdrainages, but extensive movements between these zones during the active period. Activity was greatest in late spring and early summer, declining to near zero by December. Occupancy of the dredge zone was also greatest during spring and summer and declined throughout the autumn months to an annual minimum during winter. Taken together, these data indicate that risks from harbor dredging are minimized during the autumn and early winter months.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-018-0481-9","usgsCitation":"Castro-Santos, T.R., Bolus, M., and Danylchuk, A., 2018, Assessing risks from harbor dredging to the northernmost population of diamondback terrapins using acoustic telemetry: Estuaries and Coasts, v. 42, no. 2, p. 378-389, https://doi.org/10.1007/s12237-018-0481-9.","productDescription":"12 p.","startPage":"378","endPage":"389","ipdsId":"IP-082608","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":379959,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","city":"Wellfleet","otherGeospatial":"Cape Cod","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.08316040039062,\n 41.90432124806034\n ],\n [\n -69.97055053710938,\n 41.90432124806034\n ],\n [\n -69.97055053710938,\n 41.98705662960288\n ],\n [\n -70.08316040039062,\n 41.98705662960288\n ],\n [\n -70.08316040039062,\n 41.90432124806034\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"2","noUsgsAuthors":false,"publicationDate":"2018-11-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Castro-Santos, Theodore R. 0000-0003-2575-9120 tcastrosantos@usgs.gov","orcid":"https://orcid.org/0000-0003-2575-9120","contributorId":3321,"corporation":false,"usgs":true,"family":"Castro-Santos","given":"Theodore","email":"tcastrosantos@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":803375,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bolus, M.","contributorId":244215,"corporation":false,"usgs":false,"family":"Bolus","given":"M.","email":"","affiliations":[],"preferred":false,"id":803528,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Danylchuk, A. J.","contributorId":146536,"corporation":false,"usgs":false,"family":"Danylchuk","given":"A. J.","affiliations":[{"id":16720,"text":"Department of Environmental Conservation, University of Massachusetts, Amherst, MA 01003-9485, USA","active":true,"usgs":false}],"preferred":false,"id":803529,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70200438,"text":"ofr20181167 - 2018 - Biophysical assessment for indemnity selection of Federal Lands in Colorado","interactions":[],"lastModifiedDate":"2018-11-29T15:28:51","indexId":"ofr20181167","displayToPublicDate":"2018-11-28T17:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1167","title":"Biophysical assessment for indemnity selection of Federal Lands in Colorado","docAbstract":"Information on the biophysical features of Federal lands identified as suitable for transfer to the State of Colorado was requested by the Bureau of Land Management (BLM). This information is intended for use in conducting an Environmental Assessment prior to the transfer of ownership (conveyance) to the State. The Colorado State Land Board filed a selective application to obtain public land and mineral estate in lieu of lands to which the State of Colorado was entitled but did not receive at the time of statehood. To address this legal obligation, 339 parcels of Federal lands (organized into 89 indemnity units [IUs]), currently under management by the BLM, have been identified as suitable for transfer to the State. The IUs include 23,130 acres of surface and mineral estate and 6,150 acres of mineral estate only. The specific land parcels to be transferred to the State will be finalized after an Environmental Assessment and other evaluations are completed.
To provide the biophysical information necessary for conducting a future Environmental Assessment of the potential effects of the proposed land transfer, information on ecological communities, soil characteristics, and land use was summarized at three levels: (1) all of Colorado, (2) lands under the jurisdiction of the BLM, and (3) the 89 IUs. Information was also synthesized and summarized for 179 plant and animal species or subspecies of management concern to evaluate which species had the potential for occurrence on IUs. Datasets summarized for Colorado and for indemnity units and methodological details for all data summaries are provided in U.S. Geological Survey data releases available online at https://doi.org/10.5066/F7GT5MGV and https://doi.org/10.5066/F7C24VQ0.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181167","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Carr, N.B., Burris, L.E., and Manier, D.J., 2018, Biophysical assessment for indemnity selection of Federal lands in Colorado: U.S. Geological Survey Open-File Report 2018–1167, 51 p., https://doi.org/10.3133/ofr20181167.","productDescription":"Report: vii, 51 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-094998","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":359757,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7GT5MGV","text":"USGS data release","linkHelpText":"Broad-scale assessment of biophysical features in 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\"}}]}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
Tucked in a grove of thorny mesquite trees, on an ancient coral reef on the south side of the Hawaiian island of Oahu, west of Pearl Harbor, a small unmanned observatory quietly records the Earth’s time-varying magnetic field. The Honolulu Magnetic Observatory is 1 of 14 that the U.S. Geological Survey Geomagnetism Program operates at various locations across the United States and its territories.
Data from these observatories, Honolulu, and those operated by institutions in foreign countries, record a variety of magnetic signals related to a wide diversity of physical phenomena in the Earth’s interior and its surrounding outer-space environment. USGS magnetic observatory operations are an integral part of a U.S. National Space Weather Strategy for monitoring and assessing natural hazards that potentially threaten important technological systems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183029","usgsCitation":"Love, J.J., and Finn, C.A., 2018, Honolulu Magnetic Observatory: U.S. Geological Survey Fact Sheet 2018–3029, 2 p.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-095628","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":359735,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3029/coverthb.jpg"},{"id":359736,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3029/fs20183029.pdf","text":"Report","size":"2.96 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2018-3029"}],"country":"United States","state":"Hawaii","city":"Honolulu","otherGeospatial":"Oahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -157.8907012939453,\n 21.245062376508383\n ],\n [\n -157.76229858398438,\n 21.245062376508383\n ],\n [\n -157.76229858398438,\n 21.33830997478836\n ],\n [\n -157.8907012939453,\n 21.33830997478836\n ],\n [\n -157.8907012939453,\n 21.245062376508383\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225-0046
The “National Field Manual for the Collection of Water-Quality Data” (NFM) provides guidelines and procedures for U.S. Geological Survey (USGS) personnel who collect data used to assess the quality of the Nation’s surface-water and groundwater resources. This chapter, NFM A1, provides an overview of preparations for water sampling, which includes site reconnaissance, project work plans, quality-assurance plans, basic equipment and supplies needed for fieldwork, safety precautions, and planning for data management. It updates and supersedes USGS Techniques of Water-Resources Investigations, book 9, chapter A1, version 2.0, by F.D. Wilde.
Before 2017, the NFM chapters were released in the USGS Techniques of Water-Resources Investigations series. Effective in 2018, new and revised NFM chapters are being released in the USGS Techniques and Methods series; this series change does not affect the content and format of the NFM. More information is in the general introduction to the NFM (USGS Techniques and Methods, book 9, chapter A0) at https://doi.org/10.3133/tm9A0. The authoritative current versions of NFM chapters are available in the USGS Publications Warehouse at https://pubs.er.usgs.gov/. Comments, questions, and suggestions related to the NFM can be addressed to nfm-owq@usgs.gov.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: National field manual for the collection of water-quality data in Book 9: Handbooks for water-resources investigations","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm9A1","usgsCitation":"U.S. Geological Survey, 2018, Preparations for water sampling: U.S. Geological Survey Techniques and Methods 9-A1, vii, 42 p., https://doi.org/10.3133/tm9A1.","productDescription":"vii, 42 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[],"links":[{"id":359547,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/09/a1/tm9a1.pdf","text":"Report","size":"2.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 9-A1"},{"id":359548,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/tm/09/a1/versionHist.txt","text":"Version History","size":"2.74 MB","linkFileType":{"id":2,"text":"txt"}},{"id":359546,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/09/a1/coverthb2.jpg"}],"publicComments":"The 2018 release in the Techniques and Methods series supersedes two earlier editions in the Techniques of Water-Resources Investigations series. Version 1 was released in 1998 and version 2 was released in 2005. More details are in the version history document.","contact":"Chief, Office of Quality Assurance
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 432
Reston, VA 20192
In 2015–16, the U.S. Geological Survey, in cooperation with the Muskingum Watershed Conservancy District, led a study to assess baseline (2015–16) surface-water quality in six lake drainage basins within the Muskingum River watershed that are in the early years of shale-gas development. In 2015, 9 of the 10 most active counties in Ohio for oil and gas development were wholly or partially within the Muskingum River watershed. In addition to shale gas development, the area has a history of conventional oil and gas development and coal mining.
In all, 30 surface-water sites were sampled: 20 in tributaries flowing to the lakes, 4 in lakes themselves, and 6 downstream of the lakes. At each of the 30 sites, 6 samples were collected to characterize surface-water chemistry throughout a range of hydrologic conditions. The sampling generally occurred during low flows (periods of greater groundwater contribution) rather than during runoff events (periods of high stream stage).
Trilinear diagrams of major ion chemistry revealed three main types of water in the study area―sulfate-dominated waters, bicarbonate-dominated waters, and waters with mixed bicarbonate and chloride anions. Most sites produced samples of bicarbonate-dominated water, and 11 sites produced samples with sulfate-type waters. Mixed bicarbonate and chloride waters were found in samples from two of the six lake drainage basins studied.
The baseline (2015–16) assessment of surface-water quality in the study area indicated that few water-chemistry constituents and properties occurred at concentrations or levels that would adversely affect aquatic organisms. Chemical-specific, aquatic life use criteria were not met in only three instances: two were for total dissolved solids at sites likely impacted by coal mining in their drainage basins (hereafter referred to as “mine-impacted sites”), and one was for dissolved oxygen.
Mine drainage from historical coal mining in the region likely affected the quality of about one-third of the streams sampled. To simplify interpretation of water-chemistry results, 11 sites with sulfate-type water were identified as mine-impacted sites based on water-quality criteria established by Ohio Department of Natural Resources, Division of Mineral Resources Management, and separated out for subsequent statistical analysis. Concentrations or levels of bicarbonate, boron, calcium, carbonate, total dissolved solids, fluoride, magnesium, lithium, pH, potassium, sodium, specific conductance, strontium, sulfate, and suspended sediment in water were higher (significance level of 0.05) at mine-impacted stream sites than at non-mine-impacted stream sites.
An accidental release of oil- and gas-related brines could increase salinity (sodium and chloride), the concentration of total dissolved solids in shallow groundwater and streams, and specific conductance. For this study, chloride concentrations in the study area ranged from 2.12 to 76.1 milligrams per liter. Sources of chloride in water samples were evaluated using binary mixing curves and ratios of chloride to bromide. These ratios indicated that 13 samples from 3 sites in the drainage basin that contained the highest density of conventional oil and gas wells in the study, as well as 4 samples collected from other drainage basins, likely contained a component of brine. Concentrations or levels of barium, bromide, chloride, iron, lithium, manganese, and sodium were significantly higher (alpha = 0.05) in samples with a component of brine than in samples without a component of brine.
Benzene, toluene, ethylbenzene and xylene (BTEX), compounds that occur naturally in crude oil, made up 24 of the 45 detections (53 percent) of volatile organic compounds in the study area. The BTEX detections were not associated with sites containing a component of brine. The only volatile organic compound detected in any of the 17 samples that contained a component of brine was acetone, detected in 3 (18 percent) of these samples and in 11 percent of samples not containing a component of brine. Considering that BTEX are gasoline hydrocarbons and that most of the detections occurred during warmer months in and around the lakes, the BTEX detections likely are associated with increases in outdoor activities such as automobile and boating traffic.
Radium-226 and radium-228 were included in the list of analytes for this study because production water from shale-gas drilling can contain these naturally occurring radioactive materials. Concentrations of radium-226 exceeded background levels in only two surface-water samples. Concentrations of radium-228 exceeded background levels in one surface-water sample.
A brine signature potentially indicative of oil and gas contamination was detected in samples collected at two sites that contained active or plugged waste injection wells, or both. Results from the study indicated significant differences in the median concentrations of bromide, chloride, lithium, manganese, sodium, and total dissolved nitrogen between sites with and without injection wells in their drainage areas. Median concentrations of bromide, chloride, lithium, and sodium, which are common oil- and gas-related contaminants, were higher at sites with injection wells in their drainage areas compared to sites without injection wells.
Historical (1960s, 1970s, and 1980s) chloride concentrations and streamflow data at or near five of the six sampling sites downstream from each lake dam were compared to current (2015–16) values. An analysis of covariance was done to test the effects of streamflow, time (decade), and the combined effects (cross product) of streamflow and time on chloride concentrations. Those analyses indicated that streamflow was not significant in explaining the variation in chloride concentration, likely because streamflow in those locations is controlled by dam operations; therefore, association between runoff-generating events and streamflow is less direct than in unregulated streams. From the 1980s to the study period (2015–16), data for three of the five lakes indicated an increase in chloride concentrations. The comparison of historical and current (2015–16) study data from samples collected at another lake indicated that chloride concentrations increased from the 1960s to the 1970s, but concentrations in the 1970s and 2015–16 were similar even though 13 samples from this lake drainage basin were classified as having a component of brine. Median chloride concentrations for the fifth lake, however, seemed to decrease from the 1980s to 2015–16.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185113","collaboration":"Prepared in cooperation with the Muskingum Watershed Conservancy District","usgsCitation":"Covert, S.A., Jagucki, M.L., and Huitger, C., 2018, Baseline water quality of an area undergoing shale-gas development in the Muskingum River watershed, Ohio, 2015–16: U.S. Geological Survey Scientific Investigations Report 2018–5113, 129 p., https://doi.org/10.3133/sir20185113.","productDescription":"Report: ix, 129 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-091174","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":359613,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7GF0SRT","text":"USGS data release","description":"USGS data release","linkHelpText":"Data from quality-control equipment blanks, field blanks, and field replicates for baseline water quality of an area undergoing shale-gas development in the Muskingum River watershed, Ohio, 2015-16 "},{"id":359612,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5113/sir20185113.pdf","text":"Report","size":"14.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5113"},{"id":359611,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5113/coverthb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Muskingum River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.75,\n 39.75\n ],\n [\n -80.75,\n 39.75\n ],\n [\n -80.75,\n 40.6667\n ],\n [\n -81.75,\n 40.6667\n ],\n [\n -81.75,\n 39.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd
Suite 100
Columbus, OH 43229
Located southwest of Klamath Falls, Oregon, Klamath Straits Drain is a 10.1-mile-long canal that conveys water uphill and northward through the use of pumps before discharging to the Klamath River. Klamath Straits Drain traverses an area that historically encompassed Lower Klamath Lake. Currently, the Drain receives water from farmland and from parts of the Lower Klamath Lake National Wildlife Refuge. To support water-quality improvement in Klamath Straits Drain, a hydrodynamic and water-temperature model was constructed and calibrated for calendar years 2012–15 with the two-dimensional model CE-QUAL-W2 (version 4.0). Water quality was calibrated for a subset of that time, from April 1, 2012 to March 31, 2015. Flows in calendar year 2012 were within the normal range, while calendar years 2013–15 were dry years. Significant findings from this study include:
This newly constructed model of the Klamath Straits Drain simulates flow, water levels, water temperature, and water quality with acceptable accuracy but with certain data limitations. This model should prove useful in evaluating potential strategies for flow and water-quality management and restoration.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185134","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Sullivan, A.B., and Rounds, S.A., 2018, Modeling hydrodynamics, water temperature, and water quality in Klamath Straits Drain, Oregon and California, 2012–15: U.S. Geological Survey Scientific Investigations Report 2018-5134, 30 p., https://doi.org/10.3133/sir20185134.","productDescription":"vii, 30 p.","onlineOnly":"Y","ipdsId":"IP-099157","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":359688,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5134/coverthb.jpg"},{"id":359690,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://or.water.usgs.gov/proj/keno_reach/models.html","text":"Klamath Straits Models —","description":"SIR 2018-5134 Klamath Straits Model","linkHelpText":"Water-Quality Monitoring and Modeling of the Keno Reach of the Klamath River"},{"id":359689,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5134/sir20185134.pdf","text":"Report","size":"8.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5134"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Klamath Straits Drain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122,\n 41.8333\n ],\n [\n -121.5,\n 41.8333\n ],\n [\n -121.5,\n 42.33\n ],\n [\n -122,\n 42.33\n ],\n [\n -122,\n 41.8333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Terrestrial laser scanners (TLSs) provide a tool to assess and monitor forest structure across forest landscapes. We present TLS methods, suggestions, and mapped guidelines for planning TLS acquisitions at varying scales and forest densities. We examined rates of point‐density decline with distance from two TLS that acquire data at relatively high and low point density and found that the rates were nearly identical between scanners (pvalue <0.01), suggesting that our findings are applicable to a range of TLS types. Using unique, TLS‐adapted processing methods, we determined the relative accuracy of TLS‐derived plot‐scale estimates of tree height, diameter‐at‐breast‐height, height‐to‐canopy, tree counts, as well as treatment‐scale tree density and patch metrics, using both high point density and low point density TLS among thinned and nonthinned forest treatments. The high‐density TLS consistently provides more accurate estimates of plot‐level metrics (R2 = 0.46 to 0.87) than the low‐density TLS (R2 = −0.14 to 0.53). At treatment scales, tree density estimates are similar among scanners (R2 = 0.95 vs. 0.71), as are canopy cover and patch metrics. We develop and present the normalized density‐distance index (NDDI), which can account for up to 59% of the variance in estimate error and can be used to guide TLS‐data acquisition plans. This index indicates whether a given location has generally higher point density (higher NDDI) relative to the distance from the scanner and can be used as a proxy for uncertainty. Using NDDI as a guide for fair comparison between scanners, both plot‐ and treatment‐scale estimates improved.
","language":"English","publisher":"AGU Publications","doi":"10.1029/2018EA000417","usgsCitation":"Donager, J.J., Sankey, T.T., Sankey, J.B., Sanchez Meadorc, A.J., Springer, A., and Bailey, J.D., 2018, Examining forest structure with terrestrial lidar: Suggestions and novel techniques based on comparisons between scanners and forest treatments: Earth and Space Science, v. 5, no. 11, p. 753-776, https://doi.org/10.1029/2018EA000417.","productDescription":"14 p.","startPage":"753","endPage":"776","ipdsId":"IP-081018","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":468233,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018ea000417","text":"Publisher Index Page"},{"id":437673,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F63VYX","text":"USGS data release","linkHelpText":"Northern Arizona Ponderosa Pine Forest Treatment Terrestrial Lidar Data"},{"id":359656,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-12","publicationStatus":"PW","scienceBaseUri":"5bfd146be4b0815414ca38e6","contributors":{"authors":[{"text":"Donager, Jonathon J.","contributorId":210787,"corporation":false,"usgs":false,"family":"Donager","given":"Jonathon","email":"","middleInitial":"J.","affiliations":[{"id":38148,"text":"Northern Arizona University, School of Earth Science and Environmental Sustainability, 1295 S. Knoles Drive, PO Box 5695, Flagstaff, AZ 86011","active":true,"usgs":false}],"preferred":false,"id":751966,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Temuulen T.","contributorId":173297,"corporation":false,"usgs":false,"family":"Sankey","given":"Temuulen","email":"","middleInitial":"T.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":751967,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":751965,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sanchez Meadorc, Andrew J.","contributorId":210788,"corporation":false,"usgs":false,"family":"Sanchez Meadorc","given":"Andrew","email":"","middleInitial":"J.","affiliations":[{"id":38149,"text":"Northern Arizona University, School of Forestry, 200 East Pine Knoll Drive, PO Box 15018, Flagstaff, AZ 86011","active":true,"usgs":false}],"preferred":false,"id":751968,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Springer, Abraham E.","contributorId":9558,"corporation":false,"usgs":true,"family":"Springer","given":"Abraham E.","affiliations":[],"preferred":false,"id":751969,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bailey, John D.","contributorId":42928,"corporation":false,"usgs":true,"family":"Bailey","given":"John","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":751970,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70199277,"text":"sir20185122 - 2018 - Flood-inundation maps for the North Fork Kentucky River at Hazard, Kentucky","interactions":[],"lastModifiedDate":"2018-11-26T15:06:08","indexId":"sir20185122","displayToPublicDate":"2018-11-26T11:30:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5122","displayTitle":"Flood-Inundation Maps for the North Fork Kentucky River at Hazard, Kentucky","title":"Flood-inundation maps for the North Fork Kentucky River at Hazard, Kentucky","docAbstract":"Digital flood-inundation maps for a 7.1-mile reach of the North Fork Kentucky River at Hazard, Kentucky (Ky.), were created by the U.S. Geological Survey (USGS) in cooperation with the Kentucky Silver Jackets and the U.S. Army Corps of Engineers Louisville District. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the North Fork Kentucky River at Hazard, Ky. (USGS station number 03277500). Near-real-time stages at this streamgage may be obtained on the internet from the USGS National Water Information System at https://waterdata.usgs.gov/ or the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) at https://water.weather.gov/ahps/, which also forecasts flood hydrographs at this site (NWS AHPS site HAZK2). NWS AHPS forecast peak stage information may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation.
Flood profiles were computed for the North Fork Kentucky River reach by means of a one-dimensional, step-backwater model developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated by using the current stage-discharge relation (USGS rating no. 24.0) at USGS streamgage 03277500, North Fork Kentucky River at Hazard, Ky. The calibrated hydraulic model was then used to compute 26 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from approximately bankfull (14 ft) to the highest even-foot increment stage (39 ft) of the current stage-discharge rating curve. The simulated water-surface profiles were then combined with a geographic information system digital elevation model, derived from light detection and ranging data, to delineate the area flooded at each water level.
The availability of these maps, along with information on the internet regarding current stage from the USGS streamgage at North Fork Kentucky River at Hazard, Ky., and forecasted stream stages from the NWS AHPS, provides emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185122","collaboration":"Prepared in cooperation with the Kentucky Silver Jackets and the U.S. Army Corps of Engineers Louisville District","usgsCitation":"Boldt, J.A., Lant, J.G., and Kolarik, N.E., 2018, Flood-inundation maps for the North Fork Kentucky River at Hazard, Kentucky: U.S. Geological Survey Scientific Investigations Report 2018-5122, 12 p., https://doi.org/10.3133/sir20185122.","productDescription":"Report: vi, 12 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-098752","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":359619,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CNAG9G","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial datasets and model for the flood-inundation study of the North Fork Kentucky River at Hazard, Kentucky"},{"id":359617,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5122/coverthb.jpg"},{"id":359618,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5122//sir20185122.pdf","text":"Report","size":"5.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5122"}],"country":"United States","state":"Kentucky","city":"Hazard","otherGeospatial":" North Fork Kentucky River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.20315361022949,\n 37.22158045838649\n ],\n [\n -83.15423011779785,\n 37.22158045838649\n ],\n [\n -83.15423011779785,\n 37.274872400526334\n ],\n [\n -83.20315361022949,\n 37.274872400526334\n ],\n [\n -83.20315361022949,\n 37.22158045838649\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville, KY 40299-1906
In January 2016, the Secretary of the U.S. Department of the Interior tasked the U.S. Geological Survey (USGS) with producing a publicly available and annually updated database of estimated greenhouse gas emissions associated with the extraction and use (predominantly some form of combustion) of fossil fuels from Federal lands. In response, the USGS has produced estimates of the greenhouse gas emissions resulting from the extraction and end-use combustion of fossil fuels produced on Federal lands in the United States, as well as estimates of ecosystem carbon emissions and sequestration on those lands. American Indian and Tribal lands were not included in this analysis. The emissions estimates span a 10-year period (2005–14) and are reported for 28 States and two offshore areas. Nationwide emissions from fossil fuels produced on Federal lands in 2014 were 1,279.0 million metric tons of carbon dioxide equivalent (MMT CO2 Eq.) for carbon dioxide (CO2), 47.6 MMT CO2 Eq. for methane (CH4), and 5.5 MMT CO2 Eq. for nitrous oxide (N2O). Compared to 2005, the 2014 totals represent decreases in emissions for all three greenhouse gases (decreases of 6.1 percent for CO2, 10.5 percent for CH4, and 20.3 percent for N2O). Emissions from fossil fuels produced on Federal lands represent, on average, 23.7 percent of national emissions for CO2, 7.3 percent for CH4, and 1.5 percent for N2O over the 10 years included in this estimate.
In 2005, Federal lands of the conterminous United States stored 82,289 MMT CO2 Eq. in terrestrial ecosystems. By 2014, carbon storage, or sequestration, was estimated at 83,600 MMT CO2 Eq., representing an increase of 1.6 percent, or 1,311 MMT CO2 Eq. Soils stored most of the ecosystem carbon (63 percent), followed by live vegetation (26 percent) and dead organic matter (11 percent). The rate of net carbon uptake in ecosystems ranged from a sink (sequestration) of 475 million metric tons of carbon dioxide per year (MMT CO2 Eq./yr) to a source (emission) of 51 MMT CO2 Eq./yr because of annual variability in climate and weather, rates of land-use and land-cover change, and wildfire frequency, among other factors. At the national level, the USGS estimates that terrestrial ecosystems (forests, grasslands, and shrublands) on Federal lands sequestered an average of 195 MMT CO2 Eq./yr between 2005 and 2014, offsetting approximately 15 percent of the CO2 emissions resulting from the extraction of fossil fuels on Federal lands and their end-use combustion.
The USGS estimates presented in this report represent a first-of-its-kind accounting for the emissions resulting from fossil fuel extraction on Federal lands and the end-use combustion of those fuels, as well as for the sequestration of carbon in terrestrial ecosystems on Federal lands. The net CO2 emissions estimate, which is the difference between the emitted and sequestered CO2, provides an informative combined result describing the emissions (fossil fuel extraction and end-use combustion) associated with a State’s Federal lands and sequestration on those same lands. The estimates included in this report can provide context for future energy decisions, as well as a basis to track change in the future.
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U.S. Geological Survey
956 National Center
12201 Sunrise Valley Drive
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