In 2012, 2,045 observers contributed 1,592 sites to the NPDb, encompassing all 50 states, the U.S. Virgin Islands, and Puerto Rico. At the close of 2012 the NPDb contained a total of over 1.6 million phenophase status records. More than half of these records were submitted in 2012. Observers submitted records on 547 species in 2012, including 371 plant species (comprised of 5,584 individual plants) and 176 animal species. Red maple (Acer rubrum) and American Robin (Turdus migratorius) were the most observed plant and animal species in 2012. Plant phenophases related to fruiting and flowering had the most records in 2012 and in all years combined, whereas animal phenophases related to feeding had the most records.
","language":"English","publisher":"USA National Phenology Network","usgsCitation":"Kellermann, J., Crimmins, T., Denny, E., Enquist, C., Gerst, K., Rosemartin, A., and Weltzin, J., 2013, Nature's Notebook 2012: State of the data: USA-NPN Technical Series 2013-001, 6 p.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2012-01-01","temporalEnd":"2012-12-31","ipdsId":"IP-046270","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":286003,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":321336,"type":{"id":15,"text":"Index Page"},"url":"https://www.usanpn.org/pubs/reports#USA-NPN_Technical_Series"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"535594b9e4b0120853e8c0a1","contributors":{"authors":[{"text":"Kellermann, Jherime","contributorId":20651,"corporation":false,"usgs":true,"family":"Kellermann","given":"Jherime","affiliations":[],"preferred":false,"id":481679,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crimmins, T.M.","contributorId":93823,"corporation":false,"usgs":true,"family":"Crimmins","given":"T.M.","email":"","affiliations":[],"preferred":false,"id":481683,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Denny, E.G.","contributorId":13544,"corporation":false,"usgs":true,"family":"Denny","given":"E.G.","email":"","affiliations":[],"preferred":false,"id":481677,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Enquist, C.A.F.","contributorId":38895,"corporation":false,"usgs":true,"family":"Enquist","given":"C.A.F.","email":"","affiliations":[],"preferred":false,"id":481680,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gerst, K.L.","contributorId":42521,"corporation":false,"usgs":true,"family":"Gerst","given":"K.L.","email":"","affiliations":[],"preferred":false,"id":481681,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rosemartin, A.H.","contributorId":17138,"corporation":false,"usgs":true,"family":"Rosemartin","given":"A.H.","email":"","affiliations":[],"preferred":false,"id":481678,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Weltzin, Jake F.","contributorId":51005,"corporation":false,"usgs":true,"family":"Weltzin","given":"Jake F.","affiliations":[],"preferred":false,"id":481682,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70046044,"text":"70046044 - 2013 - Sequential Gaussian co-simulation of rate decline parameters of longwall gob gas ventholes","interactions":[],"lastModifiedDate":"2013-06-17T14:34:09","indexId":"70046044","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2070,"text":"International Journal of Rock Mechanics and Mining Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Sequential Gaussian co-simulation of rate decline parameters of longwall gob gas ventholes","docAbstract":"Gob gas ventholes (GGVs) are used to control methane inflows into a longwall mining operation by capturing the gas within the overlying fractured strata before it enters the work environment. Using geostatistical co-simulation techniques, this paper maps the parameters of their rate decline behaviors across the study area, a longwall mine in the Northern Appalachian basin. Geostatistical gas-in-place (GIP) simulations were performed, using data from 64 exploration boreholes, and GIP data were mapped within the fractured zone of the study area. In addition, methane flowrates monitored from 10 GGVs were analyzed using decline curve analyses (DCA) techniques to determine parameters of decline rates. Surface elevation showed the most influence on methane production from GGVs and thus was used to investigate its relation with DCA parameters using correlation techniques on normal-scored data. Geostatistical analysis was pursued using sequential Gaussian co-simulation with surface elevation as the secondary variable and with DCA parameters as the primary variables. The primary DCA variables were effective percentage decline rate, rate at production start, rate at the beginning of forecast period, and production end duration. Co-simulation results were presented to visualize decline parameters at an area-wide scale. Wells located at lower elevations, i.e., at the bottom of valleys, tend to perform better in terms of their rate declines compared to those at higher elevations. These results were used to calculate drainage radii of GGVs using GIP realizations. The calculated drainage radii are close to ones predicted by pressure transient tests.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"International Journal of Rock Mechanics and Mining Sciences","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.ijrmms.2012.11.003","usgsCitation":"Karacan, C., and Olea, R., 2013, Sequential Gaussian co-simulation of rate decline parameters of longwall gob gas ventholes: International Journal of Rock Mechanics and Mining Sciences, v. 59, p. 1-14, https://doi.org/10.1016/j.ijrmms.2012.11.003.","productDescription":"15 p.","startPage":"1","endPage":"14","ipdsId":"IP-034214","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":473896,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":273845,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":273842,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.ijrmms.2012.11.003"}],"country":"United States","state":"Pennsylvania","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -80.52,39.72 ], [ -80.52,42.27 ], [ -74.69,42.27 ], [ -74.69,39.72 ], [ -80.52,39.72 ] ] ] } } ] }","volume":"59","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51c02ff5e4b0ee1529ed3d4d","contributors":{"authors":[{"text":"Karacan, C. Özgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":96571,"corporation":false,"usgs":true,"family":"Karacan","given":"C. Özgen","affiliations":[],"preferred":false,"id":478752,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olea, Ricardo A. 0000-0003-4308-0808","orcid":"https://orcid.org/0000-0003-4308-0808","contributorId":47873,"corporation":false,"usgs":true,"family":"Olea","given":"Ricardo A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":478751,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70045174,"text":"ofr20131031 - 2013 - Effects of equipment performance on data quality from the National Atmospheric Deposition Program/National Trends Network and the Mercury Deposition Network","interactions":[],"lastModifiedDate":"2013-04-01T12:49:10","indexId":"ofr20131031","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1031","title":"Effects of equipment performance on data quality from the National Atmospheric Deposition Program/National Trends Network and the Mercury Deposition Network","docAbstract":"The U.S. Geological Survey Branch of Quality Systems operates the Precipitation Chemistry Quality Assurance project (PCQA) to provide independent, external quality-assurance for the National Atmospheric Deposition Program (NADP). NADP is composed of five monitoring networks that measure the chemical composition of precipitation and ambient air. PCQA and the NADP Program Office completed five short-term studies to investigate the effects of equipment performance with respect to the National Trends Network (NTN) and Mercury Deposition Network (MDN) data quality: sample evaporation from NTN collectors; sample volume and mercury loss from MDN collectors; mercury adsorption to MDN collector glassware, grid-type precipitation sensors for precipitation collectors, and the effects of an NTN collector wind shield on sample catch efficiency. Sample-volume evaporation from an NTN Aerochem Metrics (ACM) collector ranged between 1.1–33 percent with a median of 4.7 percent. The results suggest that weekly NTN sample evaporation is small relative to sample volume. MDN sample evaporation occurs predominantly in western and southern regions of the United States (U.S.) and more frequently with modified ACM collectors than with N-CON Systems Inc. collectors due to differences in airflow through the collectors. Variations in mercury concentrations, measured to be as high as 47.5 percent per week with a median of 5 percent, are associated with MDN sample-volume loss. Small amounts of mercury are also lost from MDN samples by adsorption to collector glassware irrespective of collector type. MDN 11-grid sensors were found to open collectors sooner, keep them open longer, and cause fewer lid cycles than NTN 7-grid sensors. Wind shielding an NTN ACM collector resulted in collection of larger quantities of precipitation while also preserving sample integrity.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131031","usgsCitation":"Wetherbee, G.A., and Rhodes, M.F., 2013, Effects of equipment performance on data quality from the National Atmospheric Deposition Program/National Trends Network and the Mercury Deposition Network: U.S. Geological Survey Open-File Report 2013-1031, ix, 53 p., https://doi.org/10.3133/ofr20131031.","productDescription":"ix, 53 p.","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":143,"text":"Branch of Quality Systems","active":true,"usgs":true}],"links":[{"id":270417,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131031.gif"},{"id":270415,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1031/"},{"id":270416,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1031/OF13-1031.pdf"}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 144.616667,13.233333 ], [ 144.616667,71.833333 ], [ -64.566667,71.833333 ], [ -64.566667,13.233333 ], [ 144.616667,13.233333 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"515a9e5ee4b0105540728a1e","contributors":{"authors":[{"text":"Wetherbee, Gregory A. 0000-0002-6720-2294 wetherbe@usgs.gov","orcid":"https://orcid.org/0000-0002-6720-2294","contributorId":1044,"corporation":false,"usgs":true,"family":"Wetherbee","given":"Gregory","email":"wetherbe@usgs.gov","middleInitial":"A.","affiliations":[{"id":143,"text":"Branch of Quality Systems","active":true,"usgs":true}],"preferred":true,"id":476988,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rhodes, Mark F.","contributorId":17360,"corporation":false,"usgs":true,"family":"Rhodes","given":"Mark","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":476989,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70045175,"text":"ofr20131074 - 2013 - Change in the length of the northern section of the Chandeleur Islands oil berm, September 5, 2010, through September 3, 2012","interactions":[],"lastModifiedDate":"2013-04-01T13:38:52","indexId":"ofr20131074","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1074","title":"Change in the length of the northern section of the Chandeleur Islands oil berm, September 5, 2010, through September 3, 2012","docAbstract":"On April 20, 2010, an explosion on the Deepwater Horizon oil rig drilling at the Macondo Prospect site in the Gulf of Mexico resulted in a marine oil spill that continued to flow through July 15, 2010. One of the affected areas was the Breton National Wildlife Refuge, which consists of a chain of low-lying islands, including Breton Island and the Chandeleur Islands, and their surrounding waters. The island chain is located approximately 115–150 kilometers north-northwest of the spill site. A sand berm was constructed seaward of, and on, the island chain. Construction began at the northern end of the Chandeleur Islands in June 2010 and ended in April 2011. The berm consisted of three distinct sections based on where the berm was placed relative to the islands. The northern section of the berm was built in open water on a submerged portion of the Chandeleur Islands platform. The middle section was built approximately 70–90 meters seaward of the Chandeleur Islands. The southern section was built on the islands’ beaches. Repeated Landsat and SPOT satellite imagery and airborne lidar were used to observe the disintegration of the berm over time. The methods used to analyze the remotely sensed data and the resulting, derived data for the northern section are described in this report.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131074","usgsCitation":"Plant, N., and Guy, K.K., 2013, Change in the length of the northern section of the Chandeleur Islands oil berm, September 5, 2010, through September 3, 2012: U.S. Geological Survey Open-File Report 2013-1074, iii, 9 p., https://doi.org/10.3133/ofr20131074.","productDescription":"iii, 9 p.","numberOfPages":"12","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2010-09-05","temporalEnd":"2012-09-03","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":270421,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1074/pdf/ofr2013-1074.pdf"},{"id":270422,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1074/"},{"id":270423,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131074.gif"}],"country":"United States","state":"Alabama;Louisiana;Mississippi","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -89.560547,29.084977 ], [ -89.560547,30.47945 ], [ -88.041687,30.47945 ], [ -88.041687,29.084977 ], [ -89.560547,29.084977 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"515a9e5de4b0105540728a1a","contributors":{"authors":[{"text":"Plant, N.G.","contributorId":94023,"corporation":false,"usgs":true,"family":"Plant","given":"N.G.","email":"","affiliations":[],"preferred":false,"id":476991,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guy, K. K.","contributorId":24393,"corporation":false,"usgs":true,"family":"Guy","given":"K.","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":476990,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70043807,"text":"70043807 - 2013 - extrap: Software to assist the selection of extrapolation methods for moving-boat ADCP streamflow measurements","interactions":[],"lastModifiedDate":"2018-02-08T09:37:54","indexId":"70043807","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1315,"text":"Computers & Geosciences","printIssn":"0098-3004","active":true,"publicationSubtype":{"id":10}},"title":"extrap: Software to assist the selection of extrapolation methods for moving-boat ADCP streamflow measurements","docAbstract":"Selection of the appropriate extrapolation methods for computing the discharge in the unmeasured top and bottom parts of a moving-boat acoustic Doppler current profiler (ADCP) streamflow measurement is critical to the total discharge computation. The software tool, extrap, combines normalized velocity\nprofiles from the entire cross section and multiple transects to determine a mean profile for the measurement. The use of an exponent derived from normalized data from the entire cross section is shown to be valid for application of the power velocity distribution law in the computation of the unmeasured discharge in a cross section. Selected statistics are combined with empirically derived criteria to automatically select the appropriate extrapolation methods. A graphical user interface (GUI) provides the user tools to visually evaluate the automatically selected extrapolation methods and manually change them, as necessary. The sensitivity of the total discharge to available extrapolation methods is presented in the GUI. Use of extrap by field hydrographers has demonstrated that extrap is a more accurate and efficient method of determining the appropriate extrapolation methods compared with tools currently (2012) provided in the ADCP manufacturers’ software.","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.cageo.2013.02.001","usgsCitation":"Mueller, D.S., 2013, extrap: Software to assist the selection of extrapolation methods for moving-boat ADCP streamflow measurements: Computers & Geosciences, v. 54, p. 211-218, https://doi.org/10.1016/j.cageo.2013.02.001.","productDescription":"8 p.","startPage":"211","endPage":"218","numberOfPages":"8","ipdsId":"IP-043372","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":270443,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":270442,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.cageo.2013.02.001"}],"volume":"54","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"515bfdffe4b075500ee5cab0","contributors":{"authors":[{"text":"Mueller, David S. dmueller@usgs.gov","contributorId":1499,"corporation":false,"usgs":true,"family":"Mueller","given":"David","email":"dmueller@usgs.gov","middleInitial":"S.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":474258,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70045252,"text":"70045252 - 2013 - Variability of displacement at a point: Implications for earthquake‐size distribution and rupture hazard on faults","interactions":[],"lastModifiedDate":"2021-05-21T17:13:11.854001","indexId":"70045252","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Variability of displacement at a point: Implications for earthquake‐size distribution and rupture hazard on faults","docAbstract":"To investigate the nature of earthquake‐magnitude distributions on faults, we compare the interevent variability of surface displacement at a point on a fault from a composite global data set of paleoseismic observations with the variability expected from two prevailing magnitude–frequency distributions: the truncated‐exponential model and the characteristic‐earthquake model. We use forward modeling to predict the coefficient of variation (CV) for the alternative earthquake distributions, incorporating factors that would effect observations of displacement at a site. The characteristic‐earthquake model (with a characteristic‐magnitude range of ±0.25) produces CV values consistent with the data (CV∼0.5) only if the variability for a given earthquake magnitude is small. This condition implies that rupture patterns on a fault are stable, in keeping with the concept behind the model. This constraint also bears upon fault‐rupture hazard analysis, which, for lack of point‐specific information, has used global scaling relations to infer variability in average displacement for a given‐size earthquake. Exponential distributions of earthquakes (from M 5 to the maximum magnitude) give rise to CV values that are significantly larger than the empirical constraint. A version of the model truncated at M 7, however, yields values consistent with a larger CV (∼0.6) determined for small‐displacement sites. Although this result allows for a difference in the magnitude distribution of smaller surface‐rupturing earthquakes, it may reflect, in part, less stability in the displacement profile of smaller ruptures and/or the tails of larger ruptures.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120120159","usgsCitation":"Hecker, S., Abrahamson, N., and Wooddell, K., 2013, Variability of displacement at a point: Implications for earthquake‐size distribution and rupture hazard on faults: Bulletin of the Seismological Society of America, v. 103, no. 2A, p. 651-674, https://doi.org/10.1785/0120120159.","productDescription":"24 p.","startPage":"651","endPage":"674","ipdsId":"IP-037964","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":272873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"103","issue":"2A","noUsgsAuthors":false,"publicationDate":"2013-03-21","publicationStatus":"PW","scienceBaseUri":"51a5d1f1e4b0605bc571f02d","contributors":{"authors":[{"text":"Hecker, Suzanne 0000-0002-5054-372X shecker@usgs.gov","orcid":"https://orcid.org/0000-0002-5054-372X","contributorId":3553,"corporation":false,"usgs":true,"family":"Hecker","given":"Suzanne","email":"shecker@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":477140,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abrahamson, N. A.","contributorId":27152,"corporation":false,"usgs":false,"family":"Abrahamson","given":"N. A.","affiliations":[],"preferred":false,"id":477141,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wooddell, Kathryn","contributorId":47674,"corporation":false,"usgs":false,"family":"Wooddell","given":"Kathryn","email":"","affiliations":[{"id":13174,"text":"Pacific Gas & Electric","active":true,"usgs":false}],"preferred":false,"id":477142,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70044643,"text":"70044643 - 2013 - The effects of increased stream temperatures on juvenile steelhead growth in the Yakima River Basin based on projected climate change scenarios","interactions":[],"lastModifiedDate":"2016-04-26T10:00:54","indexId":"70044643","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1246,"text":"Climate Change","onlineIssn":"1573-1480","printIssn":"0165-0009","active":true,"publicationSubtype":{"id":10}},"title":"The effects of increased stream temperatures on juvenile steelhead growth in the Yakima River Basin based on projected climate change scenarios","docAbstract":"Stakeholders within the Yakima River Basin expressed concern over impacts of climate change on mid-Columbia River steelhead (Oncorhynchus mykiss), listed under the Endangered Species Act. We used a bioenergetics model to assess the impacts of changing stream temperatures—resulting from different climate change scenarios—on growth of juvenile steelhead in the Yakima River Basin. We used diet and fish size data from fieldwork in a bioenergetics model and integrated baseline and projected stream temperatures from down-scaled air temperature climate modeling into our analysis. The stream temperature models predicted that daily mean temperatures of salmonid-rearing streams in the basin could increase by 1–2°C and our bioenergetics simulations indicated that such increases could enhance the growth of steelhead in the spring, but reduce it during the summer. However, differences in growth rates of fish living under different climate change scenarios were minor, ranging from about 1–5%. Because our analysis focused mostly on the growth responses of steelhead to changes in stream temperatures, further work is needed to fully understand the potential impacts of climate change. Studies should include evaluating changing stream flows on fish activity and energy budgets, responses of aquatic insects to climate change, and integration of bioenergetics, population dynamics, and habitat responses to climate change.
","language":"English","publisher":"Springer","doi":"10.1007/s10584-012-0627-x","usgsCitation":"Hardiman, J.M., and Mesa, M.G., 2013, The effects of increased stream temperatures on juvenile steelhead growth in the Yakima River Basin based on projected climate change scenarios: Climate Change, v. 124, no. 1, p. 413-426, https://doi.org/10.1007/s10584-012-0627-x.","productDescription":"14 p.","startPage":"413","endPage":"426","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-037042","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":273237,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Yakima River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -120.65,46.19 ], [ -120.65,47.01 ], [ -119.77,47.01 ], [ -119.77,46.19 ], [ -120.65,46.19 ] ] ] } } ] }","volume":"124","issue":"1","noUsgsAuthors":false,"publicationDate":"2013-01-17","publicationStatus":"PW","scienceBaseUri":"51af0c70e4b08a3322c2c351","contributors":{"authors":[{"text":"Hardiman, Jill M. 0000-0002-3661-9695 jhardiman@usgs.gov","orcid":"https://orcid.org/0000-0002-3661-9695","contributorId":2672,"corporation":false,"usgs":true,"family":"Hardiman","given":"Jill","email":"jhardiman@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":476122,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mesa, Matthew G. mmesa@usgs.gov","contributorId":3423,"corporation":false,"usgs":true,"family":"Mesa","given":"Matthew","email":"mmesa@usgs.gov","middleInitial":"G.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":476123,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70178655,"text":"70178655 - 2013 - Development and application of a soil organic matter-based soil quality index in mineralized terrane of the Western US","interactions":[],"lastModifiedDate":"2017-11-21T15:03:23","indexId":"70178655","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1534,"text":"Environmental Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Development and application of a soil organic matter-based soil quality index in mineralized terrane of the Western US","docAbstract":"Soil quality indices provide a means of distilling large amounts of data into a single metric that evaluates the soil’s ability to carry out key ecosystem functions. Primarily developed in agroecosytems, then forested ecosystems, an index using the relation between soil organic matter and other key soil properties in more semi-arid systems of the Western US impacted by different geologic mineralization was developed. Three different sites in two different mineralization types, acid sulfate and Cu/Mo porphyry in California and Nevada, were studied. Soil samples were collected from undisturbed soils in both mineralized and nearby unmineralized terrane as well as waste rock and tailings. Eight different microbial parameters (carbon substrate utilization, microbial biomass-C, mineralized-C, mineralized-N and enzyme activities of acid phosphatase, alkaline phosphatase, arylsulfatase, and fluorescein diacetate) along with a number of physicochemical parameters were measured. Multiple linear regression models between these parameters and both total organic carbon and total nitrogen were developed, using the ratio of predicted to measured values as the soil quality index. In most instances, pooling unmineralized and mineralized soil data within a given study site resulted in lower model correlations. Enzyme activity was a consistent explanatory variable in the models across the study sites. Though similar indicators were significant in models across different mineralization types, pooling data across sites inhibited model differentiation of undisturbed and disturbed sites. This procedure could be used to monitor recovery of disturbed systems in mineralized terrane and help link scientific and management disciplines.
","language":"English","publisher":"Springer","doi":"10.1007/s12665-012-1876-8","usgsCitation":"Blecker, S., Stillings, L., Amacher, M., Ippolito, J., and DeCrappeo, N., 2013, Development and application of a soil organic matter-based soil quality index in mineralized terrane of the Western US: Environmental Earth Sciences, v. 68, no. 7, p. 1887-1901, https://doi.org/10.1007/s12665-012-1876-8.","productDescription":"15 p.","startPage":"1887","endPage":"1901","ipdsId":"IP-026505","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":473894,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://eprints.nwisrl.ars.usda.gov/id/eprint/1489/1/1453.pdf","text":"External Repository"},{"id":331460,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"68","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2012-08-12","publicationStatus":"PW","scienceBaseUri":"58468aebe4b04fc80e5236cd","contributors":{"authors":[{"text":"Blecker, S.W.","contributorId":99671,"corporation":false,"usgs":true,"family":"Blecker","given":"S.W.","email":"","affiliations":[],"preferred":false,"id":654730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stillings, Lisa L. 0000-0002-9011-8891 stilling@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-8891","contributorId":3143,"corporation":false,"usgs":true,"family":"Stillings","given":"Lisa L.","email":"stilling@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":654726,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Amacher, M.C.","contributorId":74043,"corporation":false,"usgs":true,"family":"Amacher","given":"M.C.","affiliations":[],"preferred":false,"id":654728,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Ippolito, J.A.","contributorId":54890,"corporation":false,"usgs":true,"family":"Ippolito","given":"J.A.","email":"","affiliations":[],"preferred":false,"id":654727,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"DeCrappeo, N.M.","contributorId":86269,"corporation":false,"usgs":true,"family":"DeCrappeo","given":"N.M.","affiliations":[],"preferred":false,"id":654729,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70173430,"text":"70173430 - 2013 - Advantages of geographically weighted regression for modeling benthic substrate in two Greater Yellowstone Ecosystem streams","interactions":[],"lastModifiedDate":"2016-06-20T15:35:56","indexId":"70173430","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1550,"text":"Environmental Modeling & Assessment","onlineIssn":" 1573-296","printIssn":"1420-2026","active":true,"publicationSubtype":{"id":10}},"title":"Advantages of geographically weighted regression for modeling benthic substrate in two Greater Yellowstone Ecosystem streams","docAbstract":"Stream habitat assessments are commonplace in fish management, and often involve nonspatial analysis methods for quantifying or predicting habitat, such as ordinary least squares regression (OLS). Spatial relationships, however, often exist among stream habitat variables. For example, water depth, water velocity, and benthic substrate sizes within streams are often spatially correlated and may exhibit spatial nonstationarity or inconsistency in geographic space. Thus, analysis methods should address spatial relationships within habitat datasets. In this study, OLS and a recently developed method, geographically weighted regression (GWR), were used to model benthic substrate from water depth and water velocity data at two stream sites within the Greater Yellowstone Ecosystem. For data collection, each site was represented by a grid of 0.1 m2 cells, where actual values of water depth, water velocity, and benthic substrate class were measured for each cell. Accuracies of regressed substrate class data by OLS and GWR methods were calculated by comparing maps, parameter estimates, and determination coefficient r 2. For analysis of data from both sites, Akaike’s Information Criterion corrected for sample size indicated the best approximating model for the data resulted from GWR and not from OLS. Adjusted r 2 values also supported GWR as a better approach than OLS for prediction of substrate. This study supports GWR (a spatial analysis approach) over nonspatial OLS methods for prediction of habitat for stream habitat assessments.
","language":"English","publisher":"Springer","doi":"10.1007/s10666-012-9334-2","usgsCitation":"Sheehan, K.R., Strager, M.P., and Welsh, S., 2013, Advantages of geographically weighted regression for modeling benthic substrate in two Greater Yellowstone Ecosystem streams: Environmental Modeling & Assessment, v. 18, no. 2, p. 209-219, https://doi.org/10.1007/s10666-012-9334-2.","productDescription":"11 p.","startPage":"209","endPage":"219","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-033772","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":324038,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","volume":"18","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2012-09-05","publicationStatus":"PW","scienceBaseUri":"576913aee4b07657d19fef8a","contributors":{"authors":[{"text":"Sheehan, Kenneth R.","contributorId":146541,"corporation":false,"usgs":false,"family":"Sheehan","given":"Kenneth","email":"","middleInitial":"R.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":637122,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Strager, Michael P.","contributorId":169817,"corporation":false,"usgs":false,"family":"Strager","given":"Michael","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":637123,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Welsh, Stuart A. 0000-0003-0362-054X swelsh@usgs.gov","orcid":"https://orcid.org/0000-0003-0362-054X","contributorId":152088,"corporation":false,"usgs":true,"family":"Welsh","given":"Stuart A.","email":"swelsh@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":637121,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70045178,"text":"ofr20131075 - 2013 - Change in the length of the middle section of the Chandeleur Islands oil berm, November 17, 2010, through September 6, 2011","interactions":[],"lastModifiedDate":"2013-04-01T14:06:26","indexId":"ofr20131075","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1075","title":"Change in the length of the middle section of the Chandeleur Islands oil berm, November 17, 2010, through September 6, 2011","docAbstract":"On April 20, 2010, an explosion on the Deepwater Horizon oil rig drilling at the Macondo Prospect site in the Gulf of Mexico resulted in a marine oil spill that continued to flow through July 15, 2010. One of the affected areas was the Breton National Wildlife Refuge, which consists of a chain of low-lying islands, including Breton Island and the Chandeleur Islands, and their surrounding waters. The island chain is located approximately 115-150 kilometers north-northwest of the spill site. A sand berm was constructed seaward of, and on, the island chain. Construction began at the northern end of the Chandeleur Islands in June 2010 and ended in April 2011. The berm consisted of three distinct sections based on where the berm was placed relative to the islands. The northern section of the berm was built in open water on a submerged portion of the Chandeleur Islands platform. The middle section was built approximately 70-90 meters seaward of the Chandeleur Islands. The southern section was built on the islands' beaches. Repeated Landsat and SPOT satellite imagery and airborne lidar were used to observe the disintegration of the berm over time. The methods used to analyze the remotely sensed data and the resulting, derived data for the middle section are described in this report.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131075","usgsCitation":"Plant, N., and Guy, K.K., 2013, Change in the length of the middle section of the Chandeleur Islands oil berm, November 17, 2010, through September 6, 2011: U.S. Geological Survey Open-File Report 2013-1075, iii, 8 p., https://doi.org/10.3133/ofr20131075.","productDescription":"iii, 8 p.","numberOfPages":"11","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2010-11-17","temporalEnd":"2011-09-06","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":270427,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131075.gif"},{"id":270426,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1075/"},{"id":270425,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1075/pdf/ofr2013-1075.pdf"}],"country":"United States","state":"Alabama;Louisiana;Mississippi","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -89.5605,28.8206 ], [ -89.5605,30.4794 ], [ -88.0417,30.4794 ], [ -88.0417,28.8206 ], [ -89.5605,28.8206 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"515a9e4fe4b0105540728a16","contributors":{"authors":[{"text":"Plant, N.G.","contributorId":94023,"corporation":false,"usgs":true,"family":"Plant","given":"N.G.","email":"","affiliations":[],"preferred":false,"id":476993,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guy, K. K.","contributorId":24393,"corporation":false,"usgs":true,"family":"Guy","given":"K.","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":476992,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70045072,"text":"sir20135048 - 2013 - Water quality of streams draining abandoned and reclaimed mined lands in the Kantishna Hills area, Denali National Park and Preserve, Alaska, 2008–11","interactions":[],"lastModifiedDate":"2018-07-07T18:16:14","indexId":"sir20135048","displayToPublicDate":"2013-03-29T00:00:00","publicationYear":"2013","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":"2013-5048","title":"Water quality of streams draining abandoned and reclaimed mined lands in the Kantishna Hills area, Denali National Park and Preserve, Alaska, 2008–11","docAbstract":"The Kantishna Hills are an area of low elevation mountains in the northwest part of Denali National Park and Preserve, Alaska. Streams draining the Kantishna Hills are clearwater streams that support several species of fish and are derived from rain, snowmelt, and subsurface aquifers. However, the water quality of many of these streams has been degraded by mining. Past mining practices generated acid mine drainage and excessive sediment loads that affected water quality and aquatic habitat. Because recovery through natural processes is limited owing to a short growing season, several reclamation projects have been implemented on several streams in the Kantishna Hills region. To assess the current water quality of streams in the Kantishna Hills area and to determine if reclamation efforts have improved water quality, a cooperative study between the U.S. Geological Survey and the National Park Service was undertaken during 2008-11. High levels of turbidity, an indicator of high concentrations of suspended sediment, were documented in water-quality data collected in the mid-1980s when mining was active. Mining ceased in 1985 and water-quality data collected during this study indicate that levels of turbidity have declined significantly. Turbidity levels generally were less than 2 Formazin Nephelometric Units and suspended sediment concentrations generally were less than 1 milligram per liter during the current study. Daily turbidity data at Rock Creek, an unmined stream, and at Caribou Creek, a mined stream, documented nearly identical patterns of turbidity in 2009, indicating that reclamation as well as natural revegetation in mined streams has improved water quality. Specific conductance and concentrations of dissolved solids and major ions were highest from streams that had been mined. Most of these streams flow into Moose Creek, which functions as an integrator stream, and dilutes the specific conductance and ion concentrations. Calcium and magnesium are the dominant cations, and bicarbonate and sulfate are the dominant anions. Water samples indicate that the water from Rock Creek, Moose Creek, Slate Creek, and Eldorado Creek is a calcium bicarbonate-type water. The remaining sites are a calcium sulfate type water. U.S. Environmental Protection Agency guidelines for arsenic and antimony in drinking water were exceeded in water at Slate Creek and Eureka Creek. Concentrations of arsenic, cadmium, chromium, copper, lead, nickel, and zinc in streambed sediments at many sites exceed sediment quality guideline thresholds that could be toxic to aquatic life. However, assessment of these concentrations, along with the level of organic carbon detected in the sediment, indicate that only concentrations of arsenic and chromium may be toxic to aquatic life at many sites. In 2008 and 2009, 104 macroinvertebrate taxa and 164 algae taxa were identified from samples collected from seven sites. Of the macroinvertebrates, 86 percent were insects and most of the algae consisted of diatoms. Based on the National Community Index, Rock Creek, a reference site, and Caribou Creek, and a mined stream that had undergone some reclamation, exhibited the best overall stream conditions; whereas Slate Creek and Friday Creek, two small streams that were mined extensively, exhibited the worst stream conditions. A non-metric multi-dimensional scaling analysis of the macroinvertebrate and algae data showed a distinct grouping between the 2008 and 2009 samples, likely because of differences between a wet, cool summer in 2008 and a dry, warm summer in 2009.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135048","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Brabets, T.P., and Ourso, R.T., 2013, Water quality of streams draining abandoned and reclaimed mined lands in the Kantishna Hills area, Denali National Park and Preserve, Alaska, 2008–11: U.S. Geological Survey Scientific Investigations Report 2013-5048, Report: viii, 74 p.; 8 Appendices, https://doi.org/10.3133/sir20135048.","productDescription":"Report: viii, 74 p.; 8 Appendices","numberOfPages":"84","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":270369,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135048.jpg"},{"id":270362,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5048/sir20135048_AppendixB.xls"},{"id":270359,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5048/"},{"id":270361,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5048/sir20135048_AppendixA.xls"},{"id":270363,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5048/sir20135048_AppendixC.xls"},{"id":270364,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5048/sir20135048_AppendixD.xls"},{"id":270360,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5048/pdf/sir20135048.pdf"},{"id":270365,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5048/sir20135048_AppendixE.xls"},{"id":270368,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5048/sir20135048_AppendixH.xls"},{"id":270366,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5048/sir20135048_AppendixF.xls"},{"id":270367,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5048/sir20135048_AppendixG.xls"}],"datum":"North American Datum of 1983","country":"United States","state":"Alaska","otherGeospatial":"Denali National Park;Kantishna Hills","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -15.0175,0.0016666666666666668 ], [ -15.0175,0.0016666666666666668 ], [ -0.015277777777777777,0.0016666666666666668 ], [ -0.015277777777777777,0.0016666666666666668 ], [ -15.0175,0.0016666666666666668 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5156a9ede4b06ea905cdc00a","contributors":{"authors":[{"text":"Brabets, Timothy P. tbrabets@usgs.gov","contributorId":2087,"corporation":false,"usgs":true,"family":"Brabets","given":"Timothy","email":"tbrabets@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":true,"id":476733,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ourso, Robert T. 0000-0002-5952-8681 rtourso@usgs.gov","orcid":"https://orcid.org/0000-0002-5952-8681","contributorId":203207,"corporation":false,"usgs":true,"family":"Ourso","given":"Robert","email":"rtourso@usgs.gov","middleInitial":"T.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":476734,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70045041,"text":"sir20135047 - 2013 - An evaluation of seepage gains and losses in Indian Creek Reservoir, Ada County, Idaho, April 2010–November 2011","interactions":[],"lastModifiedDate":"2013-03-29T09:51:21","indexId":"sir20135047","displayToPublicDate":"2013-03-29T00:00:00","publicationYear":"2013","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":"2013-5047","title":"An evaluation of seepage gains and losses in Indian Creek Reservoir, Ada County, Idaho, April 2010–November 2011","docAbstract":"The U.S. Geological Survey, in cooperation with the Idaho Department of Water Resources, conducted an investigation on Indian Creek Reservoir, a small impoundment in east Ada County, Idaho, to quantify groundwater seepage into and out of the reservoir. Data from the study will assist the Idaho Water Resources Department’s Comprehensive Aquifer Management Planning effort to estimate available water resources in Ada County. Three independent methods were utilized to estimate groundwater seepage: (1) the water-budget method; (2) the seepage-meter method; and (3) the segmented Darcy method. Reservoir seepage was quantified during the periods of April through August 2010 and February through November 2011. With the water-budget method, all measureable sources of inflow to and outflow from the reservoir were quantified, with the exception of groundwater; the water-budget equation was solved for groundwater inflow to or outflow from the reservoir. The seepage-meter method relies on the placement of seepage meters into the bottom sediments of the reservoir for the direct measurement of water flux across the sediment-water interface. The segmented-Darcy method utilizes a combination of water-level measurements in the reservoir and in adjacent near-shore wells to calculate water-table gradients between the wells and the reservoir within defined segments of the reservoir shoreline. The Darcy equation was used to calculate groundwater inflow to and outflow from the reservoir. Water-budget results provided continuous, daily estimates of seepage over the full period of data collection, while the seepage-meter and segmented Darcy methods provided instantaneous estimates of seepage. As a result of these and other difference in methodologies, comparisons of seepage estimates provided by the three methods are considered semi-quantitative. The results of the water-budget derived estimates of seepage indicate seepage to be seasonally variable in terms of the direction and magnitude of flow. The reservoir tended to gain water from seepage of groundwater in the early spring months (March–May), while seepage losses to groundwater from the reservoir occurred in the drier months (June–October). Net monthly seepage rates, as computed by the water-budget method, varied greatly. Reservoir gains from seepage ranged from 0.2 to 59.4 acre-feet per month, while reservoir losses to seepage ranged from 1.6 and 26.8 acre-feet per month. An analysis of seepage meter estimates and segmented-Darcy estimates qualitatively supports the seasonal patterns in seepage provided by the water-budget calculations, except that they tended to be much smaller in magnitude. This suggests that actual seepage might be smaller than those estimates made by the water-budget method. Although the results of all three methods indicate that there is some water loss from the reservoir to groundwater, the seepage losses may be due to rewetting of unsaturated near-shore soils, possible replenishment of a perched aquifer, or both, rather than through percolation to the local aquifer that lies 130 feet below the reservoir. A lithologic log from an adjacent well indicates the existence of a clay lithology that is well correlated to the original reservoir’s base elevation. If the clay lithologic unit extends beneath the reservoir basin underlying the fine-grain reservoir bed sediments, the clay layer should act as an effective barrier to reservoir seepage to the local aquifer, which would explain the low seepage loss estimates calculated in this study.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135047","collaboration":"Prepared in cooperation with the Idaho Department of Water Resources","usgsCitation":"Williams, M.L., and Etheridge, A.B., 2013, An evaluation of seepage gains and losses in Indian Creek Reservoir, Ada County, Idaho, April 2010–November 2011: U.S. Geological Survey Scientific Investigations Report 2013-5047, Report: vi, 28 p.; 3 Appendices, https://doi.org/10.3133/sir20135047.","productDescription":"Report: vi, 28 p.; 3 Appendices","numberOfPages":"36","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-035919","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":270343,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135047.jpg"},{"id":270340,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5047/sir20135047_AppendixA.xlsx"},{"id":270341,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5047/sir20135047_AppendixB.xml"},{"id":270342,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5047/sir20135047_AppendixB_bathymetry.xyz"},{"id":270338,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5047/"},{"id":270339,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5047/pdf/sir20135047.pdf"}],"country":"United States","state":"Idaho","county":"Ada","otherGeospatial":"Indian Creek Reservoir","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -113.5,4.035555555555556 ], [ -113.5,0.0011111111111111111 ], [ -11.084444444444445,0.0011111111111111111 ], [ -11.084444444444445,4.035555555555556 ], [ -113.5,4.035555555555556 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5156a9cfe4b06ea905cdbfe2","contributors":{"authors":[{"text":"Williams, Marshall L. mlwilliams@usgs.gov","contributorId":1444,"corporation":false,"usgs":true,"family":"Williams","given":"Marshall","email":"mlwilliams@usgs.gov","middleInitial":"L.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":476687,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Etheridge, Alexandra B. 0000-0003-1282-7315 aetherid@usgs.gov","orcid":"https://orcid.org/0000-0003-1282-7315","contributorId":3542,"corporation":false,"usgs":true,"family":"Etheridge","given":"Alexandra","email":"aetherid@usgs.gov","middleInitial":"B.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":476688,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70045062,"text":"ds753 - 2013 - Golden eagle records from the Midwinter Bald Eagle Survey: information for wind energy management and planning","interactions":[],"lastModifiedDate":"2017-12-11T12:03:04","indexId":"ds753","displayToPublicDate":"2013-03-29T00:00:00","publicationYear":"2013","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":"753","title":"Golden eagle records from the Midwinter Bald Eagle Survey: information for wind energy management and planning","docAbstract":"The purpose of this Data Series report is to provide the occasions, locations, and counts when golden eagles were recorded during the annual Midwinter Bald Eagle Surveys. Golden eagles (Aquila chrysaetos) are protected by Federal statutes including the Bald and Golden Eagle Protection Act (BGEPA) (16 USC 668-668c) and the Migratory Bird Treaty Act (MBTA) (16 USC 703-12). The U.S. Fish and Wildlife Service (Service) manages golden eagles with the goal of maintaining stable or increasing breeding populations (U.S. Fish and Wildlife Service, 2009). Development for the generation of electricity from wind turbines is occurring in much of the range of the golden eagle in the western United States. Development could threaten population stability because golden eagles might be disturbed by construction and operation of facilities and they are vulnerable to mortality from collisions with wind turbines (Smallwood and Thelander, 2008). Therefore, the Service has proposed a process by which wind energy developers can collect information that could lead to Eagle Conservation Plans (ECP), mitigation, and permitting that allow for golden eagle management in areas of wind energy development (U.S. Fish and Wildlife Service, 2011). The Service recommends that ECP be developed in stages, and the first stage is to learn if golden eagles occur at the landscape level where potential wind facilities might be located. Information about where eagles occur can be obtained from technical literature, agency files, and other sources of information including on-line biological databases. The broad North American distribution of golden eagles is known, but there is a paucity of readily available information about intermediate geographic scales and site-specific scales, especially during the winter season (Kochert and others, 2002).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds753","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Eakle, W., Haggerty, P., Fuller, M., and Phillips, S.L., 2013, Golden eagle records from the Midwinter Bald Eagle Survey: information for wind energy management and planning: U.S. Geological Survey Data Series 753, HTML Document: Report, Figures 1-2, Tables 1-2, 4 Appendices, Metadata, https://doi.org/10.3133/ds753.","productDescription":"HTML Document: Report, Figures 1-2, Tables 1-2, 4 Appendices, Metadata","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-044369","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":270345,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds753.bmp"},{"id":270344,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/753/index.html"}],"country":"United States","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5156a9e5e4b06ea905cdbfea","contributors":{"authors":[{"text":"Eakle, Wade","contributorId":94178,"corporation":false,"usgs":true,"family":"Eakle","given":"Wade","email":"","affiliations":[],"preferred":false,"id":476715,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haggerty, Patti","contributorId":85856,"corporation":false,"usgs":true,"family":"Haggerty","given":"Patti","email":"","affiliations":[],"preferred":false,"id":476714,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fuller, Mark","contributorId":24660,"corporation":false,"usgs":true,"family":"Fuller","given":"Mark","affiliations":[],"preferred":false,"id":476713,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Phillips, Susan L. 0000-0002-5891-8485 sue_phillips@usgs.gov","orcid":"https://orcid.org/0000-0002-5891-8485","contributorId":717,"corporation":false,"usgs":true,"family":"Phillips","given":"Susan","email":"sue_phillips@usgs.gov","middleInitial":"L.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":476712,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70045064,"text":"tm4C3 - 2013 - Stochastic empirical loading and dilution model (SELDM) version 1.0.0","interactions":[],"lastModifiedDate":"2014-06-10T15:49:42","indexId":"tm4C3","displayToPublicDate":"2013-03-29T00:00:00","publicationYear":"2013","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":"4-C3","title":"Stochastic empirical loading and dilution model (SELDM) version 1.0.0","docAbstract":"The Stochastic Empirical Loading and Dilution Model (SELDM) is designed to transform complex scientific data into meaningful information about the risk of adverse effects of runoff on receiving waters, the potential need for mitigation measures, and the potential effectiveness of such management measures for reducing these risks. The U.S. Geological Survey developed SELDM in cooperation with the Federal Highway Administration to help develop planning-level estimates of event mean concentrations, flows, and loads in stormwater from a site of interest and from an upstream basin. Planning-level estimates are defined as the results of analyses used to evaluate alternative management measures; planning-level estimates are recognized to include substantial uncertainties (commonly orders of magnitude). SELDM uses information about a highway site, the associated receiving-water basin, precipitation events, stormflow, water quality, and the performance of mitigation measures to produce a stochastic population of runoff-quality variables. SELDM provides input statistics for precipitation, prestorm flow, runoff coefficients, and concentrations of selected water-quality constituents from National datasets. Input statistics may be selected on the basis of the latitude, longitude, and physical characteristics of the site of interest and the upstream basin. The user also may derive and input statistics for each variable that are specific to a given site of interest or a given area. SELDM is a stochastic model because it uses Monte Carlo methods to produce the random combinations of input variable values needed to generate the stochastic population of values for each component variable. SELDM calculates the dilution of runoff in the receiving waters and the resulting downstream event mean concentrations and annual average lake concentrations. Results are ranked, and plotting positions are calculated, to indicate the level of risk of adverse effects caused by runoff concentrations, flows, and loads on receiving waters by storm and by year. Unlike deterministic hydrologic models, SELDM is not calibrated by changing values of input variables to match a historical record of values. Instead, input values for SELDM are based on site characteristics and representative statistics for each hydrologic variable. Thus, SELDM is an empirical model based on data and statistics rather than theoretical physiochemical equations. SELDM is a lumped parameter model because the highway site, the upstream basin, and the lake basin each are represented as a single homogeneous unit. Each of these source areas is represented by average basin properties, and results from SELDM are calculated as point estimates for the site of interest. Use of the lumped parameter approach facilitates rapid specification of model parameters to develop planning-level estimates with available data. The approach allows for parsimony in the required inputs to and outputs from the model and flexibility in the use of the model. For example, SELDM can be used to model runoff from various land covers or land uses by using the highway-site definition as long as representative water quality and impervious-fraction data are available.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section C: Water Quality in Book 4 Hydrologic Analysis and Interpretation","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm4C3","collaboration":"Prepared in cooperation with the Department of Transportation Federal Highway Administration, Office of Project Development and Environmental Review. This report is Chapter 3 of Section C: Water Quality in Book 4 Hydrologic Analysis and Interpretation","usgsCitation":"Granato, G., 2013, Stochastic empirical loading and dilution model (SELDM) version 1.0.0: U.S. Geological Survey Techniques and Methods 4-C3, Manual: xii, 112 p.; 5 Appendices; Digital Media Directory, https://doi.org/10.3133/tm4C3.","productDescription":"Manual: xii, 112 p.; 5 Appendices; Digital Media Directory","numberOfPages":"124","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":270337,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/tm4C3.jpg"},{"id":270332,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/04/c03/tm4-C3_final_508_files/tm4-C3_apdx1_v030813.pdf"},{"id":270330,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/tm/04/c03/"},{"id":270333,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/04/c03/tm4-C3_final_508_files/tm4-C3_apdx2_v030813.pdf"},{"id":270331,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/c03/tm4-C3_final_508_files/tm4-C3_main_v031913.pdf"},{"id":270334,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/04/c03/tm4-C3_final_508_files/tm4-C3_apdx3_pages_v030813.pdf"},{"id":270335,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/04/c03/tm4-C3_final_508_files/tm4-C3_apdx4_v030813.pdf"},{"id":270336,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/tm/04/c03/virtual_CD/index.html"}],"country":"United States","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5156a9ece4b06ea905cdc006","contributors":{"authors":[{"text":"Granato, Gregory E. 0000-0002-2561-9913 ggranato@usgs.gov","orcid":"https://orcid.org/0000-0002-2561-9913","contributorId":1692,"corporation":false,"usgs":true,"family":"Granato","given":"Gregory E.","email":"ggranato@usgs.gov","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":false,"id":476716,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70045065,"text":"sir20135015 - 2013 - Characterization and data-gap analysis of surface-water quality data in the Piceance study area, western Colorado, 1959–2009","interactions":[],"lastModifiedDate":"2013-03-29T10:23:41","indexId":"sir20135015","displayToPublicDate":"2013-03-29T00:00:00","publicationYear":"2013","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":"2013-5015","title":"Characterization and data-gap analysis of surface-water quality data in the Piceance study area, western Colorado, 1959–2009","docAbstract":"The U.S. Geological Survey, in cooperation with Federal, State, county, and industry partners, developed a Web-accessible common data repository to provide access to historical and current (as of August 2009) water-quality information (available on the Internet at http://rmgsc.cr.usgs.gov/cwqdr/Piceance/index.shtml). Surface-water-quality data from public and private sources were compiled for the period 1931 to 2009 and loaded into the common data repository for the Piceance Basin. A subset of surface-water-quality data for 1959 to 2009 from the repository were compiled, reviewed, and checked for quality assurance for this report. This report contains data summaries, comparisons to water-quality standards, trend analyses, a generalized spatial analysis, and a data-gap analysis for select water-quality properties and constituents. Summary statistics and a comparison to standards were provided for 347 sites for 33 constituents including field properties, nutrients, major ions, trace elements, suspended sediment, Escherichia coli, and BTEX (benzene, toluene, ethylbenzene, and xylene). When sufficient data were available, trends over time were analyzed and loads were calculated for those sites where there were also continuous streamflow data. The majority of sites had information on field properties. Water temperature data was available for 316 sites where data were collected between 1959 and 2009. The only trend that was detected in temperature was an upward trend at the Gunnison River near Grand Junction, Colorado. There were 326 values out of a total of 32,006 values in the study area that exceeded the aquatic-life standard for daily maximum water temperature. For the entire study area, 196 sites had dissolved-oxygen data collected between 1970 and 2009, and median dissolved-oxygen concentrations ranged from 6.8 to11.2 milligrams per liter (mg/L). There were 185 concentrations that exceeded the dissolved oxygen aquatic-life standard out of a total of 11,248 values. The pH data were available for 276 sites, and median pH values ranged from 7.5 to 9.0. There were 241 values that exceeded the high pH standard and 13 values that were less than the low pH standard of the 16,790 values in the study area. Nutrients within the study area were not well represented in each basin and were often not being sampled currently. For the entire study area, 62 sites had nitrate data collected between 1958 and 2009, and median nitrate concentrations ranged from less than detection to 3.72 mg/L as nitrogen. The maximum contaminant level for domestic water supply for nitrate is 10 mg/L and was exceeded once in 3,736 samples. Total phosphorus was collected at 113 sites between 1974 and 2009, and median total phosphorus concentrations ranged from less than detection to 5.04 mg/L. The U.S. Environmental Protection Agency recommendation for phosphorus is less than 0.1 mg/L, and 1,469 of 4,842 samples exceeded this recommended standard. An upward trend in both nitrate and total phosphorus was detected in the White River above Coal Creek near Meeker, Colo. Standards for major ions exist only for chloride and sulfate. For the entire study area, 118 sites had both chloride and sulfate concentration data collected between 1958 and 2009. Median chloride concentrations ranged from 0.085 mg/L to 280 mg/L. Median sulfate concentrations ranged from 4.57 mg/L to 15,000 mg/L. Both chloride and sulfate domestic water-supply standards are 250 mg/L. There were 120 chloride concentrations and 1,111 sulfate concentration samples that exceeded these standards. A downward trend in dissolved solids was detected at the Colorado River near the Colorado-Utah state border and could be a result of salinity control work near Grand Junction, Colo. Trace elements were relatively well represented both temporally and spatially in the study area though the number of trace element samples per site was not typically enough to compute trends or loads except for selenium. There were 127 sites that had dissolved iron concentration data collected between 1961 and 2009, and median iron concentrations ranged from less than detection to 1,100 micrograms per liter (µg/L). The 30-day drinking-water standard for iron is 300 µg/L, and 203 samples exceeded the standard. Selenium was the best represented trace element with selenium concentration data collected at 197 sites between 1973 and 2009, and median selenium concentrations range from less than detection to 181 µg/L. The chronic standard of 4.6 µg/L for selenium concentrations was exceeded in 899 samples, and the acute aquatic-life standard of 18.4 µg/ for selenium was exceeded in 629 samples. High concentrations of selenium are of concern in the Lower Gunnison River Basin because of the combination of geologic formations and land use. There were significant downward trends in selenium at both main-stem sites on the Gunnison River at Delta, Colo., and the Gunnison River near Grand Junction, Colo. High selenium concentrations correlate with high salinity concentrations; thus, when salinity control efforts are conducted in selenium-rich areas in the Lower Gunnison River Basin, both salinity and selenium have the potential to decrease. Spatial, temporal, and analytical data gaps were identified in the study area. The spatial coverage of sampling sites could be expanded in the White River Basin by adding more tributary sites. No water-quality data exist for tributary streams in the area north of Rangely, Colo., where extensive energy development has occurred in a complex geologic setting. Douglas Creek has a drainage area of 425 square miles and has limited historic water-quality and water-quantity data. Limited data were available for field properties, major ions, nutrients, and trace elements on the main stem of the Colorado River between Glenwood Springs and Cameo, Colo. Nutrient data were minimally collected upstream from Colorado River at the Colorado-Utah state border and on the Gunnison River (major tributary in the reach). Approximately 30 percent of the samples for total phosphorus in the Lower Gunnison River Basin exceeded the recommended standard, yet there were insufficient data to do trends analysis in the Lower Gunnison River Basin except at the Gunnison near Grand Junction site. There is limited trace element data except for selenium in the Lower Gunnison River Basin. Additional sampling is necessary to understand the occurrence, concentrations, and loads of these constituents.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135015","collaboration":"Prepared in cooperation with (in alphabetical order) Antero Resources, Bureau of Land Management, Bureau of Reclamation, Chevron Corporation, City of Grand Junction and City of Rifle, Colo., Colorado Department of Agriculture, Colorado Department of Natural Resources, Colorado Department of Public Health and Environment, Colorado Division of Wildlife–River Watch, Colorado Oil and Gas Conservation Commission, Colorado River Water Conservation District, Delta County, Colo., EnCana Oil & Gas (USA) Inc., Garfield County, Colo., Gunnison Energy Corp., National Park Service, Natural Soda, Inc., North Fork River Improvement Association, Oxy Petroleum Corporation, Petroleum Development Corp., Rio Blanco County, Shell Oil Company, Solvay Chemicals, Towns of Carbondale, De Beque, Palisade, Parachute, Rangely, and Silt, Colo., U.S. Forest Service, West Divide Water Conservancy District, and Williams Companies, Inc.","usgsCitation":"Thomas, J.C., Moore, J.L., Schaffrath, K.R., Dupree, J.A., Williams, C.A., and Leib, K.J., 2013, Characterization and data-gap analysis of surface-water quality data in the Piceance study area, western Colorado, 1959–2009: U.S. Geological Survey Scientific Investigations Report 2013-5015, Report: ix, 74 p.; 2 Appendices, https://doi.org/10.3133/sir20135015.","productDescription":"Report: ix, 74 p.; 2 Appendices","numberOfPages":"79","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":270349,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135015.gif"},{"id":270348,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5015/appendix"},{"id":270346,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5015/"},{"id":270347,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5015/sir2013-5015.pdf"}],"country":"United States","state":"Colorado","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -109.33,38 ], [ -109.33,40.5 ], [ -107,40.5 ], [ -107,38 ], [ -109.33,38 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5156a9e5e4b06ea905cdbfe6","contributors":{"authors":[{"text":"Thomas, Judith C. 0000-0001-7883-1419 juthomas@usgs.gov","orcid":"https://orcid.org/0000-0001-7883-1419","contributorId":1468,"corporation":false,"usgs":true,"family":"Thomas","given":"Judith","email":"juthomas@usgs.gov","middleInitial":"C.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":476719,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Jennifer L.","contributorId":68447,"corporation":false,"usgs":true,"family":"Moore","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":476722,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schaffrath, Keelin R.","contributorId":7552,"corporation":false,"usgs":true,"family":"Schaffrath","given":"Keelin","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":476721,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dupree, Jean A. dupree@usgs.gov","contributorId":2563,"corporation":false,"usgs":true,"family":"Dupree","given":"Jean","email":"dupree@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":476720,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Williams, Cory A. 0000-0003-1461-7848 cawillia@usgs.gov","orcid":"https://orcid.org/0000-0003-1461-7848","contributorId":689,"corporation":false,"usgs":true,"family":"Williams","given":"Cory","email":"cawillia@usgs.gov","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":476717,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Leib, Kenneth J. 0000-0002-0373-0768 kjleib@usgs.gov","orcid":"https://orcid.org/0000-0002-0373-0768","contributorId":701,"corporation":false,"usgs":true,"family":"Leib","given":"Kenneth","email":"kjleib@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":476718,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70045067,"text":"sir20125248 - 2013 - Status and understanding of groundwater quality in the San Francisco Bay groundwater basins, 2007—California GAMA Priority Basin Project","interactions":[],"lastModifiedDate":"2013-03-29T11:19:06","indexId":"sir20125248","displayToPublicDate":"2013-03-29T00:00:00","publicationYear":"2013","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":"2012-5248","title":"Status and understanding of groundwater quality in the San Francisco Bay groundwater basins, 2007—California GAMA Priority Basin Project","docAbstract":"Groundwater quality in the approximately 620-square-mile (1,600-square-kilometer) San Francisco Bay study unit was investigated as part of the Priority Basin Project of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is located in the Southern Coast Ranges of California, in San Francisco, San Mateo, Santa Clara, Alameda, and Contra Costa Counties. The GAMA Priority Basin Project is being conducted by the California State Water Resources Control Board in collaboration with the U.S. Geological Survey (USGS) and the Lawrence Livermore National Laboratory. The GAMA San Francisco Bay study was designed to provide a spatially unbiased assessment of the quality of untreated groundwater within the primary aquifer system, as well as a statistically consistent basis for comparing water quality throughout the State. The assessment is based on water-quality and ancillary data collected by the USGS from 79 wells in 2007 and is supplemented with water-quality data from the California Department of Public Health (CDPH) database. The primary aquifer system is defined by the depth interval of the wells listed in the CDPH database for the San Francisco Bay study unit. The quality of groundwater in shallower or deeper water-bearing zones may differ from that in the primary aquifer system; shallower groundwater may be more vulnerable to surficial contamination. The first component of this study, the status of the current quality of the groundwater resource, was assessed by using data from samples analyzed for volatile organic compounds (VOCs), pesticides, and naturally occurring inorganic constituents, such as major ions and trace elements. Water- quality data from the CDPH database also were incorporated for this assessment. This status assessment is intended to characterize the quality of groundwater resources within the primary aquifer system of the San Francisco Bay study unit, not the treated drinking water delivered to consumers by water purveyors.\n Relative-concentrations (sample concentration divided by the benchmark concentration) were used for evaluating groundwater quality for those constituents that have Federal and (or) California benchmarks. A relative-concentration greater than (>) 1.0 indicates a concentration greater than a benchmark, and a relative-concentration less than or equal to (≤) 1.0 indicates a concentration equal to or less than a benchmark. Relative-concentrations of organic and special-interest constituents were classified as low (relative- concentration ≤ 0.1), moderate (0.1 < relative- concentration ≤ 1.0), or high (relative-concentration > 1.0). Inorganic constituent relative- concentrations were classified as low (relative-concentration ≤ 0.5), moderate (0.5 < relative-concentration ≤ 1.0), or high (relative- concentration > 1.0). A lower threshold value of relative-concentration was used to distinguish between low and moderate values of organic constituents because organic constituents are generally less prevalent and have smaller relative-concentrations than naturally occurring inorganic constituents. Aquifer-scale proportion was used as the metric for evaluating regional-scale groundwater quality. High aquifer-scale proportion is defined as the percentage of the primary aquifer system that has relative-concentration greater than 1.0 for a particular constituent or class of constituents; proportion is based on an areal rather than a volumetric basis. Moderate and low aquifer-scale proportions were defined as the percentages of the primary aquifer system that have moderate and low relative-concentrations, respectively. Two statistical approaches—grid-based and spatially weighted—were used to evaluate aquifer-scale proportion for individual constituents and classes of constituents. Grid-based and spatially weighted estimates were comparable in the San Francisco Bay study unit (90-percent confidence intervals). Inorganic constituents with health-based benchmarks were present at high relative-concentrations in 5.1 percent of the primary aquifer system, and at moderate relative-concentrations in 25 percent. The high aquifer-scale proportion of inorganic constituents primarily reflected high aquifer-scale proportions of barium (3.0 percent) and nitrate (2.1 percent). Inorganic constituents with secondary maximum contaminant levels were present at high relative-concentrations in 14 percent of the primary aquifer system and at moderate relative-concentrations in 33 percent. The constituents present at high relative-concentrations included total dissolved solids (7.0 percent), chloride (6.1 percent), manganese (12 percent), and iron (3.0 percent). Organic constituents with health-based benchmarks were present at high relative-concentrations in 0.6 percent and at moderate relative-concentrations in 12 percent of the primary aquifer system. Of the 202 organic constituents analyzed for, 32 were detected. Three organic constituents were frequently detected (in 10 percent or more of samples): the trihalomethane chloroform, the solvent 1,1,1-trichloroethane and the refrigerant 1,1,2-trichlorotrifluoroethane. One special-interest constituent, perchlorate, was detected at moderate relative-concentrations in 42 percent of the primary aquifer system. The second component of this work, the understanding assessment, identified some of the primary natural and human factors that may affect groundwater quality by evaluating land use, physical characteristics of the wells, and geochemical conditions of the aquifer. Results from these evaluations were used to explain the occurrence and distribution of constituents in the study unit.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125248","collaboration":"Prepared in cooperation with the California State Water Resources Control Board A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program","usgsCitation":"Parsons, M.C., Kulongoski, J., and Belitz, K., 2013, Status and understanding of groundwater quality in the San Francisco Bay groundwater basins, 2007—California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2012-5248, x, 78 p., https://doi.org/10.3133/sir20125248.","productDescription":"x, 78 p.","numberOfPages":"90","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":270354,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20125248.jpg"},{"id":270352,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5248/"},{"id":270353,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5248/pdf/sir20125248.pdf"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.01055555555556,8.333333333333334E-4 ], [ -122.01055555555556,8.333333333333334E-4 ], [ -0.01611111111111111,8.333333333333334E-4 ], [ -0.01611111111111111,8.333333333333334E-4 ], [ -122.01055555555556,8.333333333333334E-4 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5156a9ebe4b06ea905cdc002","contributors":{"authors":[{"text":"Parsons, Mary C. mparsons@usgs.gov","contributorId":1571,"corporation":false,"usgs":true,"family":"Parsons","given":"Mary","email":"mparsons@usgs.gov","middleInitial":"C.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":476724,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":94750,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin T.","affiliations":[],"preferred":false,"id":476725,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Belitz, Kenneth 0000-0003-4481-2345 kbelitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":442,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","email":"kbelitz@usgs.gov","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":476723,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70045073,"text":"sir20135049 - 2013 - Shallow groundwater in the Matanuska-Susitna Valley, Alaska—Conceptualization and simulation of flow","interactions":[],"lastModifiedDate":"2018-07-18T13:50:39","indexId":"sir20135049","displayToPublicDate":"2013-03-29T00:00:00","publicationYear":"2013","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":"2013-5049","title":"Shallow groundwater in the Matanuska-Susitna Valley, Alaska—Conceptualization and simulation of flow","docAbstract":"The Matanuska-Susitna Valley is in the Upper Cook Inlet Basin and is currently undergoing rapid population growth outside of municipal water and sewer service areas. In response to concerns about the effects of increasing water use on future groundwater availability, a study was initiated between the Alaska Department of Natural Resources and the U.S. Geological Survey. The goals of the study were (1) to compile existing data and collect new data to support hydrogeologic conceptualization of the study area, and (2) to develop a groundwater flow model to simulate flow dynamics important at the regional scale. The purpose of the groundwater flow model is to provide a scientific framework for analysis of regional-scale groundwater availability. To address the first study goal, subsurface lithologic data were compiled into a database and were used to construct a regional hydrogeologic framework model describing the extent and thickness of hydrogeologic units in the Matanuska-Susitna Valley. The hydrogeologic framework model synthesizes existing maps of surficial geology and conceptual geochronologies developed in the study area with the distribution of lithologies encountered in hundreds of boreholes. The geologic modeling package Geological Surveying and Investigation in Three Dimensions (GSI3D) was used to construct the hydrogeologic framework model. In addition to characterizing the hydrogeologic framework, major groundwater-budget components were quantified using several different techniques. A land-surface model known as the Deep Percolation Model was used to estimate in-place groundwater recharge across the study area. This model incorporates data on topography, soils, vegetation, and climate. Model-simulated surface runoff was consistent with observed streamflow at U.S. Geological Survey streamgages. Groundwater withdrawals were estimated on the basis of records from major water suppliers during 2004-2010. Fluxes between groundwater and surface water were estimated during field investigations on several small streams. Regional groundwater flow patterns were characterized by synthesizing previous water-table maps with a synoptic water-level measurement conducted during 2009. Time-series water-level data were collected at groundwater and lake monitoring stations over the study period (2009–present). Comparison of historical groundwater-level records with time-series groundwater-level data collected during this study showed similar patterns in groundwater-level fluctuation in response to precipitation. Groundwater-age data collected during previous studies show that water moves quickly through the groundwater system, suggesting that the system responds quickly to changes in climate forcing. Similarly, the groundwater system quickly returns to long-term average conditions following variability due to seasonal or interannual changes in precipitation. These analyses indicate that the groundwater system is in a state of dynamic equilibrium, characterized by water-level fluctuation about a constant average state, with no long-term trends in aquifer-system storage. To address the second study goal, a steady-state groundwater flow model was developed to simulate regional groundwater flow patterns. The groundwater flow model was bounded by physically meaningful hydrologic features, and appropriate internal model boundaries were specified on the basis of conceptualization of the groundwater system resulting in a three-layer model. Calibration data included 173 water‑level measurements and 18 measurements of streamflow gains and losses along small streams. Comparison of simulated and observed heads and flows showed that the model accurately simulates important regional characteristics of the groundwater flow system. This model is therefore appropriate for studying regional-scale groundwater availability. Mismatch between model-simulated and observed hydrologic quantities is likely because of the coarse grid size of the model and seasonal transient effects. Next steps towards model refinement include the development of a transient groundwater flow model that is suitable for analysis of seasonal variability in hydraulic heads and flows. In addition, several important groundwater budget components remain poorly quantified—including groundwater outflow to the Matanuska River, Little Susitna River, and Knik Arm.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135049","collaboration":"Prepared in cooperation with the Alaska Department of Natural Resources","usgsCitation":"Kikuchi, C.P., 2013, Shallow groundwater in the Matanuska-Susitna Valley, Alaska—Conceptualization and simulation of flow: U.S. Geological Survey Scientific Investigations Report 2013-5049, Report: viii, 86 p.; 4 Appendices, https://doi.org/10.3133/sir20135049.","productDescription":"Report: viii, 86 p.; 4 Appendices","numberOfPages":"96","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":270376,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135049.jpg"},{"id":270372,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5049/sir20135049_appendixA.xlsx"},{"id":270373,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5049/sir20135049_appendixB.xlsx"},{"id":270374,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5049/sir20135049_appendixC.xlsx"},{"id":270375,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5049/sir20135049_appendixD.xlsx"},{"id":270370,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5049/"},{"id":270371,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5049/pdf/sir20135049.pdf"}],"country":"United States","state":"Alaska","otherGeospatial":"Matanuska-susitna Valley","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5156a9e9e4b06ea905cdbff6","contributors":{"authors":[{"text":"Kikuchi, Colin P.","contributorId":61311,"corporation":false,"usgs":true,"family":"Kikuchi","given":"Colin","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":476735,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70045096,"text":"sir20135046 - 2013 - Multi-regional synthesis of temporal trends in biotic assemblages in streams and rivers of the continental United States","interactions":[],"lastModifiedDate":"2025-04-01T20:07:42.570685","indexId":"sir20135046","displayToPublicDate":"2013-03-29T00:00:00","publicationYear":"2013","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":"2013-5046","title":"Multi-regional synthesis of temporal trends in biotic assemblages in streams and rivers of the continental United States","docAbstract":"Biotic assemblages in aquatic ecosystems are excellent integrators and indicators of changing environmental conditions within a watershed. Therefore, temporal changes in abiotic environmental variables often can be inferred from temporal changes in biotic assemblages. Algae, macroinvertebrate, and fish assemblage data were collected from 91 sampling sites in 4 geographic regions (northeastern/north-central, southeastern, south-central, and western), collectively encompassing the continental United States, from 1993 to 2009 as part of the U.S. Geological Survey National Water-Quality Assessment Program. This report uses a multivariate approach to synthesize temporal trends in biotic assemblages and correlations with relevant abiotic parameters as a function of biotic assemblage, geographic region, and land use. Of the three groups of biota, algal assemblages had temporal trends at the greatest percentage of sites. Of the regions, a greater percentage of sites in the northeastern/north-central and western regions had temporal trends in biotic assemblages. In terms of land use, a greater percentage of watersheds draining agricultural, urban, and undeveloped areas had significant temporal changes in biota, as compared to watersheds with mixed use. Correlations between biotic assemblages and abiotic variables indicate that, in general, macroinvertebrate assemblages correlated with water quality and fish assemblages correlated with physical habitat. Taken together, results indicate that there are regional differences in how individual biotic assemblages (algae, macroinvertebrates, and fish) respond to different abiotic drivers of change.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135046","collaboration":"National Water-Quality Assessment (NAWQA) Program","usgsCitation":"Miller, M.P., Brasher, A., and Keenen, J.G., 2013, Multi-regional synthesis of temporal trends in biotic assemblages in streams and rivers of the continental United States: U.S. Geological Survey Scientific Investigations Report 2013-5046, vi, 22 p., https://doi.org/10.3133/sir20135046.","productDescription":"vi, 22 p.","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":610,"text":"Utah Water Science 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G.","contributorId":16292,"corporation":false,"usgs":true,"family":"Keenen","given":"Jonathan","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":476781,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70045083,"text":"sir20135043 - 2013 - Statistical classification of hydrogeologic regions in the fractured rock area of Maryland and parts of the District of Columbia, Virginia, West Virginia, Pennsylvania, and Delaware","interactions":[],"lastModifiedDate":"2023-03-09T20:13:46.600467","indexId":"sir20135043","displayToPublicDate":"2013-03-29T00:00:00","publicationYear":"2013","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":"2013-5043","title":"Statistical classification of hydrogeologic regions in the fractured rock area of Maryland and parts of the District of Columbia, Virginia, West Virginia, Pennsylvania, and Delaware","docAbstract":"Hydrogeologic regions in the fractured rock area of Maryland were classified using geographic information system tools with principal components and cluster analyses. A study area consisting of the 8-digit Hydrologic Unit Code (HUC) watersheds with rivers that flow through the fractured rock area of Maryland and bounded by the Fall Line was further subdivided into 21,431 catchments from the National Hydrography Dataset Plus. The catchments were then used as a common hydrologic unit to compile relevant climatic, topographic, and geologic variables. A principal components analysis was performed on 10 input variables, and 4 principal components that accounted for 83 percent of the variability in the original data were identified. A subsequent cluster analysis grouped the catchments based on four principal component scores into six hydrogeologic regions. Two crystalline rock hydrogeologic regions, including large parts of the Washington, D.C. and Baltimore metropolitan regions that represent over 50 percent of the fractured rock area of Maryland, are distinguished by differences in recharge, Precipitation minus Potential Evapotranspiration, sand content in soils, and groundwater contributions to streams. This classification system will provide a georeferenced digital hydrogeologic framework for future investigations of groundwater availability in the fractured rock area of Maryland.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135043","collaboration":"Prepared in cooperation with the Maryland Department of the Environment","usgsCitation":"Fleming, B.J., LaMotte, A.E., and Sekellick, A.J., 2013, Statistical classification of hydrogeologic regions in the fractured rock area of Maryland and parts of the District of Columbia, Virginia, West Virginia, Pennsylvania, and Delaware: U.S. Geological Survey Scientific Investigations Report 2013-5043, Report: vi, 16 p.; Additional Information, https://doi.org/10.3133/sir20135043.","productDescription":"Report: vi, 16 p.; Additional Information","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":270388,"rank":4,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135043.gif"},{"id":270387,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/sir/2013/5043/frac_rx_HRs.csv"},{"id":270386,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5043/pdf/sir2013-5043.pdf"},{"id":270385,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5043/"}],"scale":"100000","datum":"North American Datum 1983","country":"United States","state":"Delaware;District of Columbia;Maryl;Pennsylvania;Virginia;West Virginia","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -80,3.118888888888889 ], [ -80,0.0011111111111111111 ], [ -75,0.0011111111111111111 ], [ -75,3.118888888888889 ], [ -80,3.118888888888889 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5156a9eae4b06ea905cdbffe","contributors":{"authors":[{"text":"Fleming, Brandon J. 0000-0001-9649-7485 bjflemin@usgs.gov","orcid":"https://orcid.org/0000-0001-9649-7485","contributorId":4115,"corporation":false,"usgs":true,"family":"Fleming","given":"Brandon","email":"bjflemin@usgs.gov","middleInitial":"J.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":476759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"LaMotte, Andrew E. 0000-0002-1434-6518 alamotte@usgs.gov","orcid":"https://orcid.org/0000-0002-1434-6518","contributorId":2842,"corporation":false,"usgs":true,"family":"LaMotte","given":"Andrew","email":"alamotte@usgs.gov","middleInitial":"E.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":476758,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sekellick, Andrew J. 0000-0002-0440-7655 ajsekell@usgs.gov","orcid":"https://orcid.org/0000-0002-0440-7655","contributorId":4125,"corporation":false,"usgs":true,"family":"Sekellick","given":"Andrew","email":"ajsekell@usgs.gov","middleInitial":"J.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":476760,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70045074,"text":"sir20135036 - 2013 - Simulation of salinity intrusion along the Georgia and South Carolina coasts using climate-change scenarios","interactions":[],"lastModifiedDate":"2017-01-18T13:12:10","indexId":"sir20135036","displayToPublicDate":"2013-03-29T00:00:00","publicationYear":"2013","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":"2013-5036","title":"Simulation of salinity intrusion along the Georgia and South Carolina coasts using climate-change scenarios","docAbstract":"Potential changes in climate could alter interactions between environmental and societal systems and adversely affect the availability of water resources in many coastal communities. Changes in streamflow patterns in conjunction with sea-level rise may change the salinity-intrusion dynamics of coastal rivers. Several municipal water-supply intakes are located along the Georgia and South Carolina coast that are proximal to the present day saltwater-freshwater interface of tidal rivers. Increases in the extent of salinity intrusion resulting from climate change could threaten the availability of freshwater supplies in the vicinity of these intakes. To effectively manage these supplies, water-resource managers need estimates of potential changes in the frequency, duration, and magnitude of salinity intrusion near their water-supply intakes that may occur as a result of climate change. This study examines potential effects of climate change, including altered streamflow and sea-level rise, on the dynamics of saltwater intrusion near municipal water-supply intakes in two coastal areas. One area consists of the Atlantic Intracoastal Waterway (AIW) and the Waccamaw River near Myrtle Beach along the Grand Strand of the South Carolina Coast, and the second area is on or near the lower Savannah River near Savannah, Georgia. The study evaluated how future sea-level rise and a reduction in streamflows can potentially affect salinity intrusion and threaten municipal water supplies and the biodiversity of freshwater tidal marshes in these two areas. Salinity intrusion occurs as a result of the interaction between three principal forces—streamflow, mean coastal water levels, and tidal range. To analyze and simulate salinity dynamics at critical coastal gaging stations near four municipal water-supply intakes, various data-mining techniques, including artificial neural network (ANN) models, were used to evaluate hourly streamflow, salinity, and coastal water-level data collected over a period exceeding 10 years. The ANN models were trained (calibrated) to learn the specific interactions that cause salinity intrusions, and resulting models were able to accurately simulate historical salinity dynamics in both study areas. Changes in sea level and streamflow quantity and timing can be simulated by the salinity intrusion models to evaluate various climate-change scenarios. The salinity intrusion models for the study areas are deployed in a decision support system to facilitate the use of the models for management decisions by coastal water-resource managers. The report describes the use of the salinity-intrusion models decision support system to evaluate salinity-intrusion dynamics for various climate-change scenarios, including incremental increases in sea level in combination with incremental decreases in streamflow. Operation of municipal water-treatment plants is problematic when the specific-conductance values for source water are greater than 1,000 to 2,000 microsiemens per centimeter (µS/cm). High specific-conductance values contribute to taste problems that require treatment. Data from a gage downstream from a municipal water intake indicate specific conductance exceeded 1,000 µS/cm about 5.4 percent of the time over the 14-year period from August 1995 to August 2008. Simulations of specific conductance at this gaging station that incorporates sea-level rises resulted in a doubling of the exceedances to 11.0 percent for a 1-foot increase and 17.6 percent for a 2-foot increase. The frequency of intrusion of water with specific conductance values of 1,000 µS/cm was less sensitive to incremental reductions in streamflow than to incremental increases in sea level. Simulations of conditions associated with a 10-percent reduction in streamflow, in combination with a 1-foot rise in sea level, increased the percentage of time specific conductance exceeded 1,000 µS/cm at this site from 11.0 to 13.3 percent, and a 20-percent reduction in streamflow increased the percentage of time to 16.6 percent. Precipitation and temperature data from a global circulation model were used, after scale adjustments, as input to a watershed model of the Yadkin-Pee Dee River basin, which flows into the Waccamaw River and Atlantic Intracoastal Waterway study area in South Carolina. The simulated streamflow for historical conditions and projected climate change in the future was used as input for the ANN model in decision support system. Results of simulations incorporating climate-change projections for alterations in streamflow indicate an increase in the frequency of salinity-intrusion events and a shift in the seasonal occurrence of the intrusion events from the summer to the fall.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135036","collaboration":"Prepared in cooperation with the Beaufort-Jasper Water and Sewer Authority","usgsCitation":"Conrads, P., Roehl, E.A., Daamen, R.C., and Cook, J., 2013, Simulation of salinity intrusion along the Georgia and South Carolina coasts using climate-change scenarios: U.S. Geological Survey Scientific Investigations Report 2013-5036, Report: xix, 94 p.; 5 Appendices, https://doi.org/10.3133/sir20135036.","productDescription":"Report: xix, 94 p.; 5 Appendices","numberOfPages":"110","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":13634,"text":"South Atlantic Water Science 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\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5156a9eae4b06ea905cdbffa","contributors":{"authors":[{"text":"Conrads, Paul 0000-0003-0408-4208 pconrads@usgs.gov","orcid":"https://orcid.org/0000-0003-0408-4208","contributorId":764,"corporation":false,"usgs":true,"family":"Conrads","given":"Paul","email":"pconrads@usgs.gov","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":false,"id":476736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roehl, Edwin A. Jr.","contributorId":108083,"corporation":false,"usgs":false,"family":"Roehl","given":"Edwin","suffix":"Jr.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":476739,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Daamen, Ruby C.","contributorId":105391,"corporation":false,"usgs":true,"family":"Daamen","given":"Ruby","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":476738,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cook, John B.","contributorId":45594,"corporation":false,"usgs":true,"family":"Cook","given":"John B.","affiliations":[],"preferred":false,"id":476737,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70045019,"text":"70045019 - 2013 - The NAS Alert System: A look at the first eight years","interactions":[],"lastModifiedDate":"2017-05-03T10:37:21","indexId":"70045019","displayToPublicDate":"2013-03-28T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1657,"text":"Fisheries","onlineIssn":"1548-8446","printIssn":"0363-2415","active":true,"publicationSubtype":{"id":10}},"title":"The NAS Alert System: A look at the first eight years","docAbstract":"The U.S. Geological Survey's Nonindigenous Aquatic Species (NAS) database program (http://nas.er.usgs.gov) tracks the distribution of introduced aquatic organisms across the United States. Awareness of, and timely response to, novel species introductions by those involved in nonindigenous aquatic species management and research requires a framework for rapid dissemination of occurrence data as it is incorporated into the NAS database. In May 2004, the NAS program developed an alert system to notify registered users of new introductions as part of a national early detection/rapid response system. This article summarizes information on system users and dispatched alerts from the system's inception through the end of 2011. The NAS alert system has registered over 1,700 users, with approximately 800 current subscribers. A total of 1,189 alerts had been transmitted through 2011. More alerts were sent for Florida (134 alerts) than for any other state. Fishes comprise the largest taxonomic group of alerts (440), with mollusks, plants, and crustaceans each containing over 100 alerts. Most alerts were for organisms that were intentionally released (414 alerts), with shipping, escape from captivity, and hitchhiking also representing major vectors. To explore the archive of sent alerts and to register, the search and signup page for the alert system can be found online at http://nas.er.usgs.gov/AlertSystem/default.aspx.","language":"English","publisher":"Taylor & Francis","publisherLocation":"London, UK","doi":"10.1080/03632415.2013.767241","usgsCitation":"Fuller, P.L., Neilson, M., and Huge, D.H., 2013, The NAS Alert System: A look at the first eight years: Fisheries, v. 38, no. 3, p. 128-138, https://doi.org/10.1080/03632415.2013.767241.","productDescription":"11 p.","startPage":"128","endPage":"138","ipdsId":"IP-021071","costCenters":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"links":[{"id":270329,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"38","issue":"3","noUsgsAuthors":false,"publicationDate":"2013-03-21","publicationStatus":"PW","scienceBaseUri":"51555859e4b04e73fa963876","contributors":{"authors":[{"text":"Fuller, Pamela L. 0000-0002-9389-9144 pfuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9389-9144","contributorId":3217,"corporation":false,"usgs":true,"family":"Fuller","given":"Pamela","email":"pfuller@usgs.gov","middleInitial":"L.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":false,"id":476624,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Neilson, Matt","contributorId":90187,"corporation":false,"usgs":true,"family":"Neilson","given":"Matt","email":"","affiliations":[],"preferred":false,"id":476626,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huge, Dane H. dhuge@usgs.gov","contributorId":4314,"corporation":false,"usgs":true,"family":"Huge","given":"Dane","email":"dhuge@usgs.gov","middleInitial":"H.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":476625,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70045097,"text":"70045097 - 2013 - A new map of standardized terrestrial ecosystems of Africa","interactions":[],"lastModifiedDate":"2018-03-23T14:25:00","indexId":"70045097","displayToPublicDate":"2013-03-28T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":669,"text":"African Geographical Review","active":true,"publicationSubtype":{"id":10}},"title":"A new map of standardized terrestrial ecosystems of Africa","docAbstract":"Terrestrial ecosystems and vegetation of Africa were classified and mapped as part of a larger effort and global protocol (GEOSS – the Global Earth Observation System of Systems), which includes an activity to map terrestrial ecosystems of the earth in a standardized, robust, and practical manner, and at the finest possible spatial resolution. To model the potential distribution of ecosystems, new continental datasets for several key physical environment datalayers (including coastline, landforms, surficial lithology, and bioclimates) were developed at spatial and classification resolutions finer than existing similar datalayers. A hierarchical vegetation classification was developed by African ecosystem scientists and vegetation geographers, who also provided sample locations of the newly classified vegetation units. The vegetation types and ecosystems were then mapped across the continent using a classification and regression tree (CART) inductive model, which predicted the potential distribution of vegetation types from a suite of biophysical environmental attributes including bioclimate region, biogeographic region, surficial lithology, landform, elevation and land cover. Multi-scale ecosystems were classified and mapped in an increasingly detailed hierarchical framework using vegetation-based concepts of class, subclass, formation, division, and macrogroup levels. The finest vegetation units (macrogroups) classified and mapped in this effort are defined using diagnostic plant species and diagnostic growth forms that reflect biogeographic differences in composition and sub-continental to regional differences in mesoclimate, geology, substrates, hydrology, and disturbance regimes (FGDC, 2008). The macrogroups are regarded as meso-scale (100s to 10,000s of hectares) ecosystems. A total of 126 macrogroup types were mapped, each with multiple, repeating occurrences on the landscape. The modeling effort was implemented at a base spatial resolution of 90 m. In addition to creating several rich, new continent-wide biophysical datalayers describing African vegetation and ecosystems, our intention was to explore feasible approaches to rapidly moving this type of standardized, continent-wide, ecosystem classification and mapping effort forward.","language":"English","publisher":"Association of American Geographers","publisherLocation":"Washington, D.C.","usgsCitation":"Sayre, R.G., Comer, P., Hak, J., Josse, C., Bow, J., Warner, H., Larwanou, M., Kelbessa, E., Bekele, T., Kehl, H., Amena, R., Andriamasimanana, R., Ba, T., Benson, L., Boucher, T., Brown, M., Cress, J.J., Dassering, O., Friesen, B.A., Gachathi, F., Houcine, S., Keita, M., Khamala, E., Marangu, D., Mokua, F., Morou, B., Mucina, L., Mugisha, S., Mwavu, E., Rutherford, M., Sanou, P., Syampungani, S., Tomor, B., Vall, A.O., Vande Weghe, J.P., Wangui, E., and Waruingi, L., 2013, A new map of standardized terrestrial ecosystems of Africa: African Geographical Review, 24 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Here, we develop and validate modeling and sampling methodology for detecting and characterizing seagrass beds by analyzing GPS telemetry records from radio-tagged manatees. Between October 2002 and October 2005, 14 manatees were tracked in the Ten Thousand Islands (TTI) in southwest Florida (USA) using Global Positioning System (GPS) tags. High density manatee use areas were found to occur off each island facing the open, nearshore waters of the Gulf of Mexico. We implemented a spatially stratified random sampling plan and used a camera-based sampling technique to observe and record bottom observations of seagrass and macroalgae presence and abundance. Five species of seagrass were identified in our study area: Halodule wrightii, Thalassia testudinum, Syringodium filiforme, Halophila engelmannii, and Halophila decipiens. A Bayesian model was developed to choose and parameterize a spatial process function that would describe the observed patterns of seagrass and macroalgae. The seagrasses were found in depths <2 m and in the higher manatee use strata, whereas macroalgae was found at moderate densities at all sampled depths and manatee use strata. The manatee spatial data showed a strong association with seagrass beds, a relationship that increased seagrass sampling efficiency. Our camera-based field sampling proved to be effective for assessing seagrass density and spatial coverage under turbid water conditions, and would be an effective monitoring tool to detect changes in seagrass beds.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Marine Ecology Progress Series","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Inter-Research Science Center","publisherLocation":"Lüneburg, Germany","doi":"10.3354/meps10156","usgsCitation":"Slone, D., Reid, J.P., and Kenworthy, W., 2013, Mapping spatial resources with GPS animal telemetry: foraging manatees locate seagrass beds in the Ten Thousand Islands, Florida, USA: Marine Ecology Progress Series, v. 476, p. 285-299, https://doi.org/10.3354/meps10156.","productDescription":"15 p.","startPage":"285","endPage":"299","temporalStart":"2002-10-01","temporalEnd":"2005-10-31","ipdsId":"IP-034057","costCenters":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"links":[{"id":473900,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps10156","text":"Publisher Index Page"},{"id":270318,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":270317,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.3354/meps10156"}],"country":"United States","state":"Florida","otherGeospatial":"Ten Thousand Islands","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -81.52,24.85 ], [ -81.52,25.9 ], [ -80.385,25.9 ], [ -80.385,24.85 ], [ -81.52,24.85 ] ] ] } } ] }","volume":"476","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"515406dce4b030c71ee0670b","contributors":{"authors":[{"text":"Slone, Daniel H. 0000-0002-9903-9727 dslone@usgs.gov","orcid":"https://orcid.org/0000-0002-9903-9727","contributorId":1749,"corporation":false,"usgs":true,"family":"Slone","given":"Daniel H.","email":"dslone@usgs.gov","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":false,"id":476609,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reid, James P. 0000-0002-8497-1132 jreid@usgs.gov","orcid":"https://orcid.org/0000-0002-8497-1132","contributorId":3460,"corporation":false,"usgs":true,"family":"Reid","given":"James","email":"jreid@usgs.gov","middleInitial":"P.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":476610,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kenworthy, W. 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The geodatabase was created using the Environmental Systems Research Institute ArcGIS JavaScript Application Programing Interface 3.2. The database can be accessed at http://cwscpublic2.cr.usgs.gov/projects/coflood/COFloodMap.html.\n\nData on 6,767 flood events at 1,597 individual sites throughout Colorado were compiled to generate the flood database. The data sources of flood information are indirect discharge measurements that were stored in U.S. Geological Survey offices (water years 1867–2011), flood data from indirect discharge measurements referenced in U.S. Geological Survey reports (water years 1884–2011), paleoflood studies from six peer-reviewed journal articles (data on events occurring in the past 5,000 to 10,000 years), and the U.S. Geological Survey National Water Information System peak-discharge database (water years 1883–2010). A number of tests were performed on the flood database to ensure the quality of the data. The Web interface was programmed using the Environmental Systems Research Institute ArcGIS JavaScript Application Programing Interface 3.2, which allows for display, query, georeference, and export of the data in the flood database. The data fields in the flood database used to search and filter the database include hydrologic unit code, U.S. Geological Survey station number, site name, county, drainage area, elevation, data source, date of flood, peak discharge, and field method used to determine discharge. Additional data fields can be viewed and exported, but the data fields described above are the only ones that can be used for queries.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121225","collaboration":"Prepared in cooperation with the Colorado Department of Transportation","usgsCitation":"Kohn, M.S., Jarrett, R.D., Krammes, G.S., and Mommandi, A., 2013, Web-based flood database for Colorado, water years 1867 through 2011: U.S. Geological Survey Open-File Report 2012-1225, vi, 26 p., https://doi.org/10.3133/ofr20121225.","productDescription":"vi, 26 p.","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"1867-09-30","temporalEnd":"2011-09-30","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":270312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20121225.gif"},{"id":270310,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1225/"},{"id":270311,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1225/OF12-1225-508.pdf"}],"country":"United States","state":"Colorado","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -109.0,37.0 ], [ -109.0,41.0 ], [ -102.0,41.0 ], [ -102.0,37.0 ], [ -109.0,37.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"515406e1e4b030c71ee06717","contributors":{"authors":[{"text":"Kohn, Michael S. 0000-0002-5989-7700 mkohn@usgs.gov","orcid":"https://orcid.org/0000-0002-5989-7700","contributorId":4549,"corporation":false,"usgs":true,"family":"Kohn","given":"Michael","email":"mkohn@usgs.gov","middleInitial":"S.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":476634,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jarrett, Robert D. rjarrett@usgs.gov","contributorId":2260,"corporation":false,"usgs":true,"family":"Jarrett","given":"Robert","email":"rjarrett@usgs.gov","middleInitial":"D.","affiliations":[],"preferred":true,"id":476633,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krammes, Gary S. gkrammes@usgs.gov","contributorId":5102,"corporation":false,"usgs":true,"family":"Krammes","given":"Gary","email":"gkrammes@usgs.gov","middleInitial":"S.","affiliations":[],"preferred":true,"id":476635,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mommandi, Amanullah","contributorId":40874,"corporation":false,"usgs":true,"family":"Mommandi","given":"Amanullah","email":"","affiliations":[],"preferred":false,"id":476636,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}