Climate change, tectonics, and humans create long- and short-term temporal variations in the supply of suspended sediment to rivers. These signals, generated in upland erosional areas, are filtered by alluvial storage before reaching the basin outlet. We quantified this filter using a random walk model driven by sediment budget data, a power-law distributed probability density function (PDF) to determine how long sediment remains stored, and a constant downstream drift velocity during transport of 157 km/yr. For 25 km of transport, few particles are stored, and the median travel time is 0.2 yr. For 1000 km of transport, nearly all particles are stored, and the median travel time is 2.5 m.y. Both travel-time distributions are power laws. The 1000 km travel-time distribution was then used to filter sinusoidal input signals with periods of 10 yr and 104 yr. The 10 yr signal is delayed by 12.5 times its input period, damped by a factor of 380, and is output as a power law. The 104 yr signal is delayed by 0.15 times its input period, damped by a factor of 3, and the output signal retains its sinusoidal input form (but with a power-law “tail”). Delivery time scales for these two signals are controlled by storage; in-channel transport time is insignificant, and low-frequency signals are transmitted with greater fidelity than high-frequency signals. These signal modifications are essential to consider when evaluating watershed restoration schemes designed to control sediment loading, and where source-area geomorphic processes are inferred from the geologic record.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/G38170.1","usgsCitation":"Pizzuto, J.E., Keeler, J., Skalak, K., and Karwan, D., 2017, Storage filters upland suspended sediment signals delivered from watersheds: Geology, v. 45, no. 2, p. 151-154, https://doi.org/10.1130/G38170.1.","productDescription":"4 p.","startPage":"151","endPage":"154","ipdsId":"IP-081208","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":343977,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"45","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-01","publicationStatus":"PW","scienceBaseUri":"596f1e1ee4b0d1f9f0640734","contributors":{"authors":[{"text":"Pizzuto, James E.","contributorId":49424,"corporation":false,"usgs":false,"family":"Pizzuto","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":13220,"text":"The Charles E. Via, Jr. Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":705310,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Keeler, Jeremy","contributorId":194778,"corporation":false,"usgs":false,"family":"Keeler","given":"Jeremy","email":"","affiliations":[],"preferred":false,"id":705311,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Skalak, Katherine 0000-0003-4122-1240 kskalak@usgs.gov","orcid":"https://orcid.org/0000-0003-4122-1240","contributorId":3990,"corporation":false,"usgs":true,"family":"Skalak","given":"Katherine","email":"kskalak@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":705309,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Karwan, Diana","contributorId":194779,"corporation":false,"usgs":false,"family":"Karwan","given":"Diana","affiliations":[],"preferred":false,"id":705312,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70189569,"text":"70189569 - 2017 - Limiting the effects of earthquakes on gravitational-wave interferometers","interactions":[],"lastModifiedDate":"2017-07-18T08:29:36","indexId":"70189569","displayToPublicDate":"2017-07-18T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5464,"text":"Classical and Quantum Gravity","active":true,"publicationSubtype":{"id":10}},"title":"Limiting the effects of earthquakes on gravitational-wave interferometers","docAbstract":"Ground-based gravitational wave interferometers such as the Laser Interferometer Gravitational-wave Observatory (LIGO) are susceptible to ground shaking from high-magnitude teleseismic events, which can interrupt their operation in science mode and significantly reduce their duty cycle. It can take several hours for a detector to stabilize enough to return to its nominal state for scientific observations. The down time can be reduced if advance warning of impending shaking is received and the impact is suppressed in the isolation system with the goal of maintaining stable operation even at the expense of increased instrumental noise. Here, we describe an early warning system for modern gravitational-wave observatories. The system relies on near real-time earthquake alerts provided by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). Preliminary low latency hypocenter and magnitude information is generally available in 5 to 20 min of a significant earthquake depending on its magnitude and location. The alerts are used to estimate arrival times and ground velocities at the gravitational-wave detectors. In general, 90% of the predictions for ground-motion amplitude are within a factor of 5 of measured values. The error in both arrival time and ground-motion prediction introduced by using preliminary, rather than final, hypocenter and magnitude information is minimal. By using a machine learning algorithm, we develop a prediction model that calculates the probability that a given earthquake will prevent a detector from taking data. Our initial results indicate that by using detector control configuration changes, we could prevent interruption of operation from 40 to 100 earthquake events in a 6-month time-period.
","language":"English","publisher":"Institute of Physics","doi":"10.1088/1361-6382/aa5a60","usgsCitation":"Coughlin, M., Earle, P.S., Harms, J., Biscans, S., Buchanan, C., Coughlin, E., Donovan, F., Fee, J., Gabbard, H., Guy, M.M., Mukund, N., and Perry, M., 2017, Limiting the effects of earthquakes on gravitational-wave interferometers: Classical and Quantum Gravity, v. 34, no. 4, Article 044004: 14 p., https://doi.org/10.1088/1361-6382/aa5a60.","productDescription":"Article 044004: 14 p.","ipdsId":"IP-083270","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":469676,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://arxiv.org/abs/1611.09812","text":"External Repository"},{"id":343966,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-02","publicationStatus":"PW","scienceBaseUri":"596f1e20e4b0d1f9f0640744","contributors":{"authors":[{"text":"Coughlin, Michael","contributorId":194752,"corporation":false,"usgs":false,"family":"Coughlin","given":"Michael","email":"","affiliations":[],"preferred":false,"id":705250,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Earle, Paul S. 0000-0002-3500-017X pearle@usgs.gov","orcid":"https://orcid.org/0000-0002-3500-017X","contributorId":173551,"corporation":false,"usgs":true,"family":"Earle","given":"Paul","email":"pearle@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705251,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harms, Jan","contributorId":194753,"corporation":false,"usgs":false,"family":"Harms","given":"Jan","email":"","affiliations":[],"preferred":false,"id":705252,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Biscans, Sebastien","contributorId":194754,"corporation":false,"usgs":false,"family":"Biscans","given":"Sebastien","email":"","affiliations":[],"preferred":false,"id":705253,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buchanan, Christopher","contributorId":194755,"corporation":false,"usgs":false,"family":"Buchanan","given":"Christopher","email":"","affiliations":[],"preferred":false,"id":705254,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Coughlin, Eric","contributorId":194756,"corporation":false,"usgs":false,"family":"Coughlin","given":"Eric","email":"","affiliations":[],"preferred":false,"id":705255,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Donovan, Fred","contributorId":194757,"corporation":false,"usgs":false,"family":"Donovan","given":"Fred","email":"","affiliations":[],"preferred":false,"id":705256,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fee, Jeremy 0000-0002-6851-2796 jmfee@usgs.gov","orcid":"https://orcid.org/0000-0002-6851-2796","contributorId":194758,"corporation":false,"usgs":true,"family":"Fee","given":"Jeremy","email":"jmfee@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705257,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gabbard, Hunter","contributorId":194759,"corporation":false,"usgs":false,"family":"Gabbard","given":"Hunter","email":"","affiliations":[],"preferred":false,"id":705258,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Guy, Michelle M. 0000-0003-3450-4656 mguy@usgs.gov","orcid":"https://orcid.org/0000-0003-3450-4656","contributorId":173432,"corporation":false,"usgs":true,"family":"Guy","given":"Michelle","email":"mguy@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705259,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mukund, Nikhil","contributorId":194760,"corporation":false,"usgs":false,"family":"Mukund","given":"Nikhil","email":"","affiliations":[],"preferred":false,"id":705260,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Perry, Matthew","contributorId":194761,"corporation":false,"usgs":false,"family":"Perry","given":"Matthew","affiliations":[],"preferred":false,"id":705261,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70189586,"text":"fs20173054 - 2017 - Brackish groundwater and its potential to augment freshwater supplies","interactions":[],"lastModifiedDate":"2017-07-19T08:49:00","indexId":"fs20173054","displayToPublicDate":"2017-07-18T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3054","title":"Brackish groundwater and its potential to augment freshwater supplies","docAbstract":"Secure, reliable, and sustainable water resources are fundamental to the Nation’s food production, energy independence, and ecological and human health and well-being. Indications are that at any given time, water resources are under stress in selected parts of the country. The large-scale development of groundwater resources has caused declines in the amount of groundwater in storage and declines in discharges to surface water bodies (Reilly and others, 2008). Water supply in some regions, particularly in arid and semiarid regions, is not adequate to meet demand, and severe drought intensifies the stresses affecting water resources (National Drought Mitigation Center, the U.S. Department of Agriculture, and the National Oceanic and Atmospheric Association, 2015). If these drought conditions continue, water shortages could adversely affect the human condition and threaten environmental flows necessary to maintain ecosystem health.
In support of the national census of water resources, the U.S. Geological Survey (USGS) completed the national brackish groundwater assessment to provide updated information about brackish groundwater as a potential resource to augment or replace freshwater supplies (Stanton and others, 2017). Study objectives were to consolidate available data into a comprehensive database of brackish groundwater resources in the United States and to produce a summary report highlighting the distribution, physical and chemical characteristics, and use of brackish groundwater resources. This assessment was authorized by section 9507 of the Omnibus Public Land Management Act of 2009 (42 U.S.C. 10367), passed by Congress in March 2009. Before this assessment, the last national brackish groundwater compilation was completed in the mid-1960s (Feth, 1965). Since that time, substantially more hydrologic and geochemical data have been collected and now can be used to improve the understanding of the Nation’s brackish groundwater resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173054","usgsCitation":"Stanton, J.S., and Dennehy, K.F., 2017, Brackish groundwater and its potential to augment freshwater supplies: U.S. Geological Survey Fact Sheet 2017–3054, 4 p., https://doi.org/10.3133/fs20173054.","productDescription":"Document: 4 p.; Companion File; Data Release","onlineOnly":"N","ipdsId":"IP-078564","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":343973,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3054/coverthb.jpg"},{"id":343974,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3054/fs20173054.pdf","text":"Fact Sheet","size":"2.38 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017–3054"},{"id":343975,"rank":3,"type":{"id":7,"text":"Companion 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States\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Rock core and sediment from U.S. Geological Survey test corehole G–2984 completed in 2011 in Broward County, Florida, provide an opportunity to improve the understanding of the lithostratigraphic, sequence stratigraphic, and hydrogeologic framework of the intermediate confining unit and Floridan aquifer system in southeastern Florida. A multidisciplinary approach including characterization of sequence stratigraphy, lithofacies, ichnology, foraminiferal paleontology, depositional environments, porosity, and permeability was used to describe the geologic samples from this test corehole. This information has produced a detailed characterization of the lithofacies and sequence stratigraphy of the upper part of the middle Eocene Avon Park Formation and Oligocene to middle Miocene Arcadia Formation. This enhancement of the knowledge of the sequence stratigraphic framework is especially important, because subaerial karst unconformities at the upper boundary of depositional cycles at various hierarchical scales are commonly associated with secondary porosity and enhanced permeability in the Floridan aquifer system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171074","collaboration":"Prepared in cooperation with Broward County, Florida","usgsCitation":"Cunningham, K.J., and Robinson, Edward, 2017, Lithofacies and sequence stratigraphic description of the upper part of the Avon Park Formation and the Arcadia Formation in U.S. Geological Survey G–2984 test corehole, Broward County, Florida: U.S. Geological Survey Open File-Report 2017–1074, 139 p., https://doi.org/10.3133/ofr20171074.","productDescription":"v, 139 p.","onlineOnly":"Y","ipdsId":"IP-083814","costCenters":[{"id":269,"text":"FLWSC-Ft. Lauderdale","active":true,"usgs":true}],"links":[{"id":343757,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1074/coverthb2.jpg"},{"id":343758,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1074/ofr20171074.pdf","text":"Report","size":"23.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1074"}],"country":"United States","state":"Florida","county":"Broward County","otherGeospatial":"Test corehole G-2984","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.15,\n 26.35\n ],\n [\n -80.1,\n 26.35\n ],\n [\n -80.1,\n 26.3\n ],\n [\n -80.15,\n 26.3\n ],\n [\n -80.15,\n 26.35\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Globally, rivers and streams are highly altered by impoundments, diversions, and stream channelization associated with agricultural and water delivery needs. Climate change imposes additional challenges by further reducing discharge, introducing variability in seasonal precipitation patterns, and increasing temperatures. Collectively, these changes in a river or stream’s annual hydrology affects surface and groundwater dynamics, fluvial processes, and the linked aquatic and riparian responses, particularly in arid regions. Recognizing the inherent ecosystem services that riparian and aquatic habitats provide, society increasingly supports restoring the functionality of riparian and aquatic ecosystems.
Given the wide range in types and scales of riparian impacts, approaches to riparian restoration can range from tactical, short-term, and site-specific efforts to strategic projects and long-term collaborations best pursued at the watershed scale. In the spirit of sharing information, the U.S. Geological Survey’s Grand Canyon Monitoring and Research Center convened a workshop June 23-25, 2015, in Flagstaff, Ariz. for practitioners in restoration science to share general principles, successful restoration practices, and discuss the challenges that face those practicing riparian restoration in the southwestern United States. Presenters from the Colorado River and the Rio Grande basins, offered their perspectives and experiences in restoration at the local, reach and watershed scale. Outcomes of the workshop include this Proceedings volume, which is composed of extended abstracts of most of the presentations given at the workshop, and recommendations or information needs identified by participants. The organization of the Proceedings follows a general progression from local scale restoration to river and watershed scale approaches, and finishes with restoration assessments and monitoring.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171091","usgsCitation":"Ralston, B.E., and Sarr, D.A., 2017, Case studies of riparian and watershed restoration in the southwestern United States—Principles, challenges, and successes: U.S. Geological Survey Open-File Report 2017-1091, 116 p., https://doi.org/10.3133/ofr20171091.","productDescription":"ix, 116 p.","onlineOnly":"Y","ipdsId":"IP-087333","costCenters":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"links":[{"id":343991,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1091/ofr20171091.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1091"},{"id":343990,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1091/coverthb.jpg"}],"country":"United States","contact":"Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center routinely produces and distributes a remote sensing phenology (RSP) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) 1-km data compiled from a series of National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-11, −14, −16, −17, −18, and −19). Each NOAA satellite experienced orbital drift during its duty period, which influenced the AVHRR reflectance measurements. To understand the effect of the orbital drift on the AVHRR-derived RSP dataset, we analyzed the impact of solar zenith angle (SZA) on the RSP metrics in the conterminous United States (CONUS). The AVHRR weekly composites were used to calculate the growing-season median SZA at the pixel level for each year from 1989 to 2014. The results showed that the SZA increased towards the end of each NOAA satellite mission with the highest increasing rate occurring during NOAA-11 (1989–1994) and NOAA-14 (1995–2000) missions. The growing-season median SZA values (44°–60°) in 1992, 1993, 1994, 1999, and 2000 were substantially higher than those in other years (28°–40°). The high SZA in those years caused negative trends in the SZA time series, that were statistically significant (at α = 0.05 level) in 76.9% of the CONUS area. A pixel-based temporal correlation analysis showed that the phenological metrics and SZA were significantly correlated (at α = 0.05 level) in 4.1–20.4% of the CONUS area. After excluding the 5 years with high SZA (>40°) from the analysis, the temporal SZA trend was largely reduced, significantly affecting less than 2% of the study area. Additionally, significant correlation between the phenological metrics and SZA was observed in less than 7% of the study area. Our study concluded that the NOAA satellite orbital drift increased SZA, and in turn, influenced the phenological metrics. Elimination of the years with high median SZA reduced the influence of orbital drift on the RSP time series.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jag.2017.06.013","usgsCitation":"Ji, L., and Brown, J.F., 2017, Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics: International Journal of Applied Earth Observation and Geoinformation, v. 62, p. 215-223, https://doi.org/10.1016/j.jag.2017.06.013.","productDescription":"9 p.","startPage":"215","endPage":"223","ipdsId":"IP-081678","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":469675,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jag.2017.06.013","text":"Publisher Index Page"},{"id":343965,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"596f1e1fe4b0d1f9f064073c","contributors":{"authors":[{"text":"Ji, Lei 0000-0002-6133-1036 lji@usgs.gov","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":139587,"corporation":false,"usgs":true,"family":"Ji","given":"Lei","email":"lji@usgs.gov","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":705288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","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":705289,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70189571,"text":"70189571 - 2017 - Oil Shale","interactions":[],"lastModifiedDate":"2017-07-18T09:05:31","indexId":"70189571","displayToPublicDate":"2017-07-18T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Oil Shale","docAbstract":"Oil shales are fine-grained sedimentary rocks formed in many different depositional environments (terrestrial, lacustrine, marine) containing large quantities of thermally immature organic matter in the forms of kerogen and bitumen. If defined from an economic standpoint, a rock containing a sufficient concentration of oil-prone kerogen to generate economic quantities of synthetic crude oil upon heating to high temperatures (350–600 °C) in the absence of oxygen (pyrolysis) can be considered an oil shale.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Encyclopedia of Geochemistry","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer International Publishing","doi":"10.1007/978-3-319-39193-9_181-1","isbn":"978-3-319-39193-9","usgsCitation":"Birdwell, J.E., 2017, Oil Shale, chap. of Encyclopedia of Geochemistry, 3 p., https://doi.org/10.1007/978-3-319-39193-9_181-1.","productDescription":"3 p.","ipdsId":"IP-085028","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":343971,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-14","publicationStatus":"PW","scienceBaseUri":"596f1e1fe4b0d1f9f0640740","contributors":{"authors":[{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":705264,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70187723,"text":"fs20173039 - 2017 - Assessment of continuous oil and gas resources in the Perth Basin Province, Australia, 2017","interactions":[],"lastModifiedDate":"2019-12-23T09:37:30","indexId":"fs20173039","displayToPublicDate":"2017-07-17T15:25:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3039","title":"Assessment of continuous oil and gas resources in the Perth Basin Province, Australia, 2017","docAbstract":"Using a geology-based assessment methodology, the U.S. Geological Survey assessed undiscovered, technically recoverable mean resources of 223 million barrels of oil and 14.5 trillion cubic feet of gas in the Perth Basin Province, Australia.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173039","usgsCitation":"Schenk, C.J., Tennyson, M.E., Finn, T.M., Mercier, T.J., Hawkins, S.J., Gaswirth, S.B., Marra, K.R., Klett, T.R., Le, P.A., Leathers-Miller, H.M., and Woodall, C.A., 2017, Assessment of continuous oil and gas resources in the Perth Basin Province, Australia, 2017: U.S. Geological Survey Fact Sheet 2017–3039, 2 p., https://doi.org/10.3133/fs20173039.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-084736","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":343834,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3039/fs20173039.pdf ","text":"Report","size":"344 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3039"},{"id":343833,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3039/coverthb.jpg"}],"country":"Australia","otherGeospatial":"Perth Basin Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 114.08203125,\n -35.42486791930557\n ],\n [\n 119.2236328125,\n -35.42486791930557\n ],\n [\n 119.2236328125,\n -27.955591004642528\n ],\n [\n 114.08203125,\n -27.955591004642528\n ],\n [\n 114.08203125,\n -35.42486791930557\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
The Energy Independence and Security Act of 2007 authorized the U.S. Geological Survey (USGS) to conduct a national assessment of geologic storage resources for carbon dioxide (CO2) and requested the USGS to estimate the “potential volumes of oil and gas recoverable by injection and sequestration of industrial carbon dioxide in potential sequestration formations” (42 U.S.C. 17271(b)(4)). Geologic CO2 sequestration associated with enhanced oil recovery (EOR) using CO2 in existing hydrocarbon reservoirs has the potential to increase the U.S. hydrocarbon recoverable resource. The objective of this report is to provide detailed information on three approaches that can be used to calculate the incremental recovery factors for CO2-EOR. Therefore, the contents of this report could form an integral part of an assessment methodology that can be used to assess the sedimentary basins of the United States for the hydrocarbon recovery potential using CO2-EOR methods in conventional oil reservoirs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175062","usgsCitation":"Verma, M.K., ed., 2017, Three approaches for estimating recovery factors in carbon dioxide enhanced oil recovery: U.S. Geological Survey Scientific Investigations Report 2017–5062–A–E, variously paged, https://doi.org/10.3133/sir20175062.","productDescription":"88 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-068432","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":342892,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5062/sir20175062.pdf","text":"Report","size":"1.77 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5062"},{"id":342891,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5062/coverthb.jpg"}],"contact":" Eastern Energy Resources Science Center
U.S. Geological Survey
Mail Stop 956 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
The need to increase the efficiency of oil recovery and environmental concerns are bringing to prominence the use of carbon dioxide (CO2) as a tertiary recovery agent. Assessment of the impact of flooding with CO2 all eligible reservoirs in the United States not yet undergoing enhanced oil recovery (EOR) requires making the best possible use of the experience gained in 40 years of applications. Review of the publicly available literature has located relevant CO2-EOR information for 53 units (fields, reservoirs, pilot areas) in the United States and 17 abroad.
As the world simultaneously faces an increasing concentration of CO2 in the atmosphere and a higher demand for fossil fuels, the CO2-EOR process continues to gain popularity for its efficiency as a tertiary recovery agent and for the potential for having some CO2 trapped in the subsurface as an unintended consequence of the enhanced production (Advanced Resources International and Melzer Consulting, 2009). More extensive application of CO2-EOR worldwide, however, is not making it significantly easier to predict the exact outcome of the CO2 flooding in new reservoirs. The standard approach to examine and manage risks is to analyze the intended target by conducting laboratory work, running simulation models, and, finally, gaining field experience with a pilot test. This approach, though, is not always possible. For example, assessment of the potential of CO2-EOR at the national level in a vast country such as the United States requires making forecasts based on information already available.
Although many studies are proprietary, the published literature has provided reviews of CO2-EOR projects. Yet, there is always interest in updating reports and analyzing the information under new perspectives. Brock and Bryan (1989) described results obtained during the earlier days of CO2-EOR from 1972 to 1987. Most of the recovery predictions, however, were based on intended injections of 30 percent the size of the reservoir’s hydrocarbon pore volume (HCPV), and the predictions in most cases badly missed the actual recoveries because of the embryonic state of tertiary recovery in general and CO2 flooding in particular at the time. Brock and Bryan (1989), for example, reported for the Weber Sandstone in the Rangely oil field in Colorado, an expected recovery of 7.5 percent of the original oil in place (OOIP) after injecting a volume of CO2 equivalent to 30 percent of the HCPV, but Clark (2012) reported that after injecting a volume of CO2 equivalent to 46 percent of the HCPV, the actual recovery was 4.8 percent of the OOIP. Decades later, the numbers by Brock and Bryan (1989) continue to be cited as part of expanded reviews, such as the one by Kuuskraa and Koperna (2006). Other comprehensive reviews including recovery factors are those of Christensen and others (2001) and Lake and Walsh (2008). The Oil and Gas Journal (O&GJ) periodically reports on active CO2-EOR operations worldwide, but those releases do not include recovery factors. The monograph by Jarrell and others (2002) remains the most technically comprehensive publication on CO2 flooding, but it does not cover recovery factors either.
This chapter is a review of the literature found in a search for information about CO2-EOR. It has been prepared as part of a project by the U.S. Geological Survey (USGS) to assess the incremental oil production that would be technically feasible by CO2 flooding of all suitable oil reservoirs in the country not yet undergoing tertiary recovery.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Three approaches for estimating recovery factors in carbon dioxide enhanced oil recovery (Scientific Investigations Report 2017–5062)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Virginia","doi":"10.3133/sir20175062D","usgsCitation":"Olea, R.A., 2017, Carbon dioxide enhanced oil recovery performance according to the literature, chap. D of Verma, M.K., ed., Three approaches for estimating recovery factors in carbon dioxide enhanced oil recovery: U.S. Geological Survey Scientific Investigations Report 2017–5062, p. D1–D21, https://doi.org/10.3133/sir20175062D.","productDescription":"iii, 21 p.","numberOfPages":"25","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":343121,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5062/d/coverthb.jpg"},{"id":343122,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5062/d/sir20175062_chapd.pdf","text":"Report","size":"570 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5062D"}],"contact":" Eastern Energy Resources Science Center
U.S. Geological Survey
Mail Stop 956 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
The U.S. Geological Survey (USGS) compared methods for estimating an incremental recovery factor (RF) for the carbon dioxide enhanced oil recovery (CO2-EOR) process involving the injection of CO2 into oil reservoirs. This chapter first provides some basic information on the RF, including its dependence on various reservoir and operational parameters, and then discusses the three development phases of oil recovery—primary, secondary, and tertiary (EOR). It ends with a brief discussion of the three approaches for estimating recovery factors, which are detailed in subsequent chapters.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Three approaches for estimating recovery factors in carbon dioxide enhanced oil recovery (Scientific Investigations Report 2017–5062)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175062A","usgsCitation":"Verma, M.K., 2017, General introduction and recovery factors, chap. A of Verma, M.K., ed., Three approaches for estimating recovery factors in carbon dioxide enhanced oil recovery: U.S. Geological Survey Scientific Investigations Report 2017–5062, p. A1–A3, https://doi.org/10.3133/sir20175062A.","productDescription":"iii, 3 p.","numberOfPages":"7","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":343114,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5062/a/sir20175062_chapa.pdf","text":"Report","size":"239 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5062A"},{"id":343113,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5062/a/coverthb.jpg"}],"contact":" Eastern Energy Resources Science Center
U.S. Geological Survey
Mail Stop 956 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
In order to determine the hydrocarbon potential of oil reservoirs within the U.S. sedimentary basins for which the carbon dioxide enhanced oil recovery (CO2-EOR) process has been considered suitable, the CO2 Prophet model was chosen by the U.S. Geological Survey (USGS) to be the primary source for estimating recovery-factor values for individual reservoirs. The choice was made because of the model’s reliability and the ease with which it can be used to assess a large number of reservoirs. The other two approaches—the empirical decline curve analysis (DCA) method and a review of published literature on CO2-EOR projects—were deployed to verify the results of the CO2 Prophet model. This chapter discusses the results from CO2 Prophet (chapter B, by Emil D. Attanasi, this report) and compares them with results from decline curve analysis (chapter C, by Hossein Jahediesfanjani) and those reported in the literature for selected reservoirs with adequate data for analyses (chapter D, by Ricardo A. Olea).
To estimate the technically recoverable hydrocarbon potential for oil reservoirs where CO2-EOR has been applied, two of the three approaches—CO2 Prophet modeling and DCA—do not include analysis of economic factors, while the third approach—review of published literature—implicitly includes economics. For selected reservoirs, DCA has provided estimates of the technically recoverable hydrocarbon volumes, which, in combination with calculated amounts of original oil in place (OOIP), helped establish incremental CO2-EOR recovery factors for individual reservoirs.
The review of published technical papers and reports has provided substantial information on recovery factors for 70 CO2-EOR projects that are either commercially profitable or classified as pilot tests. When comparing the results, it is important to bear in mind the differences and limitations of these three approaches.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Three approaches for estimating recovery factors in carbon dioxide enhanced oil recovery (Scientific Investigations Report 2017–5062)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175062E","usgsCitation":"Verma, M.K., 2017, Summary of the analyses for recovery factors, chap. E of Verma, M.K., ed., Three approaches for estimating recovery factors in carbon dioxide enhanced oil recovery: U.S. Geological Survey Scientific Investigations Report 2017–5062, p. E1–E2, https://doi.org/10.3133/sir20175062E.","productDescription":"iii, 2 p.","numberOfPages":"6","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":343123,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5062/e/coverthb.jpg"},{"id":343124,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5062/e/sir20175062_chape.pdf","text":"Report","size":"198 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5062E"}],"contact":" Eastern Energy Resources Science Center
U.S. Geological Survey
Mail Stop 956 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
In the decline curve analysis (DCA) method of estimating recoverable hydrocarbon volumes, the analyst uses historical production data from a well, lease, group of wells (or pattern), or reservoir and plots production rates against time or cumulative production for the analysis. The DCA of an individual well is founded on the same basis as the fluid-flow principles that are used for pressure-transient analysis of a single well in a reservoir domain and therefore can provide scientifically reasonable and accurate results. However, when used for a group of wells, a lease, or a reservoir, the DCA becomes more of an empirical method. Plots from the DCA reflect the reservoir response to the oil withdrawal (or production) under the prevailing operating and reservoir conditions, and they continue to be good tools for estimating recoverable hydrocarbon volumes and future production rates. For predicting the total recoverable hydrocarbon volume, the DCA results can help the analyst to evaluate the reservoir performance under any of the three phases of reservoir productive life—primary, secondary (waterflood), or tertiary (enhanced oil recovery) phases—so long as the historical production data are sufficient to establish decline trends at the end of the three phases.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Three approaches for estimating recovery factors in carbon dioxide enhanced oil recovery (Scientific Investigations Report 2017–5062)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175062C","usgsCitation":"Jahediesfanjani, Hossein, 2017, Application of decline curve analysis to estimate recovery factors for carbon dioxide enhanced oil recovery, chap. C of Verma, M.K., ed., Three approaches for estimating recovery factors in carbon dioxide enhanced oil recovery: U.S. Geological Survey Scientific Investigations Report 2017–5062, \np. C1–C20, https://doi.org/10.3133/sir20175062C.","productDescription":"iv, 20 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":343119,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5062/c/sir20175062_chapc.pdf","text":"Report","size":"1.10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017=5062C"},{"id":343118,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5062/c/coverthb.jpg"}],"contact":" Eastern Energy Resources Science Center
U.S. Geological Survey
Mail Stop 956 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
The Oil and Gas Journal’s enhanced oil recovery (EOR) survey for 2014 (Koottungal, 2014) showed that gas injection is the most frequently applied method of EOR in the United States and that carbon dioxide (CO2 ) is the most commonly used injection fluid for miscible operations. The CO2-EOR process typically follows primary and secondary (waterflood) phases of oil reservoir development. The common objective of implementing a CO2-EOR program is to produce oil that remains after the economic limit of waterflood recovery is reached. Under conditions of miscibility or multicontact miscibility, the injected CO2 partitions between the gas and liquid CO2 phases, swells the oil, and reduces the viscosity of the residual oil so that the lighter fractions of the oil vaporize and mix with the CO2 gas phase (Teletzke and others, 2005). Miscibility occurs when the reservoir pressure is at least at the minimum miscibility pressure (MMP). The MMP depends, in turn, on oil composition, impurities of the CO2 injection stream, and reservoir temperature. At pressures below the MMP, component partitioning, oil swelling, and viscosity reduction occur, but the efficiency is increasingly reduced as the pressure falls farther below the MMP.
CO2-EOR processes are applied at the reservoir level, where a reservoir is defined as an underground formation containing an individual and separate pool of producible hydrocarbons that is confined by impermeable rock or water barriers and is characterized by a single natural pressure system. A field may consist of a single reservoir or multiple reservoirs that are not in communication but which may be associated with or related to a single structural or stratigraphic feature (U.S. Energy Information Administration [EIA], 2000).
The purpose of modeling the CO2-EOR process is discussed along with the potential CO2-EOR predictive models. The data demands of models and the scope of the assessments require tradeoffs between reservoir-specific data that can be assembled and simplifying assumptions that allow assignment of default values for some reservoir parameters. These issues are discussed in the context of the CO2 Prophet EOR model, and their resolution is demonstrated with the computation of recovery-factor estimates for CO2-EOR of 143 reservoirs in the Powder River Basin Province in southeastern Montana and northeastern Wyoming.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Three approaches for estimating recovery factors in carbon dioxide enhanced oil recovery (Scientific Investigations Report 2017–5062)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175062B","usgsCitation":"Attanasi, E.D., 2017, Using CO2 Prophet to estimate recovery factors for carbon dioxide enhanced oil recovery, chap. B of Verma, M.K., ed., Three approaches for estimating recovery factors in carbon dioxide enhanced oil recovery: U.S. Geological Survey Scientific Investigations Report 2017–5062, p. B1–B10, https://doi.org/10.3133/sir20175062B.","productDescription":"iii, 10 p.","numberOfPages":"14","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":343112,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5062/b/sir20175062_chapb.pdf","text":"Report","size":"377 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5062B"},{"id":343111,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5062/b/coverthb.jpg"}],"contact":" Eastern Energy Resources Science Center
U.S. Geological Survey
Mail Stop 956 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
The U.S. Geological Survey has developed a new global high-resolution hydrologic derivative database. Loosely modeled on the HYDRO1k database, this new database, entitled Hydrologic Derivatives for Modeling and Analysis, provides comprehensive and consistent global coverage of topographically derived raster layers (digital elevation model data, flow direction, flow accumulation, slope, and compound topographic index) and vector layers (streams and catchment boundaries). The coverage of the data is global, and the underlying digital elevation model is a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), GMTED2010 (Global Multi-resolution Terrain Elevation Data 2010), and the SRTM (Shuttle Radar Topography Mission). For most of the globe south of 60°N., the raster resolution of the data is 3 arc-seconds, corresponding to the resolution of the SRTM. For the areas north of 60°N., the resolution is 7.5 arc-seconds (the highest resolution of the GMTED2010 dataset) except for Greenland, where the resolution is 30 arc-seconds. The streams and catchments are attributed with Pfafstetter codes, based on a hierarchical numbering system, that carry important topological information. This database is appropriate for use in continental-scale modeling efforts. The work described in this report was conducted by the U.S. Geological Survey in cooperation with the National Aeronautics and Space Administration Goddard Space Flight Center.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1053","collaboration":"Prepared in cooperation with the National Aeronautics and Space Administration Goddard Space Flight Center","usgsCitation":"Verdin, K.L., 2017, Hydrologic Derivatives for Modeling and Analysis—A new global high-resolution database: U.S. Geological Survey Data Series 1053, 16 p., https://doi.org/10.3133/ds1053.","productDescription":"Report: iv, 16 p.; Data Release","numberOfPages":"24","onlineOnly":"Y","ipdsId":"IP-079740","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":343796,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1053/ds1053.pdf","text":"Report","size":"7.92 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1053"},{"id":343795,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1053/coverthb.jpg"},{"id":343823,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7S180ZP","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Hydrologic Derivatives for Modeling and Applications (HDMA) database"}],"contact":"Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046
We produce a one‐year 2017 seismic‐hazard forecast for the central and eastern United States from induced and natural earthquakes that updates the 2016 one‐year forecast; this map is intended to provide information to the public and to facilitate the development of induced seismicity forecasting models, methods, and data. The 2017 hazard model applies the same methodology and input logic tree as the 2016 forecast, but with an updated earthquake catalog. We also evaluate the 2016 seismic‐hazard forecast to improve future assessments. The 2016 forecast indicated high seismic hazard (greater than 1% probability of potentially damaging ground shaking in one year) in five focus areas: Oklahoma–Kansas, the Raton basin (Colorado/New Mexico border), north Texas, north Arkansas, and the New Madrid Seismic Zone. During 2016, several damaging induced earthquakes occurred in Oklahoma within the highest hazard region of the 2016 forecast; all of the 21 moment magnitude (M) ≥4 and 3 M≥5 earthquakes occurred within the highest hazard area in the 2016 forecast. Outside the Oklahoma–Kansas focus area, two earthquakes with M≥4 occurred near Trinidad, Colorado (in the Raton basin focus area), but no earthquakes with M≥2.7 were observed in the north Texas or north Arkansas focus areas. Several observations of damaging ground‐shaking levels were also recorded in the highest hazard region of Oklahoma. The 2017 forecasted seismic rates are lower in regions of induced activity due to lower rates of earthquakes in 2016 compared with 2015, which may be related to decreased wastewater injection caused by regulatory actions or by a decrease in unconventional oil and gas production. Nevertheless, the 2017 forecasted hazard is still significantly elevated in Oklahoma compared to the hazard calculated from seismicity before 2009.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220170005","usgsCitation":"Petersen, M.D., Mueller, C., Moschetti, M.P., Hoover, S.M., Shumway, A., McNamara, D.E., Williams, R., Llenos, A.L., Ellsworth, W., Rubinstein, J.L., McGarr, A.F., and Rukstales, K.S., 2017, 2017 One‐year seismic‐hazard forecast for the central and eastern United States from induced and natural earthquakes: Seismological Research Letters, v. 88, no. 3, p. 772-783, https://doi.org/10.1785/0220170005.","productDescription":"12 p.","startPage":"772","endPage":"783","ipdsId":"IP-083989","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":438266,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7KP80B9","text":"USGS data release","linkHelpText":"Earthquake catalogs for the 2017 Central and Eastern U.S. short-term seismic hazard model"},{"id":438265,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7RV0KWR","text":"USGS data release","linkHelpText":"2017 One-Year Seismic Hazard Forecast for the Central and Eastern United States from Induced and Natural Earthquakes"},{"id":343937,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"88","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-01","publicationStatus":"PW","scienceBaseUri":"596dcca1e4b0d1f9f062754e","contributors":{"authors":[{"text":"Petersen, Mark D. 0000-0001-8542-3990 mpetersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8542-3990","contributorId":1163,"corporation":false,"usgs":true,"family":"Petersen","given":"Mark","email":"mpetersen@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":705084,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mueller, Charles 0000-0002-1868-9710 cmueller@usgs.gov","orcid":"https://orcid.org/0000-0002-1868-9710","contributorId":140380,"corporation":false,"usgs":true,"family":"Mueller","given":"Charles","email":"cmueller@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705085,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705086,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hoover, Susan M. 0000-0002-8682-6668 shoover@usgs.gov","orcid":"https://orcid.org/0000-0002-8682-6668","contributorId":5715,"corporation":false,"usgs":true,"family":"Hoover","given":"Susan","email":"shoover@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705087,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shumway, Allison 0000-0003-1142-7141 ashumway@usgs.gov","orcid":"https://orcid.org/0000-0003-1142-7141","contributorId":147862,"corporation":false,"usgs":true,"family":"Shumway","given":"Allison","email":"ashumway@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705088,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McNamara, Daniel E. 0000-0001-6860-0350 mcnamara@usgs.gov","orcid":"https://orcid.org/0000-0001-6860-0350","contributorId":402,"corporation":false,"usgs":true,"family":"McNamara","given":"Daniel","email":"mcnamara@usgs.gov","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705089,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Williams, Robert 0000-0002-2973-8493 rawilliams@usgs.gov","orcid":"https://orcid.org/0000-0002-2973-8493","contributorId":140741,"corporation":false,"usgs":true,"family":"Williams","given":"Robert","email":"rawilliams@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705090,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Llenos, Andrea L. 0000-0002-4088-6737 allenos@usgs.gov","orcid":"https://orcid.org/0000-0002-4088-6737","contributorId":4455,"corporation":false,"usgs":true,"family":"Llenos","given":"Andrea","email":"allenos@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":705091,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ellsworth, William L. 0000-0001-8378-4979","orcid":"https://orcid.org/0000-0001-8378-4979","contributorId":194691,"corporation":false,"usgs":true,"family":"Ellsworth","given":"William L.","affiliations":[],"preferred":false,"id":705092,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Rubinstein, Justin L. 0000-0003-1274-6785 jrubinstein@usgs.gov","orcid":"https://orcid.org/0000-0003-1274-6785","contributorId":2404,"corporation":false,"usgs":true,"family":"Rubinstein","given":"Justin","email":"jrubinstein@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":705093,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"McGarr, Arthur F. 0000-0001-9769-4093 mcgarr@usgs.gov","orcid":"https://orcid.org/0000-0001-9769-4093","contributorId":3178,"corporation":false,"usgs":true,"family":"McGarr","given":"Arthur","email":"mcgarr@usgs.gov","middleInitial":"F.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":705094,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Rukstales, Kenneth S. 0000-0003-2818-078X rukstales@usgs.gov","orcid":"https://orcid.org/0000-0003-2818-078X","contributorId":775,"corporation":false,"usgs":true,"family":"Rukstales","given":"Kenneth","email":"rukstales@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":705095,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70194723,"text":"70194723 - 2017 - A method for examining temporal changes in cyanobacterial harmful algal bloom spatial extent using satellite remote sensing","interactions":[],"lastModifiedDate":"2017-12-15T10:18:37","indexId":"70194723","displayToPublicDate":"2017-07-17T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1878,"text":"Harmful Algae","active":true,"publicationSubtype":{"id":10}},"title":"A method for examining temporal changes in cyanobacterial harmful algal bloom spatial extent using satellite remote sensing","docAbstract":"Cyanobacterial harmful algal blooms (CyanoHAB) are thought to be increasing globally over the past few decades, but relatively little quantitative information is available about the spatial extent of blooms. Satellite remote sensing provides a potential technology for identifying cyanoHABs in multiple water bodies and across geo-political boundaries. An assessment method was developed using MEdium Resolution Imaging Spectrometer (MERIS) imagery to quantify cyanoHAB surface area extent, transferable to different spatial areas, in Florida, Ohio, and California for the test period of 2008 to 2012. Temporal assessment was used to evaluate changes in satellite resolvable inland waterbodies for each state of interest. To further assess cyanoHAB risk within the states, the World Health Organization’s (WHO) recreational guidance level thresholds were used to categorize surface area of cyanoHABs into three risk categories: low, moderate, and high-risk bloom area. Results showed that in Florida, the area of cyanoHABs increased largely due to observed increases in high-risk bloom area. California exhibited a slight decrease in cyanoHAB extent, primarily attributed to decreases in Northern California. In Ohio (excluding Lake Erie), little change in cyanoHAB surface area was observed. This study uses satellite remote sensing to quantify changes in inland cyanoHAB surface area across numerous water bodies within an entire state. The temporal assessment method developed here will be relevant into the future as it is transferable to the Ocean Land Colour Instrument (OLCI) on Sentinel-3A/3B missions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.hal.2017.06.001","usgsCitation":"Urquhart, E.A., Schaeffer, B.A., Stumpf, R.P., Loftin, K.A., and Werdell, P.J., 2017, A method for examining temporal changes in cyanobacterial harmful algal bloom spatial extent using satellite remote sensing: Harmful Algae, v. 67, p. 144-152, https://doi.org/10.1016/j.hal.2017.06.001.","productDescription":"9 p.","startPage":"144","endPage":"152","ipdsId":"IP-087775","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":469677,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hal.2017.06.001","text":"Publisher Index Page"},{"id":349988,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Florida, Ohio","volume":"67","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fb81e4b06e28e9c23148","contributors":{"authors":[{"text":"Urquhart, Erin A.","contributorId":201327,"corporation":false,"usgs":false,"family":"Urquhart","given":"Erin","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":725009,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schaeffer, Blake A.","contributorId":201328,"corporation":false,"usgs":false,"family":"Schaeffer","given":"Blake","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":725010,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stumpf, Richard P.","contributorId":201329,"corporation":false,"usgs":false,"family":"Stumpf","given":"Richard","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":725011,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loftin, Keith A. 0000-0001-5291-876X kloftin@usgs.gov","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":868,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","email":"kloftin@usgs.gov","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":725008,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Werdell, P. Jeremy","contributorId":201330,"corporation":false,"usgs":false,"family":"Werdell","given":"P.","email":"","middleInitial":"Jeremy","affiliations":[],"preferred":false,"id":725012,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70189549,"text":"70189549 - 2017 - Atypical feeding behavior of Long-tailed Ducks in the wake of a commercial fishing boat while clamming","interactions":[],"lastModifiedDate":"2017-07-17T10:17:25","indexId":"70189549","displayToPublicDate":"2017-07-17T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2898,"text":"Northeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Atypical feeding behavior of Long-tailed Ducks in the wake of a commercial fishing boat while clamming","docAbstract":"A foraging group of Clangula hyemalis (Long-tailed Duck) was observed on 10 February 2010 diving behind a commercial boat that was clamming near Monomoy Island, Nantucket Sound, MA. We used a shotgun to collect 9 of the ducks, and our analyses of gizzard and gullet (esophagus and proventriculus) revealed 37 food items in the gizzard and 16 in the gullet. Mollusca were the dominant food in the gizzard (49%), whereas Crustacea were dominant in the gullet (57%). Crustacea were the second most important food in the gizzard (38%), whereas Mollusca were the second most important food in the gullet (31%). Relatively high volumes of the Amphipoda Caprella sp. (skeleton shrimp) and the Decopoda Crangon septemspinosa (Sand Shrimp) were recorded in the gullet and gizzard. Ensis directus (Atlantic Jackknife Clam) formed the greatest volume of Mollusca in the gizzard (15%) and in the gullet (15%). Long-tailed Ducks had fed on this Bivalvia and several other species of Mollusca that had no shell or broken shell when consumed. Many of the food organisms were apparently dislodged and some damaged by the clamming operation creating an opportunistic feeding strategy for the Long-tailed Ducks.
","language":"English","publisher":"Eagle Hill Institute","doi":"10.1656/045.024.0213","usgsCitation":"Perry, M., Osenton, P.C., and White, T.P., 2017, Atypical feeding behavior of Long-tailed Ducks in the wake of a commercial fishing boat while clamming: Northeastern Naturalist, v. 24, no. 2, p. N19-N25, https://doi.org/10.1656/045.024.0213.","productDescription":"7 p.","startPage":"N19","endPage":"N25","ipdsId":"IP-085297","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":343923,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Nantucket Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.63247680664062,\n 41.23031465959445\n ],\n [\n -69.92111206054686,\n 41.23031465959445\n ],\n [\n -69.92111206054686,\n 41.69957665997156\n ],\n [\n -70.63247680664062,\n 41.69957665997156\n ],\n [\n -70.63247680664062,\n 41.23031465959445\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"24","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-15","publicationStatus":"PW","scienceBaseUri":"596dcca1e4b0d1f9f062754b","contributors":{"authors":[{"text":"Perry, Matthew 0000-0001-6452-9534 mperry@usgs.gov","orcid":"https://orcid.org/0000-0001-6452-9534","contributorId":179173,"corporation":false,"usgs":true,"family":"Perry","given":"Matthew","email":"mperry@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":705143,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Osenton, Peter C.","contributorId":174040,"corporation":false,"usgs":false,"family":"Osenton","given":"Peter","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":705144,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, Timothy P.","contributorId":194703,"corporation":false,"usgs":false,"family":"White","given":"Timothy","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":705145,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70187752,"text":"fs20173038 - 2017 - The U.S. Geological Survey Astrogeology Science Center","interactions":[],"lastModifiedDate":"2018-11-08T16:37:32","indexId":"fs20173038","displayToPublicDate":"2017-07-17T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3038","title":"The U.S. Geological Survey Astrogeology Science Center","docAbstract":"In 1960, Eugene Shoemaker and a small team of other scientists founded the field of astrogeology to develop tools and methods for astronauts studying the geology of the Moon and other planetary bodies. Subsequently, in 1962, the U.S. Geological Survey Branch of Astrogeology was established in Menlo Park, California. In 1963, the Branch moved to Flagstaff, Arizona, to be closer to the young lava flows of the San Francisco Volcanic Field and Meteor Crater, the best preserved impact crater in the world. These geologic features of northern Arizona were considered good analogs for the Moon and other planetary bodies and valuable for geologic studies and astronaut field training. From its Flagstaff campus, the USGS has supported the National Aeronautics and Space Administration (NASA) space program with scientific and cartographic expertise for more than 50 years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173038","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-084115","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":343926,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3038/coverthb.jpg"},{"id":343927,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3038/fs20173038.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3038"}],"contact":"Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
The U.S. Geological Survey (USGS), in cooperation with the U.S. Nuclear Regulatory Commission, has investigated statistical methods for probabilistic flood hazard assessment to provide guidance on very low annual exceedance probability (AEP) estimation of peak-streamflow frequency and the quantification of corresponding uncertainties using streamgage-specific data. The term “very low AEP” implies exceptionally rare events defined as those having AEPs less than about 0.001 (or 1 × 10–3 in scientific notation or for brevity 10–3). Such low AEPs are of great interest to those involved with peak-streamflow frequency analyses for critical infrastructure, such as nuclear power plants. Flood frequency analyses at streamgages are most commonly based on annual instantaneous peak streamflow data and a probability distribution fit to these data. The fitted distribution provides a means to extrapolate to very low AEPs. Within the United States, the Pearson type III probability distribution, when fit to the base-10 logarithms of streamflow, is widely used, but other distribution choices exist. The USGS-PeakFQ software, implementing the Pearson type III within the Federal agency guidelines of Bulletin 17B (method of moments) and updates to the expected moments algorithm (EMA), was specially adapted for an “Extended Output” user option to provide estimates at selected AEPs from 10–3 to 10–6. Parameter estimation methods, in addition to product moments and EMA, include L-moments, maximum likelihood, and maximum product of spacings (maximum spacing estimation). This study comprehensively investigates multiple distributions and parameter estimation methods for two USGS streamgages (01400500 Raritan River at Manville, New Jersey, and 01638500 Potomac River at Point of Rocks, Maryland). The results of this study specifically involve the four methods for parameter estimation and up to nine probability distributions, including the generalized extreme value, generalized log-normal, generalized Pareto, and Weibull. Uncertainties in streamflow estimates for corresponding AEP are depicted and quantified as two primary forms: quantile (aleatoric [random sampling] uncertainty) and distribution-choice (epistemic [model] uncertainty). Sampling uncertainties of a given distribution are relatively straightforward to compute from analytical or Monte Carlo-based approaches. Distribution-choice uncertainty stems from choices of potentially applicable probability distributions for which divergence among the choices increases as AEP decreases. Conventional goodness-of-fit statistics, such as Cramér-von Mises, and L-moment ratio diagrams are demonstrated in order to hone distribution choice. The results generally show that distribution choice uncertainty is larger than sampling uncertainty for very low AEP values.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175038","collaboration":"Prepared in cooperation with the U.S. Nuclear Regulatory Commission","usgsCitation":"Asquith, W.H., Kiang, J.E., and Cohn, T.A., 2017, Application of at-site peak-streamflow frequency analyses for very low annual exceedance probabilities: U.S. Geological Survey Scientific Investigation Report 2017–5038, 93 p., https://doi.org/10.3133/sir20175038.","productDescription":"ix, 93 p.","onlineOnly":"Y","ipdsId":"IP-079000","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":343747,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5038/coverthb.jpg"},{"id":343748,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5038/sir20175038.pdf","text":"Report","size":"6.24 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5038"}],"contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
The portion of the Snake River fall Chinook Salmon Oncorhynchus tshawytscha ESU that spawns upstream of Lower Granite Dam transitioned from low to high abundance during 1992–2016 in association with U.S. Endangered Species Act recovery efforts and other federally mandated actions. This annual report focuses on (1) numeric and habitat use responses by natural- and hatchery-origin spawners, (2) phenotypic and numeric responses by natural-origin juveniles, and (3) predator responses in the Snake River upper and lower reaches as abundance of adult and juvenile fall Chinook Salmon increased. Spawners have located and used most of the available spawning habitat and that habitat is gradually approaching redd capacity. Timing of spawning and fry emergence has been relatively stable; whereas the timing of parr dispersal from riverine rearing habitat into Lower Granite Reservoir has become earlier as apparent abundance of juveniles has increased. Growth rate (g/d) and dispersal size of parr also declined as apparent abundance of juveniles increased. Passage timing of smolts from the two Snake River reaches has become earlier and downstream movement rate faster as estimated abundance of fall Chinook Salmon smolts in Lower Granite Reservoir has increased. In 2016, we described estimated the consumption rate and loss of subyearlings by Smallmouth Bass before, during, and after four hatchery releases. Before releases, Smallmouth Bass consumption rates of subyearling was low (0–0.36 fish/bass/d), but the day after the releases consumption rates reached as high as 1.6 fish/bass/d. Bass consumption in the upper portion of Hells Canyon was high for about 1–2 d before returning to pre-release levels, but in the lower river consumption rates were reduced but took longer to return to pre-release levels. We estimated that most of the subyearlings consumed by bass were of hatchery origin. Smallmouth Bass predation on subyearlings is intense following a hatchery release, but the predation pressure is relatively short-lived as subyearlings quickly disperse downstream. This information will allow us to better estimate subyearling loss to predation from our past efforts at time intervals less than 2 weeks. These findings coupled with stock-recruitment analyses presented in this report provide evidence for density-dependence in the Snake River reaches and in Lower Granite Reservoir that was influenced by the expansion of the recovery program. The long-term goal is to use the information covered here in a comprehensive modeling effort to conduct action effectiveness and uncertainty research and to inform Fish Population, Hydrosystem, Harvest, Hatchery, and Predation and Invasive Species Management RM&E.
","language":"English","publisher":"Bonneville Power Administration","usgsCitation":"Connor, W.P., Mullins, F.L., Tiffan, K.F., Plumb, J.M., Perry, R.W., Erhardt, J.M., Hemingway, R.J., Bickford, B.K., and Rhodes, T.N., 2017, Research, monitoring, and evaluation of emerging issues and measures to recover the Snake River Fall Chinook Salmon ESU, 1/1/2016 - 12/31/2016, 67 p.","productDescription":"67 p.","ipdsId":"IP-085073","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":343912,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":343904,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/Document.mvc/Viewer/P154616"}],"country":"United States","otherGeospatial":"Snake River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.35546875000001,\n 44.715513732021336\n ],\n [\n -114.6478271484375,\n 44.715513732021336\n ],\n [\n -114.6478271484375,\n 47.10378387099161\n ],\n [\n -119.35546875000001,\n 47.10378387099161\n ],\n [\n -119.35546875000001,\n 44.715513732021336\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"596c7b6ee4b0d1f9f0615dc9","contributors":{"authors":[{"text":"Connor, William P.","contributorId":107589,"corporation":false,"usgs":false,"family":"Connor","given":"William","email":"","middleInitial":"P.","affiliations":[{"id":16677,"text":"U.S. Fish and Wildlife Service, Idaho Fishery Resource Office, 276 Dworshak Complex Drive, Orofino, ID 83544","active":true,"usgs":false}],"preferred":false,"id":705121,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mullins, Frank L.","contributorId":146343,"corporation":false,"usgs":false,"family":"Mullins","given":"Frank","email":"","middleInitial":"L.","affiliations":[{"id":16677,"text":"U.S. Fish and Wildlife Service, Idaho Fishery Resource Office, 276 Dworshak Complex Drive, Orofino, ID 83544","active":true,"usgs":false}],"preferred":false,"id":705122,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tiffan, Kenneth F. 0000-0002-5831-2846 ktiffan@usgs.gov","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":3200,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","email":"ktiffan@usgs.gov","middleInitial":"F.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":705120,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Plumb, John M. 0000-0003-4255-1612 jplumb@usgs.gov","orcid":"https://orcid.org/0000-0003-4255-1612","contributorId":3569,"corporation":false,"usgs":true,"family":"Plumb","given":"John","email":"jplumb@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":705123,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":705124,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Erhardt, John M. 0000-0002-5170-285X jerhardt@usgs.gov","orcid":"https://orcid.org/0000-0002-5170-285X","contributorId":5380,"corporation":false,"usgs":true,"family":"Erhardt","given":"John","email":"jerhardt@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":705125,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hemingway, Rulon J. 0000-0001-8143-0325 rhemingway@usgs.gov","orcid":"https://orcid.org/0000-0001-8143-0325","contributorId":194697,"corporation":false,"usgs":true,"family":"Hemingway","given":"Rulon","email":"rhemingway@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":705127,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bickford, Brad K. 0000-0003-3756-6588 bbickford@usgs.gov","orcid":"https://orcid.org/0000-0003-3756-6588","contributorId":140889,"corporation":false,"usgs":true,"family":"Bickford","given":"Brad","email":"bbickford@usgs.gov","middleInitial":"K.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":705128,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rhodes, Tobyn N. 0000-0002-4023-4827 trhodes@usgs.gov","orcid":"https://orcid.org/0000-0002-4023-4827","contributorId":140890,"corporation":false,"usgs":true,"family":"Rhodes","given":"Tobyn","email":"trhodes@usgs.gov","middleInitial":"N.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":705129,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70189540,"text":"70189540 - 2017 - Snake River Fall Chinook Salmon life history investigations","interactions":[],"lastModifiedDate":"2017-07-16T09:30:16","indexId":"70189540","displayToPublicDate":"2017-07-16T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Snake River Fall Chinook Salmon life history investigations","docAbstract":"Predation by nonnative fishes is one factor that has been implicated in the decline of juvenile salmonids in the Pacific Northwest. Impoundment of much of the Snake and Columbia rivers has altered food webs and created habitat favorable for species such as Smallmouth Bass Micropterus dolomieu. Smallmouth Bass are common throughout the Columbia River basin and have become the most abundant predator in lower Snake River reservoirs (Zimmerman and Parker 1995). This is a concern for Snake River Fall Chinook Salmon Oncorhynchus tshawytscha (hereafter, subyearlings) that may be particularly vulnerable due to their relatively small size and because their main-stem rearing habitats often overlap or are in close proximity to habitats used by Smallmouth Bass (Curet 1993; Tabor et al. 1993).
Concern over juvenile salmon predation spawned a number of large-scale studies to quantify its effect in the late 1980s, 1990s, and early 2000s (Poe et al. 1991; Rieman et al. 1991; Vigg et al. 1991; Fritts and Pearsons 2004; Naughton et al. 2004). Smallmouth Bass predation represented 9% of total salmon consumption by predatory fishes in John Day Reservoir, Columbia River, from 1983 through 1986 (Rieman et al. 1991). In transitional habitat between the Hanford Reach of the Columbia River and McNary Reservoir, juvenile salmon (presumably subyearlings) were found in 65% of Smallmouth Bass (>200 mm) stomachs and comprised 59% of the diet by weight (Tabor et al. 1993). Within Lower Granite Reservoir on the Snake River, Naughton et al. (2004) showed that monthly consumption (based on weight) ranged from 5% in the upper reaches of the reservoir to 11% in the forebay. However, studies in the Snake River were conducted soon after Endangered Species Act (ESA) listing of Snake River Fall Chinook Salmon (NMFS 1992). During this time, Fall Chinook Salmon abundance was at an historic low, which may explain why consumption rates were relatively low compared to those from studies conducted in the Columbia and Yakima rivers where abundance was higher (e.g., Tabor et al. 1993; Fritts and Pearsons 2004).
We speculate that predation on subyearlings by Smallmouth Bass in the Snake River may have increased in recent years for several reasons. Since their ESA listing, recovery measures implemented for Snake River Fall Chinook salmon have resulted in a large increase in the juvenile population (Connor et al. 2013). Considering that subyearlings probably now make up a larger portion of the forage fish population, it is plausible they should make up a large portion of Smallmouth Bass diets. Second, migrating subyearlings delay downstream movement in the transition zones of the Clearwater River and Snake River for varying lengths of time (Tiffan et al. 2010), which increases their exposure and vulnerability to predators. Spatial overlap in locations of Smallmouth Bass and subyearlings that died during migration provides support for this (Tiffan et al. 2010). Finally, the later outmigration of subyearlings from the Clearwater River results in their presence in Lower Granite Reservoir during the warmest summer months when predation rates of Smallmouth Bass should be highest.
In 2016, we focused our efforts on Smallmouth Bass predation in Lower Granite Reservoir downstream of the transition zones and the confluence area where we worked during 2012–2015. Similar to past years, our first objective was to quantify Smallmouth Bass consumption rates of subyearlings, determine relative bass abundance, and describe bass diets. In addition, Tiffan et al. (2016a) posited that predation risk to subyearlings may be higher in shoreline habitats that are more suitable for Smallmouth Bass and lower in shoreline habitats that are more suitable for subyearlings. To test this hypothesis, our second objective examines the relationship between Smallmouth Bass predation of subyearlings and habitat suitability.
","language":"English","publisher":"Bonneville Power Administration","usgsCitation":"Erhardt, J.M., Bickford, B.K., Hemingway, R.J., Rhodes, T.N., and Tiffan, K.F., 2017, Snake River Fall Chinook Salmon life history investigations, 21 p.","productDescription":"21 p.","ipdsId":"IP-085067","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":343911,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":343903,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/Document.mvc/Viewer/P154611"}],"country":"United States","state":"Idaho, Washington","otherGeospatial":"Lower Granite Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.45620727539062,\n 46.35545866673858\n ],\n [\n -116.95083618164061,\n 46.35545866673858\n ],\n [\n -116.95083618164061,\n 46.68242094391242\n ],\n [\n -117.45620727539062,\n 46.68242094391242\n ],\n [\n -117.45620727539062,\n 46.35545866673858\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"596c7b71e4b0d1f9f0615dcb","contributors":{"authors":[{"text":"Erhardt, John M. 0000-0002-5170-285X jerhardt@usgs.gov","orcid":"https://orcid.org/0000-0002-5170-285X","contributorId":5380,"corporation":false,"usgs":true,"family":"Erhardt","given":"John","email":"jerhardt@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":705116,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bickford, Brad K. 0000-0003-3756-6588 bbickford@usgs.gov","orcid":"https://orcid.org/0000-0003-3756-6588","contributorId":140889,"corporation":false,"usgs":true,"family":"Bickford","given":"Brad","email":"bbickford@usgs.gov","middleInitial":"K.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":705117,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hemingway, Rulon J. 0000-0001-8143-0325 rhemingway@usgs.gov","orcid":"https://orcid.org/0000-0001-8143-0325","contributorId":194697,"corporation":false,"usgs":true,"family":"Hemingway","given":"Rulon","email":"rhemingway@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":705118,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rhodes, Tobyn N. 0000-0002-4023-4827 trhodes@usgs.gov","orcid":"https://orcid.org/0000-0002-4023-4827","contributorId":140890,"corporation":false,"usgs":true,"family":"Rhodes","given":"Tobyn","email":"trhodes@usgs.gov","middleInitial":"N.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":705119,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tiffan, Kenneth F. 0000-0002-5831-2846 ktiffan@usgs.gov","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":3200,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","email":"ktiffan@usgs.gov","middleInitial":"F.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":705115,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70189542,"text":"70189542 - 2017 - Preface to the special issue “Impact of omics on comparative immunology”","interactions":[],"lastModifiedDate":"2017-07-15T11:30:16","indexId":"70189542","displayToPublicDate":"2017-07-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1383,"text":"Developmental and Comparative Immunology","active":true,"publicationSubtype":{"id":10}},"title":"Preface to the special issue “Impact of omics on comparative immunology”","docAbstract":"No abstract available.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.dci.2017.05.006","usgsCitation":"Boudinot, P., Grimholt, U., and Hansen, J.D., 2017, Preface to the special issue “Impact of omics on comparative immunology”: Developmental and Comparative Immunology, v. 75, p. 1-2, https://doi.org/10.1016/j.dci.2017.05.006.","productDescription":"2 p.","startPage":"1","endPage":"2","ipdsId":"IP-087796","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":343906,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"75","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"596b2990e4b0d1f9f0615cf7","contributors":{"authors":[{"text":"Boudinot, Pierre","contributorId":194698,"corporation":false,"usgs":false,"family":"Boudinot","given":"Pierre","email":"","affiliations":[],"preferred":false,"id":705131,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grimholt, Unni","contributorId":194699,"corporation":false,"usgs":false,"family":"Grimholt","given":"Unni","email":"","affiliations":[],"preferred":false,"id":705132,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hansen, John D. 0000-0002-3006-2734 jhansen@usgs.gov","orcid":"https://orcid.org/0000-0002-3006-2734","contributorId":3440,"corporation":false,"usgs":true,"family":"Hansen","given":"John","email":"jhansen@usgs.gov","middleInitial":"D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":705130,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70187748,"text":"tm6F1 - 2017 - Coding conventions and principles for a National Land-Change Modeling Framework","interactions":[],"lastModifiedDate":"2017-07-17T10:33:31","indexId":"tm6F1","displayToPublicDate":"2017-07-14T14:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"6-F1","title":"Coding conventions and principles for a National Land-Change Modeling Framework","docAbstract":"This report establishes specific rules for writing computer source code for use with the National Land-Change Modeling Framework (NLCMF). These specific rules consist of conventions and principles for writing code primarily in the C and C++ programming languages. Collectively, these coding conventions and coding principles create an NLCMF programming style. In addition to detailed naming conventions, this report provides general coding conventions and principles intended to facilitate the development of high-performance software implemented with code that is extensible, flexible, and interoperable. Conventions for developing modular code are explained in general terms and also enabled and demonstrated through the appended templates for C++ base source-code and header files. The NLCMF limited-extern approach to module structure, code inclusion, and cross-module access to data is both explained in the text and then illustrated through the module templates. Advice on the use of global variables is provided.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section F: Land-change modeling and analysis in Book 6: Modeling techniques","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6F1","usgsCitation":"Donato, D.I., 2017, Coding conventions and principles for a National Land-Change Modeling Framework: U.S. Geological Survey Techniques and Methods, book 6, chap. F1, 30 p., https://doi.org/10.3133/tm6F1.","productDescription":"iv, 30 p. ","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-068071","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":343791,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/f01/coverthb.jpg"},{"id":343792,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/f01/tm6f1.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"TM 06-F1"}],"publicComments":"This report is Chapter 1 of Section F: Land-change modeling and analysis in Book 6: Modeling techniques.","contact":"Director, Eastern Geographic Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 521
Reston, VA 20192