The recent commercial availability of in-situ optical sensors, together with new techniques for data collection and analysis, provides the opportunity to monitor a wide range of water-quality constituents over time scales during which environmental conditions actually change. Traditional approaches for data collection (daily to monthly discrete samples) are often limited by high sample collection, processing, and analytical costs, difficult site access, and logistical challenges, particularly for long-term sampling at a large number of sites. Optical sensors that continuously measure constituents in the environment by absorbance or fluorescence properties (Figure 1) have had a long history of use in oceanography for measuring highly resolved concentrations and fluxes of organic matter, nutrients, and algal material. However, much of the work using commercially-available optical sensors in rivers and streams has taken place in only the last few years. Figure 1. [NOT SHOWN] Optical sensor technology is now sufficiently developed to warrant broader application for research and monitoring in coastal and freshwater systems, and the United States Geological Survey (a U.S. science agency) is now using these sensors in a variety of research and monitoring programs to better understand water quality in-situ and in real-time. Examples are numerous and range from the applications of nitrate sensors for calculating loads to estuaries susceptible to hypoxia (Pellerin et al., 2014) to the use of fluorometers to estimate methymercury fluxes (Bergamaschi et al., 2011) and disinfection byproduct formation (Carpenter et al., 2013). Transmitting these data in real-time provides information that can be used for early trend detection, help identify monitoring gaps critical for water management, and provide science-based decision support across a range of issues related to water quality, freshwater ecosystems, and human health. Despite the value of these sensors, collecting data that meet high-quality standards requires investment in and adherence to tested and established methods and protocols for sensor operation and data management (Pellerin et al., 2013). For example, optical sensor measurements can be strongly influenced by a variety of matrix effects, including water temperature, inner filtering from highly colored water, and scattering of light by suspended particles (Downing et al., 2012). Characterizing and correcting sensors for these effects – as well as the continued development of common methodologies and protocols for sensor use – will be critical to ensuring comparable measurements across sites and over time. In addition, collaborative efforts such as the Nutrient Sensor Challenge (www.nutrients-challenge.org) will continue to accelerate the development, production and use of affordable, reliable and accurate sensors for a range of environments. REFERENCES Bergamaschi .B.A., Fleck J.A., Downing B.D., Boss E., Pellerin B.A., Ganju N.K., Schoellhamer D.H., Byington A.A., Heim W.A., Stephenson M., Fujii R. (2011), Methyl mercury dynamics in a tidal wetland quantified using in situ optical measurements. Limnology and Oceanography, 56(4): 1355-1371. Carpenter K.D., Kraus T.E.C., Goldman J.H., Saraceno J., Downing B.D., Bergamaschi B.A., McGhee G., Triplett T. (2013), Sources and Characteristics of Organic Matter in the Clackamas River, Oregon, Related to the Formation of Disinfection By-products in Treated Drinking Water: U.S. Geological Survey Scientific Investigations Report 2013–5001, 78 p. Downing .B.D., Pellerin B.A., Bergamaschi B.A., Saraceno J., Kraus T.E.K. (2012), Seeing the light: The effects of particles, temperature and inner filtering on in situ CDOM fluorescence in rivers and streams. Limnology and Oceanography: Methods, 10: 767-775. Pellerin B.A., Bergamaschi B.A., Downing B.D., Saraceno J., Garrett J.D., Olsen L.D. (2013), Optical Techniques for the Determination of Nitrate in En
","conferenceTitle":"The SMART water grid International conference","conferenceDate":"October 27-28, 2015","conferenceLocation":"Incheon, South Korea","language":"English","usgsCitation":"Pellerin, B., 2015, Applications of optical sensors for high-frequency water-quality monitoring and research, The SMART water grid International conference, Incheon, South Korea, October 27-28, 2015, 1 p.","productDescription":"1 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068720","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":311184,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":307561,"type":{"id":15,"text":"Index Page"},"url":"https://www.swgic.org/sub/information/schedule.htm"}],"publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5643233ce4b0aafbcd017fcb","contributors":{"authors":[{"text":"Pellerin, Brian A. 0000-0003-3712-7884 bpeller@usgs.gov","orcid":"https://orcid.org/0000-0003-3712-7884","contributorId":147077,"corporation":false,"usgs":true,"family":"Pellerin","given":"Brian","email":"bpeller@usgs.gov","middleInitial":"A.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":570189,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70156292,"text":"70156292 - 2015 - Simulating maize yield and bomass with spatial variability of soil field capacity","interactions":[],"lastModifiedDate":"2016-01-06T10:35:22","indexId":"70156292","displayToPublicDate":"2015-10-27T17:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":684,"text":"Agronomy Journal","active":true,"publicationSubtype":{"id":10}},"title":"Simulating maize yield and bomass with spatial variability of soil field capacity","docAbstract":"Spatial variability in field soil properties is a challenge for system modelers who use single representative values, such as means, for model inputs, rather than their distributions. In this study, the root zone water quality model (RZWQM2) was first calibrated for 4 yr of maize (Zea mays L.) data at six irrigation levels in northern Colorado and then used to study spatial variability of soil field capacity (FC) estimated in 96 plots on maize yield and biomass. The best results were obtained when the crop parameters were fitted along with FCs, with a root mean squared error (RMSE) of 354 kg ha–1 for yield and 1202 kg ha–1 for biomass. When running the model using each of the 96 sets of field-estimated FC values, instead of calibrating FCs, the average simulated yield and biomass from the 96 runs were close to measured values with a RMSE of 376 kg ha–1 for yield and 1504 kg ha–1 for biomass. When an average of the 96 FC values for each soil layer was used, simulated yield and biomass were also acceptable with a RMSE of 438 kg ha–1 for yield and 1627 kg ha–1 for biomass. Therefore, when there are large numbers of FC measurements, an average value might be sufficient for model inputs. However, when the ranges of FC measurements were known for each soil layer, a sampled distribution of FCs using the Latin hypercube sampling (LHS) might be used for model inputs.
","language":"English","publisher":"American Society of Agronomy","publisherLocation":"Madison, WI","doi":"10.2134/agronj2015.0206","usgsCitation":"Ma, L., Ahuja, L., Trout, T., Nolan, B.T., and Malone, R.W., 2015, Simulating maize yield and bomass with spatial variability of soil field capacity: Agronomy Journal, v. 108, no. 1, p. 171-184, https://doi.org/10.2134/agronj2015.0206.","productDescription":"14 p.","startPage":"171","endPage":"184","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060069","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":313916,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"108","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"568e492ae4b0e7a44bc41a6a","contributors":{"authors":[{"text":"Ma, Liwang","contributorId":6751,"corporation":false,"usgs":false,"family":"Ma","given":"Liwang","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":583050,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ahuja, Lajpat","contributorId":100275,"corporation":false,"usgs":true,"family":"Ahuja","given":"Lajpat","email":"","affiliations":[],"preferred":false,"id":583051,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Trout, Thomas","contributorId":95785,"corporation":false,"usgs":true,"family":"Trout","given":"Thomas","email":"","affiliations":[],"preferred":false,"id":583052,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nolan, Bernard T. 0000-0002-6945-9659 btnolan@usgs.gov","orcid":"https://orcid.org/0000-0002-6945-9659","contributorId":2190,"corporation":false,"usgs":true,"family":"Nolan","given":"Bernard","email":"btnolan@usgs.gov","middleInitial":"T.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":568542,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Malone, Robert W.","contributorId":10347,"corporation":false,"usgs":false,"family":"Malone","given":"Robert","email":"","middleInitial":"W.","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":583053,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70158608,"text":"ds964 - 2015 - Public-supply water use in Kansas, 2013","interactions":[],"lastModifiedDate":"2016-02-24T10:29:28","indexId":"ds964","displayToPublicDate":"2015-10-27T14:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"964","title":"Public-supply water use in Kansas, 2013","docAbstract":"This report, prepared by the U.S. Geological Survey in cooperation with the Kansas Department of Agriculture’s Division of Water Resources, presents derivative statistics of water used by Kansas public-supply systems in 2013. The published statistics from the previous 4 years (2009–12) are also shown with the 2013 statistics and are used to calculate a 5-year average. An overall Kansas average and regional averages also are calculated and presented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds964","collaboration":"Prepared in cooperation with the Kansas Department of Agriculture’s Division of Water Resources","usgsCitation":"Lanning-Rush, J.L., and Eslick, P.J., 2015, Public-supply water use in Kansas, 2013: U.S. Geological Survey Data\nSeries 964, 46 p., https://dx.doi.org/10.3133/ds964.","productDescription":"Report: iii, 46 p.; 2 Appendixes","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2013-01-01","temporalEnd":"2013-12-31","ipdsId":"IP-067633","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":310291,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0964/coverthb.jpg"},{"id":310299,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/0964/downloads/appendix2-non_primary_mun.pdf","text":"Appendix 2","size":"33.8 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U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049
http://ks.water.usgs.gov
The Mariana Common Moorhen (Gallinula chloropus guami) is a highly endangered taxon, with fewer than 300 individuals estimated to occur in the wild. The subspecies is believed to have undergone population declines attributable to loss of wetland habitats on its native islands in the Mariana Islands. We analyzed mitochondrial DNA (mtDNA) sequences (control region and ND2 genes) and nuclear microsatellite loci in Mariana Common Moorhens from Guam and Saipan, the two most distal islands inhabited by the subspecies. Our analyses revealed similar nuclear genetic diversity and effective population size estimates on Saipan and Guam. Birds from Guam and Saipan were genetically differentiated (microsatellites: FST = 0.152; control region: FST = 0.736; ND2: FST= 0.390); however, assignment tests revealed the presence of first-generation dispersers from Guam onto Saipan (1 of 27 sampled birds) and from Saipan onto Guam (2 of 28 sampled birds), suggesting the capability for long-distance interpopulation movements within the subspecies. The observed dispersal rate was consistent with long-term estimates of effective numbers of migrants per generation between islands, indicating that movement between islands has been an ongoing process in this system. Despite known population declines, bottleneck tests revealed no signature of historical bottleneck events, suggesting that the magnitude of past population declines may have been comparatively small relative to the severity of declines that can be detected using genetic data.
","language":"English","publisher":"Cooper Ornithological Society","doi":"10.1650/CONDOR-15-42.1","usgsCitation":"Miller, M.P., Mullins, T.D., Haig, S.M., Takano, L.L., and Garcia, K., 2015, Genetic structure, diversity, and interisland dispersal in the endangered Mariana Common Moorhen (Gallinula chloropus guami): The Condor, v. 117, no. 4, p. 660-669, https://doi.org/10.1650/CONDOR-15-42.1.","productDescription":"10 p.","startPage":"660","endPage":"669","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064457","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":471701,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-15-42.1","text":"Publisher Index Page"},{"id":438670,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F6FFRD","text":"USGS data release","linkHelpText":"Mariana common moorhen (Gallinula chloropus guami) blood and tissue sample collection data, 2000-2001"},{"id":310672,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Guam, Saipan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 145.80711364746094,\n 15.294782590260944\n ],\n [\n 145.70823669433594,\n 15.221914134418109\n ],\n [\n 145.67733764648438,\n 15.117204423332618\n ],\n [\n 145.74600219726562,\n 15.084720636278325\n ],\n [\n 145.80299377441406,\n 15.166252139299058\n ],\n [\n 145.83938598632812,\n 15.275574270229807\n ],\n [\n 145.80711364746094,\n 15.294782590260944\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 144.84855651855466,\n 13.664002223865454\n ],\n [\n 144.788818359375,\n 13.523178603049868\n ],\n [\n 144.6954345703125,\n 13.481115555981754\n ],\n [\n 144.60548400878906,\n 13.452401604399896\n ],\n [\n 144.64393615722656,\n 13.400975000901058\n ],\n [\n 144.6233367919922,\n 13.341520159660119\n ],\n [\n 144.65904235839844,\n 13.257991471072014\n ],\n [\n 144.72564697265625,\n 13.239945499286312\n ],\n [\n 144.78057861328125,\n 13.294079395677374\n ],\n [\n 144.7949981689453,\n 13.40631853735722\n ],\n [\n 144.93301391601562,\n 13.518505297457194\n ],\n [\n 144.9721527099609,\n 13.610619236277133\n ],\n [\n 144.84855651855466,\n 13.664002223865454\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"117","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"563092bae4b093cee78203ca","contributors":{"authors":[{"text":"Miller, Mark P. 0000-0003-1045-1772 mpmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-1045-1772","contributorId":1967,"corporation":false,"usgs":true,"family":"Miller","given":"Mark","email":"mpmiller@usgs.gov","middleInitial":"P.","affiliations":[{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"preferred":true,"id":578316,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mullins, Thomas D. 0000-0001-8948-9604 tom_mullins@usgs.gov","orcid":"https://orcid.org/0000-0001-8948-9604","contributorId":3615,"corporation":false,"usgs":true,"family":"Mullins","given":"Thomas","email":"tom_mullins@usgs.gov","middleInitial":"D.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":578455,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haig, Susan M. 0000-0002-6616-7589 susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":578456,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Takano, Leilani L.","contributorId":149450,"corporation":false,"usgs":false,"family":"Takano","given":"Leilani","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":578457,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garcia, Karla","contributorId":149451,"corporation":false,"usgs":true,"family":"Garcia","given":"Karla","email":"","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":578458,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70155818,"text":"sir20155108 - 2015 - Flood-Inundation Maps for the North River in Colrain, Charlemont, and Shelburne, Massachusetts, From the Confluence of the East and West Branch North Rivers to the Deerfield River","interactions":[],"lastModifiedDate":"2019-12-30T14:31:00","indexId":"sir20155108","displayToPublicDate":"2015-10-27T12:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5108","title":"Flood-Inundation Maps for the North River in Colrain, Charlemont, and Shelburne, Massachusetts, From the Confluence of the East and West Branch North Rivers to the Deerfield River","docAbstract":"A series of 10 digital flood-inundation maps were developed for a 3.3-mile reach of the North River in Colrain, Charlemont, and Shelburne, Massachusetts, by the U.S. Geological Survey in cooperation with the Federal Emergency Management Agency. The coverage of the maps extends from the confluence of the East and West Branch North Rivers to the Deerfield River. Peak-flow estimates at the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities were computed for the reach from updated flood-frequency analyses. These peak flows were routed through a one-dimensional step-backwater hydraulic model to obtain the corresponding peak water-surface elevations and to place the tropical storm Irene flood of August 28, 2011, into historical context. The hydraulic model was calibrated by using the current [2015] stage-discharge relation at the U.S. Geological Survey streamgage North River at Shattuckville, MA (station number 01169000), and from documented high-water marks from the tropical storm Irene flood, which had a peak flow with approximately a 0.2-percent annual exceedance probability.
\nA hydraulic model was used to compute water-surface profiles for 10 flood stages referenced to the streamgage and ranging from 6.6 feet (ft; 464.5 ft North American Vertical Datum of 1988 [which is approximately bankfull]) to 18.3 ft (476.2 ft North American Vertical Datum of 1988 [which is the stage of the 0.2-percent annual exceedance probability peak flow and exceeds the maximum recorded water level at the streamgage and the National Weather Service major flood stage of 13.0 ft]. The mapped stages of 6.6 to 18.3 ft were selected to match the stages of flows for bankfull; the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities; and an incremental stage of 17.0 ft. The simulated water-surface profiles were combined with a geographic information system digital elevation model derived from light detection and ranging (lidar) data with a 0.5-ft vertical accuracy to create a set of flood-inundation maps.
\nThe availability of the flood-inundation maps, combined with information regarding near-real-time stage from the U.S. Geological Survey North River at Shattuckville, MA streamgage can provide emergency management personnel and residents with information that is critical for flood response activities, such as evacuations and road closures, and postflood recovery efforts. The flood-inundation maps are nonregulatory, but provide Federal, State, and local agencies and the public with estimates of the potential extent of flooding during selected peak-flow events. Introduction
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155108","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Bent, G.C., Lombard, P.J., and Dudley, R.W., 2015, Flood-inundation maps for the North River in Colrain, Charlemont, and Shelburne, Massachusetts, from the confluence of the East and West Branch North Rivers to the Deerfield River: U.S. Geological Survey Scientific Investigations Report 2015–5108, 16 p., appendixes, https://dx.doi.org/10.3133/sir20155108.","productDescription":"Report: v, 15 p.; Appendixes: 1-2; Application site; Metadata; Spacial data","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-061968","costCenters":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"links":[{"id":310349,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5108/sir20155108.pdf","text":"Report","size":"4.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5108"},{"id":310384,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2015/5108/attachments/sir20155108_flood-inundation-gis.zip","text":"Flood Inundation - GIS","size":"4.64 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2015-5108"},{"id":310385,"rank":7,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2015/5108/attachments/sir20155108_flood-inundation-gis-metadata.xml","text":"Flood Inundation - GIS Metadata (xml)","size":"12.5 KB","description":"SIR 2015-5108"},{"id":310386,"rank":8,"type":{"id":4,"text":"Application Site"},"url":"https://wimcloud.usgs.gov/apps/FIM/FloodInundationMapper.html","text":"Flood Inundation Mapper","linkFileType":{"id":5,"text":"html"}},{"id":310383,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5108/attachments/sir20155108_appendix2-shapefiles.zip","text":"Appendix 2 - Shapefiles","size":"31 KB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2015-5108"},{"id":310382,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2015/5108/attachments/sir20155108_appendix2-metadata.xml","text":"Appendix 2 - Metadata (xml)","size":"11.8 KB","description":"SIR 2015-5108"},{"id":310350,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5108/attachments/sir20155108_app1.xlsx","text":"Appendix 1","size":"13.4 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015-5108"},{"id":310629,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5108/images/coverthb.jpg"}],"country":"United States","state":"Massachusetts","city":"Colrain, Charlemont, Shelburne, Shattuckville","otherGeospatial":"North River, Deerfield River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.0810546875,\n 42.285437007491545\n ],\n [\n -72.421875,\n 42.285437007491545\n ],\n [\n -72.421875,\n 42.70665956351041\n ],\n [\n -73.0810546875,\n 42.70665956351041\n ],\n [\n -73.0810546875,\n 42.285437007491545\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Or visit our Web site at
http://newengland.water.usgs.gov/
Collecting physical bedload measurements is an expensive and time-consuming endeavor that rarely captures the spatial and temporal variability of sediment transport. Technological advances can improve monitoring of sediment transport by filling in temporal gaps between physical sampling periods. We have developed a low-cost hydrophone recording system designed to record the sediment-generated noise (SGN) resulting from collisions of coarse particles (generally larger than 4 mm) in gravel-bedded rivers. The sound level of the signal recorded by the hydrophone is assumed to be proportional to the magnitude of bedload transport as long as the acoustic frequency of the SGN is known, the grain-size distribution of the bedload is assumed constant, and the frequency band of the ambient noise is known and can be excluded from the analysis. Each system has two hydrophone heads and samples at half-hour intervals. Ten systems were deployed on the San Joaquin River, California, and its tributaries for ten months during water year 2014, and two systems were deployed during a flood event on the Gunnison River, Colorado in 2014. A mobile hydrophone system was also tested at both locations to collect longitudinal profiles of SGN. Physical samples of bedload were not collected in this study. In lieu of physical measurements, several audio recordings from each site were aurally reviewed to confirm the presence or absence of SGN, and hydraulic data were compared to historical measurements of bedload transport or transport capacity estimates to verify if hydraulic conditions during the study would likely produce bedload transport. At one site on the San Joaquin River, the threshold of movement was estimated to have occurred around 30 m 3 /s based on SGN data. During the Gunnison River flood event, continuous data showed clockwise hysteresis, indicating that bedload transport was generally less at any given streamflow discharge during the recession limb of the hydrograph. Spatial variability in transport was also detected in the longitudinal profiles audibly and using signal processing algorithms. These experiments demonstrate the ability of hydrophone technology to capture the temporal and spatial variability of sediment transport, which may be missed when samples are collected using conventional methods.
","conferenceTitle":"3rd Joint Federal Interagency Conference","conferenceDate":"April 19-23, 2015","conferenceLocation":"Reno, NV","language":"English","publisher":"Joint Federal Interagency Conference","usgsCitation":"Marineau, M.D., Minear, J., and Wright, S., 2015, Using hydrophones as a surrogate monitoring technique to detect temporal and spatial variability in bedload transport, 3rd Joint Federal Interagency Conference, Reno, NV, April 19-23, 2015, 12 p.","productDescription":"12 p.","ipdsId":"IP-060794","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":342079,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Colorado","otherGeospatial":"Gunnison River, San Joaquin River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.083333,\n 37.1\n ],\n [\n -120.083333,\n 36.683333\n ],\n [\n -119.683333,\n 36.683333\n ],\n [\n -119.683333,\n 37.1\n ],\n [\n -120.083333,\n 37.1\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.458333,\n 38.925\n ],\n [\n -108.25,\n 38.925\n ],\n [\n -108.25,\n 38.7\n ],\n [\n -108.458333,\n 38.7\n ],\n [\n -108.458333,\n 38.925\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59366daae4b0f6c2d0d7d62e","contributors":{"authors":[{"text":"Marineau, Mathieu D. 0000-0002-6568-0743 mmarineau@usgs.gov","orcid":"https://orcid.org/0000-0002-6568-0743","contributorId":4954,"corporation":false,"usgs":true,"family":"Marineau","given":"Mathieu","email":"mmarineau@usgs.gov","middleInitial":"D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":548731,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Minear, J. Toby","contributorId":9938,"corporation":false,"usgs":true,"family":"Minear","given":"J. Toby","affiliations":[],"preferred":false,"id":548732,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wright, Scott 0000-0002-0387-5713 sawright@usgs.gov","orcid":"https://orcid.org/0000-0002-0387-5713","contributorId":1536,"corporation":false,"usgs":true,"family":"Wright","given":"Scott","email":"sawright@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":548733,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70157439,"text":"sir20155139 - 2015 - Groundwater chemistry in the vicinity of the Puna Geothermal Venture Power Plant, Hawai‘i, after two decades of production","interactions":[],"lastModifiedDate":"2015-10-27T09:14:28","indexId":"sir20155139","displayToPublicDate":"2015-10-26T16:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5139","title":"Groundwater chemistry in the vicinity of the Puna Geothermal Venture Power Plant, Hawai‘i, after two decades of production","docAbstract":"We report chemical data for selected shallow wells and coastal springs that were sampled in 2014 to determine whether geothermal power production in the Puna area over the past two decades has affected the characteristics of regional groundwater. The samples were analyzed for major and minor chemical species, trace metals of environmental concern, stable isotopes of water, and two organic compounds (pentane and isopropanol) that are injected into the deep geothermal reservoir at the power plant. Isopropanol was not detected in any of the groundwaters; confirmed detection of pentane was restricted to one monitoring well near the power plant at a low concentration not indicative of source. Thus, neither organic compound linked geothermal operations to groundwater contamination, though chemical stability and transport velocity questions exist for both tracers. Based on our chemical analysis of geothermal fluid at the power plant and on many similar results from commercially analyzed samples, we could not show that geothermal constituents in the groundwaters we sampled came from the commercially developed reservoir. Our data are consistent with a long-held view that heat moves by conduction from the geothermal reservoir into shallow groundwaters through a zone of low permeability rock that blocks passage of geothermal water. The data do not rule out all impacts of geothermal production on groundwater. Removal of heat during production, for example, may be responsible for minor changes that have occurred in some groundwater over time, such as the decline in temperature of one monitoring well near the power plant. Such indirect impacts are much harder to assess, but point out the need for an ongoing groundwater monitoring program that should include the coastal springs down-gradient from the power plant.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155139","usgsCitation":"Evans, W., Bergfeld, D., Sutton, A., Lee, R., and Lorenson, T., 2015, Groundwater chemistry in the vicinity of the Puna Geothermal Venture Power Plant, Hawai‘i, after two decades of production: U.S. Geological Survey Scientific Investigations Report 2015-5139, v, 26 p., https://doi.org/10.3133/sir20155139.","productDescription":"v, 26 p.","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2014-01-01","temporalEnd":"2014-12-31","ipdsId":"IP-068405","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":310658,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5139/coverthb.jpg"},{"id":310659,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5139/sir20155139.pdf","text":"Report","size":"2.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5139"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -154.98310089111328,\n 19.34289288466279\n ],\n [\n -154.99408721923828,\n 19.51255069063782\n ],\n [\n -154.918212890625,\n 19.581463883128308\n ],\n [\n -154.8028564453125,\n 19.52484721904625\n ],\n [\n -154.81761932373047,\n 19.47533181665073\n ],\n [\n -154.98310089111328,\n 19.34289288466279\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NRP staff, National Research Program
U.S. Geological Survey
345 Middlefield Road, MS-435
Menlo Park, CA 94025
http://water.usgs.gov/nrp
A series of maps that describe the distribution and texture of sea-floor sediments and physiographic zones of Massachusetts State waters from Nahant to Salisbury, Massachusetts, including western Massachusetts Bay, have been produced by using high-resolution geophysical data (interferometric and multibeam swath bathymetry, lidar bathymetry, backscatter intensity, and seismic reflection profiles), sediment samples, and bottom photographs. These interpretations are intended to aid statewide efforts to inventory and manage coastal and marine resources, link with existing data interpretations, and provide information for research focused on coastal evolution and environmental change. Marine geologic mapping of the inner continental shelf of Massachusetts is a statewide cooperative effort of the U.S. Geological Survey and the Massachusetts Office of Coastal Zone Management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151153","collaboration":"Prepared in cooperation with the Massachusetts Office of Coastal Zone Management","usgsCitation":"Pendleton, E.A., Barnhardt, W.A., Baldwin, W.E., Foster, D.S., Schwab, W.C., Andrews, B.D., and Ackerman, S.D., 2015, Sea-floor texture and physiographic zones of the inner continental shelf from Salisbury to Nahant, Massachusetts, including the Merrimack Embayment and Western Massachusetts Bay: U.S. Geological Survey Open-File Report 2015–1153, 36 p., 1 appendix, https://dx.doi.org/10.3133/ofr20151153.","productDescription":"vi, 36 p.; HTML Documet","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-057824","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":309391,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2015/1153/index.html","text":"Report HTML","description":"OFR 2015-1153"},{"id":308684,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1153/ofr20151153.pdf","text":"Report","size":"2.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1153"},{"id":308683,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1153/images/coverthb.jpg"}],"country":"United States","state":"Massachusetts","city":"Nahant, Salisbury","otherGeospatial":"Massachusetts Bay, Merrimack Embayment","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.95794677734374,\n 42.407234661551875\n ],\n [\n -70.95794677734374,\n 42.84777884235988\n ],\n [\n -70.56106567382812,\n 42.84777884235988\n ],\n [\n -70.56106567382812,\n 42.407234661551875\n ],\n [\n -70.95794677734374,\n 42.407234661551875\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 0254
http://woodshole.er.usgs.gov/
Chambers Lake is a manmade reservoir on Birch Run, a tributary to West Branch Brandywine Creek in Chester County, Pennsylvania. The lake was created in 1994 after the completion of Multi-Purpose Dam PA-436F (Hibernia Dam), which was built under the Watershed Protection & Flood Control Prevention Act (U.S. Soil Conservation Service, 1991). Hibernia dam is 1,700 feet upstream from the confluence of Birch Run with West Branch Brandywine Creek. The primary objectives for Hibernia Dam were to provide (1) flood control, (2) a supplemental source of water supply for the greater City of Coatesville public water system, and (3) recreational opportunities. The drainage basin of Chambers Lake encompasses approximately 4.5 square miles, and the lake covers a surface area of about 95 acres at normal pool, which is at an elevation of 579.2 feet above the North American Vertical Datum of 1988 (NAVD 88) [580.0 feet above the National Geodetic Vertical Datum of 1929 (NGVD 29)]. The crest of the auxiliary spillway of the dam is 586.6 feet above NAVD 88. The elevation of the auxiliary spillway is important to this investigation because this elevation defines the flood storage capacity of Chambers Lake. Water levels exceeding this elevation are routed through the auxiliary spillway and flow through adjoining woodland to Birch Run.
\nThe U.S. Geological Survey (USGS), in cooperation with Chester County Water Resources Authority (CCWRA) and the County of Chester, surveyed the bathymetry and selected above-water features of Chambers Lake in September 2014. The purpose of the survey was to develop an accurate representation of the surface of the bottom of Chambers Lake and to determine the stage area and reservoir-storage capacity relation as of September 2014. CCWRA is responsible for operation of the dam and water-supply reservoir. Since construction, CCWRA has used a stage–storage capacity relation developed from the original survey conducted in the 1990s to estimate the volume of water available for water supply and the available flood storage. The bathymetric mapping effort was initiated due to interest in potential changes in current (2014) storage capacity when compared to the stage–storage capacity relation developed during design. The generated bathymetric surface may serve as a baseline to which temporal changes in storage capacity, owing to sedimentation and other factors, can be compared. In addition, these data will improve the overall accuracy of the stage–storage capacity table that CCWRA uses for reservoir and flood management operations.
\nThis report describes the methods used to create a bathymetric map of Chambers Lake for the computation of reservoir storage capacity as of September 2014. The product is a bathymetric map and a table showing the storage capacity of the reservoir at 2-foot increments from minimum usable elevation up to full capacity at the crest of the auxiliary spillway.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3346","collaboration":"Prepared in cooperation with the Chester County Water Resources Authority","usgsCitation":"Gyves, M.C., 2015, Bathymetry and capacity of Chambers Lake, Chester County, Pennsylvania: U.S. Geological Survey Scientific Investigations Map 3346, https://dx.doi.org/10.3133/sim3346.","productDescription":"1 Sheet: 38 x 36 inches ; Spatial Data","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-062894","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":310602,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3346/coverthb.jpg"},{"id":310604,"rank":3,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3346/sim3346_spatial_data.zip","text":"Spatial Data","size":"13.1 MB","description":"SIM 3346"},{"id":310603,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3346/sim3346.pdf","text":"Report","size":"1.71 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3346"}],"country":"United States","state":"Pennsylvania","county":"Chester County","otherGeospatial":"Chambers Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.86523056030273,\n 40.02781160777367\n ],\n [\n -75.86523056030273,\n 40.03622367578228\n ],\n [\n -75.84815025329588,\n 40.03622367578228\n ],\n [\n -75.84815025329588,\n 40.02781160777367\n ],\n [\n -75.86523056030273,\n 40.02781160777367\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Pennsylvania Water Science Center
215 Limekiln Road
New Cumberland, PA 17070
http://pa.water.usgs.gov/
Species distribution models (SDM) are used to help understand what drives the distribution of various plant and animal species. These models are typically high dimensional scalar functions, where the dimensions of the domain correspond to predictor variables of the model algorithm. Understanding and exploring the differences between models help ecologists understand areas where their data or understanding of the system is incomplete and will help guide further investigation in these regions. These differences can also indicate an important source of model to model uncertainty. However, it is cumbersome and often impractical to perform this analysis using existing tools, which allows for manual exploration of the models usually as 1-dimensional curves. In this paper, we propose a topology-based framework to help ecologists explore the differences in various SDMs directly in the high dimensional domain. In order to accomplish this, we introduce the concept of maximum topology matching that computes a locality-aware correspondence between similar extrema of two scalar functions. The matching is then used to compute the similarity between two functions. We also design a visualization interface that allows ecologists to explore SDMs using their topological features and to study the differences between pairs of models found using maximum topological matching. We demonstrate the utility of the proposed framework through several use cases using different data sets and report the feedback obtained from ecologists.
","largerWorkType":{"id":24,"text":"Conference Paper"},"conferenceTitle":"VIS 2015","conferenceDate":"October 25, 2015","language":"English","publisher":"IEEE","usgsCitation":"Poco, J., Doraiswamy, H., Talbert, M., Morisette, J., and Silva, C., 2015, Using maximum topology matching to explore differences in species distribution models, VIS 2015, October 25, 2015, 8 p.","productDescription":"8 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067412","costCenters":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"links":[{"id":324945,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":312847,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://homes.cs.washington.edu/~jpocom/pubs/topoMatching2015.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United 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Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Validation of meter-scale surface faulting offset measurements from high-resolution topographic data","docAbstract":"Studies of active fault zones have flourished with the availability of high-resolution topographic data, particularly where airborne light detection and ranging (lidar) and structure from motion (SfM) data sets provide a means to remotely analyze submeter-scale fault geomorphology. To determine surface offset at a point along a strike-slip earthquake rupture, geomorphic features (e.g., stream channels) are measured days to centuries after the event. Analysis of these and cumulatively offset features produces offset distributions for successive earthquakes that are used to understand earthquake rupture behavior. As researchers expand studies to more varied terrain types, climates, and vegetation regimes, there is an increasing need to standardize and uniformly validate measurements of tectonically displaced geomorphic features. A recently compiled catalog of nearly 5000 earthquake offsets across a range of measurement and reporting styles provides insight into quality rating and uncertainty trends from which we formulate best-practice and reporting recommendations for remote studies. In addition, a series of public and beginner-level studies validate the remote methodology for a number of tools and emphasize considerations to enhance measurement accuracy and precision for beginners and professionals. Our investigation revealed that (1) standardizing remote measurement methods and reporting quality rating schemes is essential for the utility and repeatability of fault-offset measurements; (2) measurement discrepancies often involve misinterpretation of the offset geomorphic feature and are a function of the investigator’s experience; (3) comparison of measurements made by a single investigator in different climatic regions reveals systematic differences in measurement uncertainties attributable to variation in feature preservation; (4) measuring more components of a displaced geomorphic landform produces more consistently repeatable estimates of offset; and (5) inadequate understanding of pre-event morphology and post-event modifications represents a greater epistemic limitation than the aleatoric limitations of the measurement process.
\n","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Geosphere","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Geological Society of America","publisherLocation":"Boulder, CO","doi":"10.1130/GES01197.1","usgsCitation":"Salisbury, B., Haddad, D., Rockwell, T.K., Arrowsmith, R., Madugo, C., Zielke, O., and Scharer, K.M., 2015, Validation of meter-scale surface faulting offset measurements from high-resolution topographic data: Geosphere, v. 6, no. 11, p. 1884-1901, https://doi.org/10.1130/GES01197.1.","productDescription":"18 p.","startPage":"1884","endPage":"1901","numberOfPages":"18","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064820","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":471707,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges01197.1","text":"Publisher Index Page"},{"id":318019,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-23","publicationStatus":"PW","scienceBaseUri":"56c304e0e4b0946c65208823","contributors":{"authors":[{"text":"Salisbury, Barrett","contributorId":166850,"corporation":false,"usgs":false,"family":"Salisbury","given":"Barrett","email":"","affiliations":[{"id":12431,"text":"ASU","active":true,"usgs":false}],"preferred":false,"id":620206,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haddad, D.E.","contributorId":45545,"corporation":false,"usgs":true,"family":"Haddad","given":"D.E.","email":"","affiliations":[],"preferred":false,"id":620207,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rockwell, T. K.","contributorId":34688,"corporation":false,"usgs":false,"family":"Rockwell","given":"T.","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":620208,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arrowsmith, R.","contributorId":166851,"corporation":false,"usgs":false,"family":"Arrowsmith","given":"R.","email":"","affiliations":[{"id":12431,"text":"ASU","active":true,"usgs":false}],"preferred":false,"id":620209,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Madugo, C.","contributorId":166852,"corporation":false,"usgs":false,"family":"Madugo","given":"C.","email":"","affiliations":[{"id":24560,"text":"PGE","active":true,"usgs":false}],"preferred":false,"id":620210,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zielke, O.","contributorId":166853,"corporation":false,"usgs":false,"family":"Zielke","given":"O.","affiliations":[{"id":24561,"text":"KAUST","active":true,"usgs":false}],"preferred":false,"id":620211,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Scharer, Katherine M. 0000-0003-2811-2496 kscharer@usgs.gov","orcid":"https://orcid.org/0000-0003-2811-2496","contributorId":3385,"corporation":false,"usgs":true,"family":"Scharer","given":"Katherine","email":"kscharer@usgs.gov","middleInitial":"M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":620205,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70157183,"text":"sir20155127 - 2015 - Characteristics of sediment transport at selected sites along the Missouri River, 2011–12","interactions":[],"lastModifiedDate":"2015-10-22T15:08:55","indexId":"sir20155127","displayToPublicDate":"2015-10-22T15:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5127","title":"Characteristics of sediment transport at selected sites along the Missouri River, 2011–12","docAbstract":"
Extreme flooding in the Missouri River in 2011, followed by a year of more typical streamflows in 2012, allowed the sediment-transport regime to be compared between the unprecedented conditions of 2011 and the year immediately following the flooding. As part of a cooperative effort between the U.S. Geological Survey and the U.S. Army Corps of Engineers, this report follows up U.S. Geological Survey Scientific Investigations Report 2013–5006 by comparing sediment transport between years and among sampling sites spanning the Garrison Segment in North Dakota, the Gavins Point Segment downstream from Lewis and Clark Lake, and a part of the Channelized Segment along the Nebraska-Iowa border. Suspended sediment, bed material, bedload, and streamflow data from June 2011 through November 2012 were designated as “measured” total loads, wash loads, and bed-material loads; and, alternatively, were applied to the Modified-Einstein Procedure to compute sediment loads that were designated as “estimated” total loads.
\nBeyond the expected result that sediment loads were much lower during typical streamflows than those measured during the flooding, the measured data indicated some localized sediment-transport processes for further examination. Extreme and prolonged flooding can temporarily deplete sediment supplies locally, and evidence indicating such depletion was present at some sites. Unexpectedly high bed-material loads in the Gavins Point Segment may reflect episodic bar erosion just upstream from the sampling site. The relative contribution of bedload was typically 10 percent or less of the total load during the flooding. Following the flooding, this relative amount increased at some sites but not others, the reasons for which are possibly related to differences in stream velocity. Ultimately, the bedload decreased as it entered the Channelized Segment because of increased velocity and the turbulent mixing ability of the river as compared to the Gavins Point Segment. This turbulent mixing may also convert bed-material load into wash load, thereby rendering those sediments unavailable for creating sandbars and other bedforms. Though some of the sampling data support this premise, it was not consistently manifested by differences between the sediment load of the two segments during typical-streamflow conditions.
\nThe Modified-Einstein Procedure tended to predict greater total-sediment loads when compared to measured values. These differences may be the result of sediment deficits in the Missouri River that lead to an overprediction by the Modified-Einstein Procedure, the unsampled zone above the streambed that leads to an underprediction by the suspended sampler, or general uncertainty in the sampling approach. The differences between total-sediment load obtained through measurements and that estimated from applied theoretical procedures such as the Modified-Einstein Procedure pose a challenge for reliably characterizing total-sediment transport. Though it is not clear which of the two techniques is more accurate, the general tendency of the two to be within an order of magnitude of one another may be adequate for many sediment studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155127","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Omaha District","usgsCitation":"Rus, D.L., Galloway, J.M., and Alexander, J.S., 2015, Characteristics of sediment transport at selected sites along the Missouri River, 2011–12: U.S. Geological Survey Scientific Investigations Report 2015–5127, 34 p., https://dx.doi.org/10.3133/sir20155127.","productDescription":"v, 34 p.","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2011-01-01","temporalEnd":"2012-12-31","ipdsId":"IP-055952","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":310206,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5127/coverthb.jpg"},{"id":310207,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5127/sir20155127.pdf","text":"Report","size":"1.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5127"}],"country":"United States","state":"Iowa, Kansas, Missouri, Nebraska, North Dakota, South Dakota","otherGeospatial":"Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.087890625,\n 38.685509760012\n ],\n [\n -93.42773437499999,\n 40.613952441166596\n ],\n [\n -95.2734375,\n 43.51668853502909\n ],\n [\n -97.18505859374999,\n 45.90529985724796\n ],\n [\n -101.162109375,\n 47.90161354142077\n ],\n [\n -102.9638671875,\n 47.79839667295524\n ],\n [\n -101.62353515625,\n 43.992814500489914\n ],\n [\n -97.53662109375,\n 41.77131167976407\n ],\n [\n -94.9658203125,\n 38.5825261593533\n ],\n [\n -92.08740234375,\n 38.16911413556086\n ],\n [\n -90,\n 38.444984668894705\n ],\n [\n -90.087890625,\n 38.685509760012\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, Nebraska 68512
http://ne.water.usgs.gov/
The availability of land cover data at local scales is an important component in forest management and monitoring efforts. Regional land cover data seldom provide detailed information needed to support local management needs. Here we present a transferable framework to model forest cover by major plant functional type using aerial photos, multi-date Système Pour l’Observation de la Terre (SPOT) imagery, and topographic variables. We developed probability of occurrence models for deciduous broad-leaved forest and needle-leaved evergreen forest using logistic regression in the southern portion of the Wyoming Basin Ecoregion. The model outputs were combined into a synthesis map depicting deciduous and coniferous forest cover type. We evaluated the models and synthesis map using a field-validated, independent data source. Results showed strong relationships between forest cover and model variables, and the synthesis map was accurate with an overall correct classification rate of 0.87 and Cohen’s kappa value of 0.81. The results suggest our method adequately captures the functional type, size, and distribution pattern of forest cover in a spatially heterogeneous landscape.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/2150704X.2015.1072289","usgsCitation":"Assal, T.J., Anderson, P.J., and Sibold, J., 2015, Mapping forest functional type in a forest-shrubland ecotone using SPOT imagery and predictive habitat distribution modelling: Remote Sensing Letters, v. 6, no. 10, p. 755-764, https://doi.org/10.1080/2150704X.2015.1072289.","productDescription":"10 p.","startPage":"755","endPage":"764","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066673","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":310373,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Utah, Wyoming","otherGeospatial":"Wyoming Basin Ecoregion","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.8740234375,\n 40.052847601823984\n ],\n [\n -110.8740234375,\n 41.9921602333763\n ],\n [\n -107.830810546875,\n 41.9921602333763\n ],\n [\n -107.830810546875,\n 40.052847601823984\n ],\n [\n -110.8740234375,\n 40.052847601823984\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","issue":"10","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-21","publicationStatus":"PW","scienceBaseUri":"5629faa5e4b011227bf1fd18","contributors":{"authors":[{"text":"Assal, Timothy J. 0000-0001-6342-2954 assalt@usgs.gov","orcid":"https://orcid.org/0000-0001-6342-2954","contributorId":2203,"corporation":false,"usgs":true,"family":"Assal","given":"Timothy","email":"assalt@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":577979,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Patrick J. 0000-0003-2281-389X andersonpj@usgs.gov","orcid":"https://orcid.org/0000-0003-2281-389X","contributorId":3590,"corporation":false,"usgs":true,"family":"Anderson","given":"Patrick","email":"andersonpj@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":577980,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sibold, Jason","contributorId":10724,"corporation":false,"usgs":false,"family":"Sibold","given":"Jason","affiliations":[],"preferred":false,"id":577981,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70157499,"text":"ds965 - 2015 - Topographic and hydrographic survey data for the São Francisco River near Torrinha, Bahia, Brazil, 2014","interactions":[],"lastModifiedDate":"2015-10-22T08:25:52","indexId":"ds965","displayToPublicDate":"2015-10-21T16:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"965","title":"Topographic and hydrographic survey data for the São Francisco River near Torrinha, Bahia, Brazil, 2014","docAbstract":"Navigable inland waterways, including lakes, rivers, and reservoirs, are important transportation routes for people and goods in Brazil. Natural and anthropogenic effects coupled with recent severe droughts have led to decreased inland waterway navigation. The Company for Development of the São Francisco and Parnaíba River Valleys (CODEVASF) has recognized the decrease in waterway navigation and is investing resources to help restore selected reaches of the São Francisco River for navigation. In 2011, CODEVASF signed an agreement with the U.S. Army Corps of Engineers (USACE) seeking technical assistance and engineering expertise in waterway navigation and bank stabilization. The Torrinha-Itacoatiara study reach near Torrinha, Bahia was 1 of 12 conceptual waterway navigation improvement feasibility studies and was the focus of this study. The U.S. Geological Survey, in cooperation with the USACE and CODEVASF, collected topographic and hydrographic data from May 22 to June 12, 2014, to provide baseline data for supporting computational streamflow models.
\nThis report presents the surveying techniques and data-processing methods used to collect, process, and disseminate topographic and hydrographic data. All standard and non‑standard data-collection methods, techniques, and data process methods were documented. Additional discussion describes the quality-assurance and quality-control elements used in this study, along with the limitations for the Torrinha-Itacoatiara study reach data. The topographic and hydrographic geospatial data are published along with associated metadata.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds965","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers and the Company for Development of the São Francisco and Parnaiba River Valleys","usgsCitation":"Fosness, R.L., and Dietsch, B.J., 2015, Topographic and hydrographic survey data for the São Francisco River near Torrinha, Bahia, Brazil, 2014: U.S. Geological Survey Data Series 965, 28 p., https://dx.doi.org/10.3133/ds965.","productDescription":"Report: vi, 28 p.; GIS Datasets","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2014-05-22","temporalEnd":"2014-06-12","ipdsId":"IP-063788","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":310182,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0965/coverthb.jpg"},{"id":310183,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0965/ds0965.pdf","text":"Report","size":"7.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 965 PDF"},{"id":310308,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/ds/0965/ds0965_table3.html","text":"GIS Datasets","linkFileType":{"id":5,"text":"html"},"description":"GIS Datasets","linkHelpText":"Metadata, preview illustrations, and compressed geospatial data sets for the Torrinha-Itacoatiara feasibility study, São Francisco River near Torrinha, Bahia, Brazil, 2014."}],"country":"Brazil","state":"Bahia","city":"Torrinha","otherGeospatial":"São Francisco River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -43.385009765625,\n -11.549998444541838\n ],\n [\n -43.385009765625,\n -10.992423823549997\n ],\n [\n -42.9290771484375,\n -10.992423823549997\n ],\n [\n -42.9290771484375,\n -11.549998444541838\n ],\n [\n -43.385009765625,\n -11.549998444541838\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
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The Albuquerque Basin, located in central New Mexico, is about 100 miles long and 25–40 miles wide. The basin is hydrologically defined as the extent of consolidated and unconsolidated deposits of Tertiary and Quaternary age that encompasses the structural Rio Grande Rift. Drinking-water supplies throughout the basin were obtained solely from groundwater resources until December 2008, when treatment and distribution of surface water from the Rio Grande through the San Juan-Chama Drinking Water Project began. A 20-percent population increase in the basin from 1990 to 2000 and a 22-percent population increase from 2000 to 2010 resulted in an increased demand for water.
An initial network of wells was established by the U.S. Geological Survey (USGS) in cooperation with the City of Albuquerque from April 1982 through September 1983 to monitor changes in groundwater levels throughout the basin. This network consisted of 6 wells with analog-to-digital recorders and 27 wells where water levels were measured monthly in 1983. The network currently (2014) consists of 125 wells and piezometers. (A piezometer is a specialized well open to a specific depth in the aquifer, often of small diameter and nested with other piezometers open to different depths.) The USGS, in cooperation with the Albuquerque Bernalillo County Water Utility Authority, currently (2014) measures and reports water levels from the 125 wells and piezometers in the network; this report presents water-level data collected by USGS personnel at those 125 sites through water year 2014 (October 1, 2013, to September 30, 2014).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds963","collaboration":"Prepared in cooperation with the Albuquerque Bernalillo County Water Utility Authority","usgsCitation":"Beman, J.E., 2015, Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2014 (ver. 1.1, August 2021): U.S. Geological Survey Data Series 963, 42 p., https://doi.org/10.3133/ds963.","productDescription":"iii, 42 p.","numberOfPages":"49","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-063333","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":388362,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0963/ds963.pdf","text":"Report","size":"5.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 963"},{"id":388363,"rank":2,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/ds/0963/versionHist.txt","text":"Version History","size":"535 B","linkFileType":{"id":2,"text":"txt"},"description":"DS 963 Version History"},{"id":388485,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0963/coverthb2.jpg"}],"country":"United States","state":"New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.03955078125,\n 34.252676117101515\n ],\n [\n -108.03955078125,\n 36.20882309283712\n ],\n [\n -106.23779296875,\n 36.20882309283712\n ],\n [\n -106.23779296875,\n 34.252676117101515\n ],\n [\n -108.03955078125,\n 34.252676117101515\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: August 2021","contact":"Director, New Mexico Water Science Center
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","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2015-10-21","revisedDate":"2021-08-25","noUsgsAuthors":false,"publicationDate":"2015-10-21","publicationStatus":"PW","scienceBaseUri":"5628a91ee4b0d158f5926bf9","contributors":{"authors":[{"text":"Beman, Joseph E. 0000-0002-0689-029X jebeman@usgs.gov","orcid":"https://orcid.org/0000-0002-0689-029X","contributorId":2619,"corporation":false,"usgs":true,"family":"Beman","given":"Joseph","email":"jebeman@usgs.gov","middleInitial":"E.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":572772,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70157556,"text":"tm11B7 - 2015 - 1-Meter Digital Elevation Model specification","interactions":[],"lastModifiedDate":"2015-10-22T09:41:01","indexId":"tm11B7","displayToPublicDate":"2015-10-21T09:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"11-B7","title":"1-Meter Digital Elevation Model specification","docAbstract":"
In January 2015, the U.S. Geological Survey National Geospatial Technical Operations Center began producing the 1-Meter Digital Elevation Model data product. This new product was developed to provide high resolution bare-earth digital elevation models from light detection and ranging (lidar) elevation data and other elevation data collected over the conterminous United States (lower 48 States), Hawaii, and potentially Alaska and the U.S. territories. The 1-Meter Digital Elevation Model consists of hydroflattened, topographic bare-earth raster digital elevation models, with a 1-meter x 1-meter cell size, and is available in 10,000-meter x 10,000-meter square blocks with a 6-meter overlap. This report details the specifications required for the production of the 1-Meter Digital Elevation Model.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: U.S. Geological Survey Standards in Book 11: Collection and Delineation of Spatial Data","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm11B7","usgsCitation":"Arundel, S.T., Archuleta, C.M., Phillips, L.A., Roche, B.L., and Constance, E.W., 2015, 1-meter digital elevation model specification: U.S. Geological Survey Techniques and Methods, book 11, chap. B7, 25 p. with appendixes, https://dx.doi.org/10.3133/tm11B7.","productDescription":"vi, 25 p.","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-066922","costCenters":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"links":[{"id":310105,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/11/b07/coverthb.jpg"},{"id":310106,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/11/b07/tm11-b7.pdf","text":"Report","size":"2.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 11–B7"}],"publicComments":"This report is Chapter 7 of Section B: U.S. Geological Survey Standards in Book 11: Collection and Delineation of Spatial Data","contact":"Director, National Geospatial Technical Operations Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401–2602
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Heavy rainfall occurred across South Carolina during October 1–5, 2015, as a result of an upper atmospheric low-pressure system that funneled tropical moisture from Hurricane Joaquin into the State. The storm caused major flooding from the central to the coastal areas of South Carolina. Almost 27 inches of rain fell near Mount Pleasant in Charleston County during this period. U.S. Geological Survey streamgages recorded peaks of record at 17 locations, and 15 other locations had peaks that ranked in the top 5 for the period of record. During the October 2015 flood event, U.S. Geological Survey personnel made about 140 streamflow measurements at 86 locations to verify, update, or extend existing rating curves, which are used to compute streamflow from monitored river stage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151201","usgsCitation":"Feaster, T.D., Shelton, J.M., and Robbins, J.C., 2015, Preliminary peak stage and streamflow data at selected USGS streamgaging stations for the South Carolina flood of October 2015 (ver. 1.1, November 2015): U.S. Geological Survey Open-File Report 2015–1201, 19 p., https://dx.doi.org/10.3133/ofr20151201.","productDescription":"iv, 19 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-069972","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":311080,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2015/1201/versionHist.txt","text":"Open-File Report 2015-1201, Version 1.1","size":"1.11 KB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2015-1201"},{"id":310064,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1201/ofr20151201.pdf","text":"Report","size":"2.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1201"},{"id":310063,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1201/coverthb1.jpg"}],"country":"United States","state":"South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.50830078125,\n 33.8521697014074\n ],\n [\n -79.683837890625,\n 34.813803317113155\n ],\n [\n -80.67260742187499,\n 34.813803317113155\n ],\n [\n -80.91430664062499,\n 35.10193405724606\n ],\n [\n -81.0791015625,\n 35.18278813800229\n ],\n [\n -82.41943359375,\n 35.24561909420681\n ],\n [\n -83.111572265625,\n 35.038992046780784\n ],\n [\n -83.29833984375,\n 34.90395296559004\n ],\n [\n -83.397216796875,\n 34.74161249883172\n ],\n [\n -83.001708984375,\n 34.46127728843708\n ],\n [\n -82.913818359375,\n 34.488447837809304\n ],\n [\n -82.781982421875,\n 34.31621838080741\n ],\n [\n -82.7490234375,\n 34.098159345215535\n ],\n [\n -82.584228515625,\n 33.90689555128866\n ],\n [\n -82.254638671875,\n 33.75174787568194\n ],\n [\n -82.2216796875,\n 33.58716733904656\n ],\n [\n -82.012939453125,\n 33.486435450999885\n ],\n [\n -81.968994140625,\n 33.348884792201694\n ],\n [\n -81.76025390625,\n 33.119150226768866\n ],\n [\n -81.529541015625,\n 33.03629817885956\n ],\n [\n -81.485595703125,\n 32.7872745269555\n ],\n [\n -81.463623046875,\n 32.59310597426537\n ],\n [\n -81.298828125,\n 32.50049648924482\n ],\n [\n -81.14501953125,\n 32.35212281198644\n ],\n [\n -81.1669921875,\n 32.18491105051798\n ],\n [\n -80.92529296875,\n 31.924192605327708\n ],\n [\n -80.628662109375,\n 32.287132632616355\n ],\n [\n -80.452880859375,\n 32.31499127724556\n ],\n [\n -80.364990234375,\n 32.41706632846282\n ],\n [\n -80.1123046875,\n 32.59310597426537\n ],\n [\n -79.991455078125,\n 32.602361666817515\n ],\n [\n -79.881591796875,\n 32.713355353177555\n ],\n [\n -79.73876953125,\n 32.82421110161336\n ],\n [\n -79.376220703125,\n 32.99945000822839\n ],\n [\n -79.1455078125,\n 33.211116472416855\n ],\n [\n -79.12353515625,\n 33.367237465838315\n ],\n [\n -78.958740234375,\n 33.568861182555565\n ],\n [\n -78.804931640625,\n 33.715201644740844\n ],\n [\n -78.64013671875,\n 33.815666308702774\n ],\n [\n -78.50830078125,\n 33.8521697014074\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1: Originally posted October 20, 2015; Version 1.1: November 9, 2015","contact":"Director, South Atlantic Water Science Center
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Wyoming has led the nation as the producer of uranium ore since 1995 and contains the largest reserves of any state. Approximately one third of Wyoming’s total production came from deposits in, or immediately adjacent to, the Wyoming Landscape Conservation Initiative (WLCI) study area in the southwestern corner of the state including all of Carbon, Lincoln, Sublette, Sweetwater, Uinta, and parts of southern Fremont Counties. Conventional open-pit and underground mining methods were employed in the study area until the early 1990s. Since the early 1990s, all uranium mining has been by in-situ recovery (also called in-situ leach). It is estimated that statewide remaining resources of 141,000 tonnes of uranium are about twice the 84,000 tonnes of uranium that the state has already produced.
\nAn evaluation of the mineral commodities present in the WLCI study area that may have a role in the development of southwest Wyoming includes uranium. The WLCI study area contains five uranium mineralized areas: Ketchum Buttes, Poison Basin, Shirley Basin, the southern part of Crooks Gap–Green Mountain, and most of Great Divide Basin. Mineralized areas described in the report and outlined on an accompanying map are based on the presence of either contiguous claim blocks, continuous mineralization adjacent to prospective uranium properties, suggestions of mineralization based on site entries in the U.S. Geological Survey’s Mineral Resources Data System (MRDS), or extension of geologic host units or structures. Mineralized areas are not the same as mining districts: the latter have defined administrative boundaries.
\nIn the WLCI study area, all uranium areas except Poison Basin and Ketchum Buttes contain roll-front deposits in Eocene (56–34 Ma) sedimentary rocks. Tabular sandstone-hosted uranium deposits are also recognized within the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141123","usgsCitation":"Wilson, A.B., 2015, Uranium in the Wyoming Landscape Conservation Initiative Study Area, southwestern Wyoming: U.S. Geological Survey Open-File Report 2014–1123, 33 p., 1 plate, https://dx.doi.org/10.3133/ofr20141123.","productDescription":"Report: iv, 33 p.; Plate: 35 x 28 inches; Appendixes A-B","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-040674","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":309981,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2014/1123/coverthb.jpg"},{"id":309986,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2014/1123/pdf/AppendixB_MRDS_Database_User_Manual_Explanation.pdf","text":"Appendix B","size":"367 kB","description":"OFR 2014-1123 Appendix B","linkHelpText":"Director, Central Mineral and Environmental Resources Science Center
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Box 25046, MS–973
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From 2000 to 2011, the U.S. Geological Survey conducted 139 quantitative assessments of continuous (unconventional) oil and gas accumulations within the United States. This report documents those assessments more fully than previously done by providing detailed documentation of both the assessment input and output. This report also compiles the data into spreadsheet tables that can be more readily used to provide analogs for future assessments, especially for hypothetical continuous accumulations.
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States\"}}]}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver Federal Center
Denver, CO 80225-0046
http://energy.usgs.gov/
Earth materials and structures in the El Casco quadrangle provide considerable information about the late Cenozoic geologic evolution of southern California’s Inland Empire region (fig. 2). Important structural and stratigraphic elements include (1) modern traces of the right-lateral San Jacinto Fault zone, (2) older traces of the San Jacinto Fault zone, and (3) sedimentary materials and geologic structures that formed during the last eight million years or so and that record interactions within the San Andreas Fault system. These materials, and the structures that deform them, provide a geologic context 3 for investigations of groundwater recharge and subsurface flow (Waring, 1919; Burnham and Dutcher, 1960; Bloyd, 1971; Rewis and others, 2006).
\nThis geologic database of the El Casco 7.5′ quadrangle was prepared by the Basins and Landscape Co-Evolution Project (BALANCE), a regional geologic-mapping project sponsored jointly by the U.S. Geological Survey and the California Geological Survey. The database was developed as a contribution to the National Cooperative Geologic Mapping Program’s National Geologic Map Database, and provides a general geologic setting of the El Casco quadrangle. The database and map provide information about earth materials and geologic structures, including faults and folds that have developed in the quadrangle due to complexities in the San Andreas Fault system.
\nGeologic information contained in the El Casco database is general-purpose data applicable to land-related investigations in the earth and biological sciences. The term “general-purpose” means that all geologic-feature classes have minimal information content adequate to characterize their general geologic characteristics and to interpret their general geologic history. However, no single feature class has enough information to definitively characterize its properties and origin. For this reason the database cannot be used for site-specific geologic evaluations, although it can be used to plan and guide investigations at the site-specific level.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20101274","usgsCitation":"Matti, J.C., Morton, D.M., and Langenheim, V., 2015, Geologic and geophysical maps of the El Casco 7.5′ quadrangle, Riverside County, southern California, with accompanying geologic-map database: U.S. Geological Survey Open-File Report 2010-1274, Report: vi, 141; 3 Sheets: 46.77 x 36.00 inches or smaller; Dataset; Metadata; Read Me, https://doi.org/10.3133/ofr20101274.","productDescription":"Report: vi, 141; 3 Sheets: 46.77 x 36.00 inches or smaller; Dataset; Metadata; Read Me","numberOfPages":"147","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-021187","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":308467,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2010/1274/ofr20101274_pamphlet.pdf","text":"Pamphlet","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2010-1274 Pamphlet"},{"id":308466,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2010/1274/coverthb.jpg"},{"id":308468,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2010/1274/ofr20101274_sheet1.pdf","text":"Sheet 1","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2010-1274 Sheet 1","linkHelpText":"Plot file of the geologic map of the El Casco 7.5' quadrangle"},{"id":308472,"rank":7,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2010/1274/ofr20101274_metadata.txt","text":"Metadata","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2010-1274 Metadata"},{"id":308471,"rank":6,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/of/2010/1274/ofr20101274_readme.txt","text":"Read Me","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2010-1274 Read Me"},{"id":399006,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_103546.htm"},{"id":308470,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2010/1274/ofr20101274_sheet3.pdf","text":"Sheet 3","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2010-1274 Sheet 3","linkHelpText":"Plot file of the gravity map"},{"id":308469,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2010/1274/ofr20101274_sheet2.pdf","text":"Sheet 2","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2010-1274 Sheet 2","linkHelpText":"Plot file of observation data for the El Casco 7.5' quadrangle"},{"id":308473,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/of/2010/1274/ofr20101274_data.zip","text":"Data","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2010-1274 Data"}],"scale":"24000","country":"United States","state":"California","county":"Riverside County","otherGeospatial":"El Casco 7.5' quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.125,\n 33.875\n ],\n [\n -117,\n 33.875\n ],\n [\n -117,\n 34\n ],\n [\n -117.125,\n 34\n ],\n [\n -117.125,\n 33.875\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"GMEG staff, Geology, Minerals, Energy, & Geophysics Science Center—Tucson
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http://geomaps.wr.usgs.gov/gmeg/
Trace-metal concentrations in sediment and in the clam Macoma petalum (formerly reported as Macoma balthica), clam reproductive activity, and benthic macroinvertebrate community structure were investigated in a mudflat 1 kilometer (km) south of the discharge of the Palo Alto Regional Water Quality Control Plant (PARWQCP) in South San Francisco Bay, Calif. This report includes the data collected by U.S. Geological Survey (USGS) scientists for the period January 2014 to December 2014. These append to long-term datasets extending back to 1974, and serve as the basis for the City of Palo Alto’s Near-Field Receiving Water Monitoring Program, initiated in 1994.
\nFollowing significant reductions in the late 1980s, silver (Ag) and copper (Cu) concentrations in sediment and M. petalum appear to have stabilized. Data for other metals, including chromium (Cr), mercury (Hg), nickel (Ni), selenium (Se), and zinc (Zn), have been collected since 1994. Over this period, concentrations of these elements have remained relatively constant, aside from seasonal variation that is common to all elements. In 2014, concentrations of Ag and Cu in M. petalum varied seasonally in response to a combination of site-specific metal exposures and annual growth and reproduction, as reported previously. Seasonal patterns for other elements, including Cr, Ni, Zn, Hg, and Se, were generally similar in timing and magnitude as those for Ag and Cu. In M. petalum, all observed elements showed annual maxima in January–February and minima in April, except for Zn, which was lowest in December. In sediments, annual maxima also occurred in January–February, and minima were measured in June and September. In 2014, metal concentrations in both sediments and clam tissue were among the lowest on record. This record suggests that regional-scale factors now largely control sedimentary and bioavailable concentrations of Ag and Cu, as well as other elements of regulatory interest, at the Palo Alto site.
\nAnalyses of the benthic community structure of a mudflat in South San Francisco Bay over a 40-year period show that changes in the community have occurred concurrent with reduced concentrations of metals in the sediment and in the tissues of the biosentinel clam, M. petalum, from the same area. Analysis of M. petalum shows increases in reproductive activity concurrent with the decline in metal concentrations in the tissues of this organism. Reproductive activity is presently stable (2014), with almost all animals initiating reproduction in the fall and spawning the following spring. The entire infaunal community has shifted from being dominated by several opportunistic species to a community where the species are more similar in abundance, a pattern that indicates a more stable community that is subjected to fewer stressors. In addition, two of the opportunistic species (Ampelisca abdita and Streblospio benedicti) that brood their young and live on the surface of the sediment in tubes have shown a continual decline in dominance coincident with the decline in metals; both species had short-lived rebounds in abundance in 2008, 2009, and 2010. Heteromastus filiformis (a subsurface polychaete worm that lives in the sediment, consumes sediment and organic particles residing in the sediment, and reproduces by laying its eggs on or in the sediment) showed a concurrent increase in dominance and, in the last several years before 2008, showed a stable population. H. filiformis abundance increased slightly in 2011–2012 and returned to pre-2011 numbers in 2014. An unidentified disturbance occurred on the mudflat in early 2008 that resulted in the loss of the benthic animals, except for deep-dwelling animals like Macoma petalum. However, within two months of this event animals returned to the mudflat. The resilience of the community suggested that the disturbance was not due to a persistent toxin or to anoxia. The reproductive mode of most species present in 2014 is reflective of species that were available either as pelagic larvae or as mobile adults. Although oviparous species were lower in number in this group, the authors hypothesize that these species will return slowly as more species move back into the area. The use of functional ecology was highlighted in the 2014 benthic community data, which showed that the animals that have now returned to the mudflat are those that can respond successfully to a physical, nontoxic disturbance. Today, community data show a mix of species that consume the sediment, or filter feed, have pelagic larvae that must survive landing on the sediment, and those that brood their young. USGS scientists view the 2008 disturbance event as a response by the infaunal community to an episodic natural stressor (possibly sediment accretion or a pulse of freshwater), in contrast to the long-term recovery from metal contamination. We will compare this recovery to the long-term recovery observed after the 1970’s when the decline in sediment pollutants was the dominating factor.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151199","usgsCitation":"Cain, D.J., Thompson, J.K., Crauder, J., Parcheso, F., Stewart, A.R., Kleckner, A.E., Dyke, J., Hornberger, M.I., and Luoma, S.N., 2015, Near-field receiving water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California: 2014: U.S. Geological Survey Open-File Report 2015-1199, viii, 79 p., https://doi.org/10.3133/ofr20151199.","productDescription":"viii, 79 p.","numberOfPages":"89","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2014-01-01","temporalEnd":"2014-12-31","ipdsId":"IP-068498","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":310004,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1199/ofr20151199.pdf","text":"Report","size":"9.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1199"},{"id":310003,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1199/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.11578369140626,\n 37.43493087364719\n ],\n [\n -122.11578369140626,\n 37.46123344639866\n ],\n [\n -122.09020614624023,\n 37.46123344639866\n ],\n [\n -122.09020614624023,\n 37.43493087364719\n ],\n [\n -122.11578369140626,\n 37.43493087364719\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NRP staff
National Research Program
U.S. Geological Survey
345 Middlefield Road, MS-435
Menlo Park, CA 94025
http://water.usgs.gov/nrp/
Water quality data collected in 2012 for 10 above- and 14 below-drainage coal mine discharges (CMDs), classified by mining or excavation method, in the anthracite region of Pennsylvania, USA, are compared with data for 1975, 1991, and 1999 to evaluate long-term (37 year) changes in pH, SO42−, and Fe concentrations related to geochemistry, hydrology, and natural attenuation processes. We hypothesized that CMD quality will improve over time because of diminishing quantities of unweathered pyrite, decreased access of O2 to the subsurface after mine closure, decreased rates of acid production, and relatively constant influx of alkalinity from groundwater. Discharges from shafts, slopes, and boreholes, which are vertical or steeply sloping excavations, are classified as below-drainage; these receive groundwater inputs with low dissolved O2, resulting in limited pyrite oxidation, dilution, and gradual improvement of CMD water quality. In contrast, discharges from drifts and tunnels, which are nearly horizontal excavations into hillsides, are classified as above-drainage; these would exhibit less improvement in water quality over time because the rock surfaces continue to be exposed to air, which facilitates sustained pyrite oxidation, acid production, and alkalinity consumption. Nonparametric Wilcoxon matched-pair signed rank tests between 1975 and 2012 samples indicate decreases in Fe and SO42− concentrations were highly significant (p < 0.05) and increases in pH were marginally significant (p < 0.1) for below-drainage discharges. For above-drainage discharges, changes in Fe and SO42−concentrations were not significant, and increases in pH were highly significant between 1975 and 2012. Although a greater proportion of above-drainage discharges were net acidic in 2012 compared to below-drainage discharges, the increase in pH between 1975 and 2012 was greater for above- (median pH increase from 4.4 to 6.0) compared to below- (median pH increase from 5.6 to 6.1) drainage discharges. For cases where O2 is limited, transformation of aqueous FeII species to FeIII may be kinetically limited. In contrast, where O2 is abundant, aqueous Fe concentrations may be limited by FeIIImineral precipitation; thus, trends in Fe may not follow those for SO42−. In either case, when the supply of alkalinity is sufficient to buffer decreased acidity, the pH could increase by a step trend from strongly acidic (3–3.5) to near neutral (6–6.5) values. Modeled equilibrium with respect to FeIII precipitates varies with pH and Fe and SO42−reconcentrations: increasing pH promotes the formation of ferrihydrite, while decreasing concentrations of Fe limit the formation of ferrihydrite, and decreasing Fe and SO42−concentrations limit the precipitation of schwertmannite and favor formation of FeIIIhydroxyl complexes and uncomplexed Fe2+ and Fe3+. The analysis of the long-term geochemical changes in CMDs in the anthracite field and the effect of the hydrologic setting on water quality presented in this paper can help prioritize CMD remediation and facilitate selection and design of the most appropriate treatment systems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2015.02.010","usgsCitation":"Burrows, J.E., Peters, S.C., and Cravotta, C.A., 2015, Temporal geochemical variations in above- and below-drainage coal mine discharge: Applied Geochemistry, v. 62, p. 84-95, https://doi.org/10.1016/j.apgeochem.2015.02.010.","productDescription":"12 p.","startPage":"84","endPage":"95","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2012-01-01","temporalEnd":"2012-12-31","ipdsId":"IP-056784","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":310197,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.04437255859375,\n 40.36328834091583\n ],\n [\n -77.04437255859375,\n 41.605174521299304\n ],\n [\n -75.4595947265625,\n 41.605174521299304\n ],\n [\n -75.4595947265625,\n 40.36328834091583\n ],\n [\n -77.04437255859375,\n 40.36328834091583\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"62","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"562757aae4b0d158f592650b","contributors":{"authors":[{"text":"Burrows, Jill E.","contributorId":149323,"corporation":false,"usgs":false,"family":"Burrows","given":"Jill","email":"","middleInitial":"E.","affiliations":[{"id":16160,"text":"Lehigh University","active":true,"usgs":false}],"preferred":false,"id":577961,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peters, Stephen C.","contributorId":149324,"corporation":false,"usgs":false,"family":"Peters","given":"Stephen","email":"","middleInitial":"C.","affiliations":[{"id":16160,"text":"Lehigh University","active":true,"usgs":false}],"preferred":false,"id":577962,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cravotta, Charles A. III, 0000-0003-3116-4684 cravotta@usgs.gov","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":2193,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles","suffix":"III,","email":"cravotta@usgs.gov","middleInitial":"A.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":false,"id":577960,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70159225,"text":"sir20155128 - 2015 - Storage capacity of the Fena Valley Reservoir, Guam, Mariana Islands, 2014","interactions":[],"lastModifiedDate":"2015-10-20T09:22:29","indexId":"sir20155128","displayToPublicDate":"2015-10-19T20:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5128","title":"Storage capacity of the Fena Valley Reservoir, Guam, Mariana Islands, 2014","docAbstract":"The Fena Valley Reservoir is in southern Guam and is the primary source of water for the U.S. Naval Base Guam and nearby village residents. Since the construction of the Fena Dam in 1951, sediment has accumulated in the reservoir and reduced its storage capacity. The reservoir was surveyed previously in 1973, 1979, and 1990 to estimate the loss in storage capacity. To determine the current storage capacity, the U.S. Geological Survey, in cooperation with the U.S. Department of Defense Strategic Environmental Research and Development Program, surveyed the bathymetry of the reservoir in February 2014.
\nThe bathymetric survey was accomplished by making depth soundings using a boat-mounted, acoustic Doppler current profiler. Location during bathymetric data collection was determined using a single-base Global Navigation Satellite System-Real Time Kinematic survey. Vertical profiles of conductivity, temperature, and depth were collected periodically. The conductivity, temperature, and depth profiles were used to spatially and temporally adjust the sound-speed calculations used to determine depth from the soundings. Approximately 108 kilometers of transects with a total of about 380,000 depth soundings were surveyed. In addition, approximately 2,100 topographic survey points in shallow, wadable areas near the Imong River Delta were defined by using a Global Navigation Satellite System receiver attached to a fixed-length survey rod. Depth soundings and topographic survey points were compiled and interpolated to generate a digital-elevation model of the reservoir. Data extracted from the digital-elevation model were then tabulated to determine total reservoir capacity and create reservoir stage–surface area and stage–storage capacity tables.
\nAnalyses of the bathymetric data indicate that the reservoir currently has 6,915 acre-feet of storage capacity. The engineering drawings of record show that the total reservoir capacity in 1951 was estimated to be 8,365 acre-feet. Thus, between 1951 and 2014, the total storage capacity decreased by 1,450 acre-feet (a loss of 17 percent of the original total storage capacity). The remaining live-storage capacity, or the volume of storage above the lowest-level reservoir outlet elevation, was calculated to be 5,511 acre-feet in 2014, indicating a decrease of 372 acre-feet (or 6 percent) of the original 5,883 acre-feet of live-storage capacity. The remaining dead-storage capacity, or volume of storage below the lowest-level outlet, was 1,404 acre-feet in 2014, indicating a decrease of 1,078 acre-feet (or 43 percent) of the original 2,482 acre-feet of dead-storage capacity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155128","collaboration":"Prepared in cooperation with the Strategic Environmental Research and Development Program","usgsCitation":"Marineau, M.D., and Wright, S., 2015, Storage capacity of the Fena Valley Reservoir, Guam, Mariana Islands, 2014: U.S. Geological Survey Scientific Investigations Report 2015-5128, vi, 31 p., https://doi.org/10.3133/sir20155128.","productDescription":"vi, 31 p.","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-056696","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":310069,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5128/coverthb.jpg"},{"id":310070,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5128/sir20155128.pdf","text":"Report","size":"19.7","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5128"}],"otherGeospatial":"Fena Valley Reservoir, Guam, Mariana Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 144.6954345703125,\n 13.338847687855717\n ],\n [\n 144.6954345703125,\n 13.365738086822018\n ],\n [\n 144.70796585083008,\n 13.365738086822018\n ],\n [\n 144.70796585083008,\n 13.338847687855717\n ],\n [\n 144.6954345703125,\n 13.338847687855717\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95829
http://ca.water.usgs.gov
Habitat fragmentation is a fundamental cause of population decline and increased risk of extinction for many wildlife species; animals with large home ranges and small population sizes are particularly sensitive. The Louisiana black bear (Ursus americanus luteolus) exists only in small, isolated subpopulations as a result of land clearing for agriculture, but the relative potential for inter-subpopulation movement by Louisiana black bears has not been quantified, nor have characteristics of effective travel routes between habitat fragments been identified. We placed and monitored global positioning system (GPS) radio collars on 8 female and 23 male bears located in 4 subpopulations in Louisiana, which included a reintroduced subpopulation located between 2 of the remnant subpopulations. We compared characteristics of sequential radiolocations of bears (i.e., steps) with steps that were possible but not chosen by the bears to develop step selection function models based on conditional logistic regression. The probability of a step being selected by a bear increased as the distance to natural land cover and agriculture at the end of the step decreased and as distance from roads at the end of a step increased. To characterize connectivity among subpopulations, we used the step selection models to create 4,000 hypothetical correlated random walks for each subpopulation representing potential dispersal events to estimate the proportion that intersected adjacent subpopulations (hereafter referred to as successful dispersals). Based on the models, movement paths for males intersected all adjacent subpopulations but paths for females intersected only the most proximate subpopulations. Cross-validation and genetic and independent observation data supported our findings. Our models also revealed that successful dispersals were facilitated by a reintroduced population located between 2 distant subpopulations. Successful dispersals for males were dependent on natural land cover in private ownership. The addition of hypothetical 1,000-m- or 3,000-m-wide corridors between the 4 study areas had minimal effects on connectivity among subpopulations. For females, our model suggested that habitat between subpopulations would probably have to be permanently occupied for demographic rescue to occur. Thus, the establishment of stepping-stone populations, such as the reintroduced population that we studied, may be a more effective conservation measure than long corridors without a population presence in between.
","language":"English","publisher":"Wildlife Society","doi":"10.1002/jwmg.955","collaboration":"Louisiana Department of Wildlife and Fisheries; University of Tennessee; University of Maryland; U.S. Fish and Wildlife Service","usgsCitation":"Clark, J.D., Jared S. Laufenberg, Davidson, M., and Jennifer L. Murrow, 2015, Connectivity among subpopulations of Louisiana black bears as estimated by a step selection function: Journal of Wildlife Management, v. 79, no. 8, https://doi.org/10.1002/jwmg.955.","productDescription":"14 p.","startPage":"1360","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063498","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":310052,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Tensas River Basin, Three Rivers Complex, Upper Atchafalaya River Basin, Lower Atchafalaya River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.42135620117188,\n 32.41706632846282\n ],\n [\n -91.3677978515625,\n 32.41706632846282\n ],\n [\n -91.2744140625,\n 32.400834826722196\n ],\n [\n -91.17965698242188,\n 32.265071800242445\n ],\n [\n -91.24420166015624,\n 32.19537080888963\n ],\n [\n -91.3348388671875,\n 32.11631176714\n ],\n [\n -91.40350341796875,\n 32.01855598828661\n ],\n [\n -91.5216064453125,\n 31.989441837922904\n ],\n [\n -91.57791137695312,\n 32.07442922894548\n ],\n [\n -91.55181884765625,\n 32.30338454936734\n ],\n [\n -91.51199340820312,\n 32.37416245209553\n ],\n [\n -91.42135620117188,\n 32.41706632846282\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.09152221679688,\n 30.892797477508154\n ],\n [\n -91.97067260742188,\n 30.89161900878617\n ],\n [\n -91.88552856445312,\n 30.901046352477493\n ],\n [\n -91.76605224609375,\n 30.90811625106069\n ],\n [\n -91.72073364257812,\n 30.878654895778272\n ],\n [\n -91.6644287109375,\n 30.69933500437198\n ],\n [\n -91.66717529296875,\n 30.598911818191965\n ],\n [\n -91.66717529296875,\n 30.518498237455404\n ],\n [\n -91.72760009765624,\n 30.41670336862688\n ],\n [\n -91.87728881835938,\n 30.38709188778112\n ],\n [\n -91.99676513671875,\n 30.4060442699695\n ],\n [\n -92.07092285156249,\n 30.4912844521002\n ],\n [\n -92.1807861328125,\n 30.80555164278596\n ],\n [\n -92.09152221679688,\n 30.892797477508154\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.065673828125,\n 29.826348356278118\n ],\n [\n -91.34582519531249,\n 29.99775972557893\n ],\n [\n -91.42547607421875,\n 30.0286775329042\n ],\n [\n -91.4996337890625,\n 30.07622456354286\n ],\n [\n -91.66168212890625,\n 30.05483122485245\n ],\n [\n -91.74957275390625,\n 29.907329376851553\n ],\n [\n -91.4337158203125,\n 29.62360872200976\n ],\n [\n -91.03271484375,\n 29.492206334848714\n ],\n [\n -90.98876953125,\n 29.69759650228319\n ],\n [\n -91.065673828125,\n 29.826348356278118\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.71936035156249,\n 31.60193125799097\n ],\n [\n -91.76467895507812,\n 31.186958816798732\n ],\n [\n -91.88552856445312,\n 31.083517709507184\n ],\n [\n -92.032470703125,\n 31.08116546459057\n ],\n [\n -92.16430664062499,\n 31.135252364697717\n ],\n [\n -92.18765258789061,\n 31.235114421248575\n ],\n [\n -92.20550537109375,\n 31.357155132373343\n ],\n [\n -92.21511840820312,\n 31.481379984249294\n ],\n [\n -91.99264526367186,\n 31.62532121329918\n ],\n [\n -91.93084716796874,\n 31.644028945047847\n ],\n [\n -91.8017578125,\n 31.6358447754293\n ],\n [\n -91.73583984374999,\n 31.618304843852865\n ],\n [\n -91.71936035156249,\n 31.60193125799097\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"79","issue":"8","edition":"1347","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-28","publicationStatus":"PW","scienceBaseUri":"56260618e4b0fb9a11dd75d6","chorus":{"doi":"10.1002/jwmg.955","url":"http://dx.doi.org/10.1002/jwmg.955","publisher":"Wiley-Blackwell","authors":"Clark Joseph D., Laufenberg Jared S., Davidson Maria, Murrow Jennifer L.","journalName":"The Journal of Wildlife Management","publicationDate":"8/28/2015","auditedOn":"10/5/2015"},"contributors":{"authors":[{"text":"Clark, Joseph D. 0000-0002-8547-8112 jclark1@usgs.gov","orcid":"https://orcid.org/0000-0002-8547-8112","contributorId":2265,"corporation":false,"usgs":true,"family":"Clark","given":"Joseph","email":"jclark1@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":540630,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jared S. Laufenberg","contributorId":139272,"corporation":false,"usgs":false,"family":"Jared S. Laufenberg","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":540631,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Davidson, Maria","contributorId":139273,"corporation":false,"usgs":false,"family":"Davidson","given":"Maria","email":"","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":540632,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jennifer L. Murrow","contributorId":139274,"corporation":false,"usgs":false,"family":"Jennifer L. Murrow","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":540633,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70158592,"text":"fs20153070 - 2015 - myScience—Engaging the public in U.S. Geological Survey science","interactions":[],"lastModifiedDate":"2015-10-19T12:54:53","indexId":"fs20153070","displayToPublicDate":"2015-10-19T13:00:00","publicationYear":"2015","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":"2015-3070","title":"myScience—Engaging the public in U.S. Geological Survey science","docAbstract":"myScience (http://txpub.usgs.gov/myscience/) is a Web application developed by the U.S. Geological Survey (USGS) Texas Water Science Center through a partnership with the USGS Community for Data Integration to address the need for increasing public awareness and participation in existing USGS citizen science projects. The myScience application contains data for 20 projects available for public participation representing all USGS mission areas. A visitor to the USGS education Web site (http://education.usgs.gov/) can click on the Citizen Science link to search for citizen science projects by topic or location, select a project of interest, and click “Get Involved.” Within the USGS, an internal version of myScience serves to build a community of practice and knowledge sharing among scientists who lead or would like to lead a crowdsourcing project.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153070","usgsCitation":"Holl, Sally, 2015, myScience—Engaging the public in U.S. Geological Survey Science: U.S. Geological Survey Fact Sheet 2015–3070, 2 p., https://dx.doi.org/10.3133/fs20153070.","productDescription":"2 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068920","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":310002,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3070/fs20153070.pdf","text":"Report","size":"251 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3070"},{"id":310001,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2015/3070/coverthb.jpg"}],"contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
http://tx.usgs.gov/