Streamflow and concentrations of sodium and chloride estimated from records of specific conductance were used to calculate loads of sodium and chloride during water year (WY) 2011 (October 1, 2010, to September 30, 2011), for tributaries to the Scituate Reservoir, Rhode Island. Streamflow and water-quality data used in the study were collected by the U.S. Geological Survey (USGS) or the Providence Water Supply Board (PWSB). Streamflow was measured or estimated by the USGS following standard methods at 23 streamgages; 14 of these streamgages were also equipped with instrumentation capable of continuously monitoring water level, specific conductance, and water temperature. Water-quality samples also were collected at 37 sampling stations by the PWSB and at 14 continuous-record streamgages by the USGS during WY 2011 as part of a long-term sampling program; all stations were in the Scituate Reservoir drainage area. Water-quality data collected by PWSB are summarized by using values of central tendency and are used, in combination with measured (or estimated) streamflows, to calculate loads and yields (loads per unit area) of selected water-quality constituents for WY 2011.
\nThe largest tributary to the reservoir (the Ponaganset River, which was monitored by the USGS) contributed a mean streamflow of about 37 cubic feet per second (ft3/s) to the reservoir during WY 2011. For the same time period, annual mean1 streamflows measured (or estimated) for the other monitoring stations in this study ranged from about 0.5 to about 21 ft3/s. Together, tributaries (equipped with instrumentation capable of continuously monitoring specific conductance) transported about 1,600,000 kg (kilograms) of sodium and 2,600,000 kg of chloride to the Scituate Reservoir during WY 2011; sodium and chloride yields for the tributaries ranged from 9,800 to 53,000 kilograms per square mile (kg/mi2) and from 15,000 to 90,000 kg/mi2, respectively.
\nAt the stations where water-quality samples were collected by the PWSB, the median of the median chloride concentrations was 20.0 milligrams per liter (mg/L), median nitrite concentration was 0.002 mg/L as nitrogen (N), median nitrate concentration was 0.01 mg/L as N, median orthophosphate concentration was 0.07 mg/L as phosphorus, and median concentrations of total coliform and Escherichia coli (E. coli) bacteria were 33 and 23 colony forming units per 100 milliliters (CFU/100mL), respectively. The medians of the median daily loads (and yields) of chloride, nitrite, nitrate, orthophosphate, and total coliform and E. coli bacteria were 230 kilograms per day (kg/d) (80 kilograms per day per square mile (kg/d/mi2)); 10 grams per day (g/d) (6.3 grams per day per square mile (g/d/mi2)); 110 g/d (29 g/d/mi2); 610 g/d (270 g/d/mi2); 4,600 million colony forming units per day (CFUx106/d) (2,500 CFUx106/d/mi2); and 1,800 CFUx106/d (810 CFUx106/d/mi2), respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131127","collaboration":"Prepared in cooperation with the Providence Water Supply Board","usgsCitation":"Smith, K.P., 2013, Streamflow, water quality, and constituent loads and yields, Scituate Reservoir drainage area, Rhode Island, water year 2011 (First posted July 23, 2013; Revised and reposted July 14, 2014, version 1.1): U.S. Geological Survey Open-File Report 2013-1127, vi, 32 p., https://doi.org/10.3133/ofr20131127.","productDescription":"vi, 32 p.","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2010-09-30","temporalEnd":"2011-10-31","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":275281,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131127.jpg"},{"id":275279,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1127/"},{"id":275280,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1127/pdf/ofr2013-1127.pdf"}],"country":"United States","state":"Rhode Island","otherGeospatial":"Scituate Reservoir","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -72.0,41.5 ], [ -72.0,42.0 ], [ -71.5,42.0 ], [ -71.5,41.5 ], [ -72.0,41.5 ] ] ] } } ] }","edition":"First posted July 23, 2013; Revised and reposted July 14, 2014, version 1.1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51ef97d9e4b0b09fbe58f16d","contributors":{"authors":[{"text":"Smith, Kirk P. 0000-0003-0269-474X kpsmith@usgs.gov","orcid":"https://orcid.org/0000-0003-0269-474X","contributorId":1516,"corporation":false,"usgs":true,"family":"Smith","given":"Kirk","email":"kpsmith@usgs.gov","middleInitial":"P.","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":481197,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70046834,"text":"70046834 - 2013 - Potentially induced earthquakes in Oklahoma, USA: links between wastewater injection and the 2011 Mw 5.7 earthquake sequence","interactions":[],"lastModifiedDate":"2019-07-17T16:26:58","indexId":"70046834","displayToPublicDate":"2013-07-23T09:38:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Potentially induced earthquakes in Oklahoma, USA: links between wastewater injection and the 2011 Mw 5.7 earthquake sequence","docAbstract":"Significant earthquakes are increasingly occurring within the continental interior of the United States, including five of moment magnitude (Mw) ≥ 5.0 in 2011 alone. Concurrently, the volume of fluid injected into the subsurface related to the production of unconventional resources continues to rise. Here we identify the largest earthquake potentially related to injection, an Mw 5.7 earthquake in November 2011 in Oklahoma. The earthquake was felt in at least 17 states and caused damage in the epicentral region. It occurred in a sequence, with 2 earthquakes of Mw 5.0 and a prolific sequence of aftershocks. We use the aftershocks to illuminate the faults that ruptured in the sequence, and show that the tip of the initial rupture plane is within ~200 m of active injection wells and within ~1 km of the surface; 30% of early aftershocks occur within the sedimentary section. Subsurface data indicate that fluid was injected into effectively sealed compartments, and we interpret that a net fluid volume increase after 18 yr of injection lowered effective stress on reservoir-bounding faults. Significantly, this case indicates that decades-long lags between the commencement of fluid injection and the onset of induced earthquakes are possible, and modifies our common criteria for fluid-induced events. The progressive rupture of three fault planes in this sequence suggests that stress changes from the initial rupture triggered the successive earthquakes, including one larger than the first.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Geology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/G34045.1","usgsCitation":"Keranen, K., Savage, H.M., Abers, G.A., and Cochran, E.S., 2013, Potentially induced earthquakes in Oklahoma, USA: links between wastewater injection and the 2011 Mw 5.7 earthquake sequence: Geology, v. 41, no. 6, p. 699-702, https://doi.org/10.1130/G34045.1.","productDescription":"4 p.","startPage":"699","endPage":"702","ipdsId":"IP-039045","costCenters":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":275269,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":274697,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1130/G34045.1"}],"country":"United States","state":"Oklahoma","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -103.0,33.62 ], [ -103.0,37.0 ], [ -94.43,37.0 ], [ -94.43,33.62 ], [ -103.0,33.62 ] ] ] } } ] }","volume":"41","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51ef97d6e4b0b09fbe58f15d","contributors":{"authors":[{"text":"Keranen, Katie M.","contributorId":44064,"corporation":false,"usgs":true,"family":"Keranen","given":"Katie M.","affiliations":[],"preferred":false,"id":480414,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Savage, Heather M.","contributorId":65363,"corporation":false,"usgs":true,"family":"Savage","given":"Heather","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":480415,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Abers, Geoffrey A.","contributorId":90195,"corporation":false,"usgs":true,"family":"Abers","given":"Geoffrey","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":480416,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":480413,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70046061,"text":"70046061 - 2013 - Predicting the likelihood of altered streamflows at ungauged rivers across the conterminous United States","interactions":[],"lastModifiedDate":"2013-07-23T09:48:25","indexId":"70046061","displayToPublicDate":"2013-07-23T09:35:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Predicting the likelihood of altered streamflows at ungauged rivers across the conterminous United States","docAbstract":"An approach is presented in this study to aid water-resource managers in characterizing streamflow alteration at ungauged rivers. Such approaches can be used to take advantage of the substantial amounts of biological data collected at ungauged rivers to evaluate the potential ecological consequences of altered streamflows. National-scale random forest statistical models are developed to predict the likelihood that ungauged rivers have altered streamflows (relative to expected natural condition) for five hydrologic metrics (HMs) representing different aspects of the streamflow regime. The models use human disturbance variables, such as number of dams and road density, to predict the likelihood of streamflow alteration. For each HM, separate models are derived to predict the likelihood that the observed metric is greater than (‘inflated’) or less than (‘diminished’) natural conditions. The utility of these models is demonstrated by applying them to all river segments in the South Platte River in Colorado, USA, and for all 10-digit hydrologic units in the conterminous United States. In general, the models successfully predicted the likelihood of alteration to the five HMs at the national scale as well as in the South Platte River basin. However, the models predicting the likelihood of diminished HMs consistently outperformed models predicting inflated HMs, possibly because of fewer sites across the conterminous United States where HMs are inflated. The results of these analyses suggest that the primary predictors of altered streamflow regimes across the Nation are (i) the residence time of annual runoff held in storage in reservoirs, (ii) the degree of urbanization measured by road density and (iii) the extent of agricultural land cover in the river basin.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"River Research and Applications","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1002/rra.2565","usgsCitation":"Eng, K., Carlisle, D.M., Wolock, D.M., and Falcone, J.A., 2013, Predicting the likelihood of altered streamflows at ungauged rivers across the conterminous United States: River Research and Applications, v. 29, no. 6, p. 781-791, https://doi.org/10.1002/rra.2565.","productDescription":"10 p.","startPage":"781","endPage":"791","numberOfPages":"10","ipdsId":"IP-034661","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":275268,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":275267,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/rra.2565"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -125.14,25.89 ], [ -125.14,49.11 ], [ -66.95,49.11 ], [ -66.95,25.89 ], [ -125.14,25.89 ] ] ] } } ] }","volume":"29","issue":"6","noUsgsAuthors":false,"publicationDate":"2012-03-09","publicationStatus":"PW","scienceBaseUri":"51ef97d8e4b0b09fbe58f161","contributors":{"authors":[{"text":"Eng, Ken 0000-0001-6838-5849 keng@usgs.gov","orcid":"https://orcid.org/0000-0001-6838-5849","contributorId":3580,"corporation":false,"usgs":true,"family":"Eng","given":"Ken","email":"keng@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":478791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carlisle, Daren M. 0000-0002-7367-348X dcarlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-7367-348X","contributorId":513,"corporation":false,"usgs":true,"family":"Carlisle","given":"Daren","email":"dcarlisle@usgs.gov","middleInitial":"M.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":478788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wolock, David M. 0000-0002-6209-938X dwolock@usgs.gov","orcid":"https://orcid.org/0000-0002-6209-938X","contributorId":540,"corporation":false,"usgs":true,"family":"Wolock","given":"David","email":"dwolock@usgs.gov","middleInitial":"M.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":478789,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Falcone, James A. 0000-0001-7202-3592 jfalcone@usgs.gov","orcid":"https://orcid.org/0000-0001-7202-3592","contributorId":614,"corporation":false,"usgs":true,"family":"Falcone","given":"James","email":"jfalcone@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":478790,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70047160,"text":"70047160 - 2013 - Correlating multispectral imaging and compositional data from the Mars Exploration Rovers and implications for Mars Science Laboratory","interactions":[],"lastModifiedDate":"2013-07-23T09:22:27","indexId":"70047160","displayToPublicDate":"2013-07-23T09:17:55","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1963,"text":"Icarus","active":true,"publicationSubtype":{"id":10}},"title":"Correlating multispectral imaging and compositional data from the Mars Exploration Rovers and implications for Mars Science Laboratory","docAbstract":"In an effort to infer compositional information about distant targets based on multispectral imaging data, we investigated methods of relating Mars Exploration Rover (MER) Pancam multispectral remote sensing observations to in situ alpha particle X-ray spectrometer (APXS)-derived elemental abundances and Mössbauer (MB)-derived abundances of Fe-bearing phases at the MER field sites in Gusev crater and Meridiani Planum. The majority of the partial correlation coefficients between these data sets were not statistically significant. Restricting the targets to those that were abraded by the rock abrasion tool (RAT) led to improved Pearson’s correlations, most notably between the red–blue ratio (673 nm/434 nm) and Fe3+-bearing phases, but partial correlations were not statistically significant. Partial Least Squares (PLS) calculations relating Pancam 11-color visible to near-IR (VNIR; ∼400–1000 nm) “spectra” to APXS and Mössbauer element or mineral abundances showed generally poor performance, although the presence of compositional outliers led to improved PLS results for data from Meridiani. When the Meridiani PLS model for pyroxene was tested by predicting the pyroxene content of Gusev targets, the results were poor, indicating that the PLS models for Meridiani are not applicable to data from other sites. Soft Independent Modeling of Class Analogy (SIMCA) classification of Gusev crater data showed mixed results. Of the 24 Gusev test regions of interest (ROIs) with known classes, 11 had >30% of the pixels in the ROI classified correctly, while others were mis-classified or unclassified. k-Means clustering of APXS and Mössbauer data was used to assign Meridiani targets to compositional classes. The clustering-derived classes corresponded to meaningful geologic and/or color unit differences, and SIMCA classification using these classes was somewhat successful, with >30% of pixels correctly classified in 9 of the 11 ROIs with known classes.\n\nThis work shows that the relationship between SWIR multispectral imaging data and APXS- and Mössbauer-derived composition/mineralogy is often weak, a perhaps not entirely unexpected result given the different surface sampling depths of SWIR imaging (uppermost few microns) vs. APXS (tens of μm) and MB measurements (hundreds of μm). Results from the upcoming Mars Science Laboratory (MSL) rover’s ChemCam Laser Induced Breakdown Spectroscopy (LIBS) instrument may show a closer relationship to Mastcam SWIR multispectral observations, however, because the initial laser shots onto a target will analyze only the upper few micrometers of the surface. The clustering and classification methods used in this study can be applied to any data set to formalize the definition of classes and identify targets that do not fit in previously defined classes.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Icarus","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.icarus.2012.11.029","usgsCitation":"Anderson, R., and Bell, J.F., 2013, Correlating multispectral imaging and compositional data from the Mars Exploration Rovers and implications for Mars Science Laboratory: Icarus, v. 223, no. 1, p. 157-180, https://doi.org/10.1016/j.icarus.2012.11.029.","productDescription":"24 p.","startPage":"157","endPage":"180","ipdsId":"IP-036034","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":275266,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":275265,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.icarus.2012.11.029"}],"otherGeospatial":"Mars","volume":"223","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51ef97d4e4b0b09fbe58f149","contributors":{"authors":[{"text":"Anderson, Ryan B.","contributorId":25438,"corporation":false,"usgs":true,"family":"Anderson","given":"Ryan B.","affiliations":[],"preferred":false,"id":481190,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bell, James F. III","contributorId":12737,"corporation":false,"usgs":true,"family":"Bell","given":"James","suffix":"III","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":481189,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70046880,"text":"70046880 - 2013 - Population genetics and evaluation of genetic evidence for subspecies in the Semipalmated Sandpiper (Calidris pusilla)","interactions":[],"lastModifiedDate":"2020-12-29T15:03:14.119164","indexId":"70046880","displayToPublicDate":"2013-07-23T08:34:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3731,"text":"Waterbirds","onlineIssn":"19385390","printIssn":"15244695","active":true,"publicationSubtype":{"id":10}},"title":"Population genetics and evaluation of genetic evidence for subspecies in the Semipalmated Sandpiper (Calidris pusilla)","docAbstract":"Semipalmated Sandpipers (Calidris pusilla) are among the most common North American shorebirds. Breeding in Arctic North America, this species displays regional differences in migratory pathways and possesses longitudinal bill length variation. Previous investigations suggested that genetic structure may occur within Semipalmated Sandpipers and that three subspecies corresponding to western, central, and eastern breeding groups exist. In this study, mitochondrial control region sequences and nuclear microsatellite loci were used to analyze DNA of birds (microsatellites: n = 120; mtDNA: n = 114) sampled from seven North American locations. Analyses designed to quantify genetic structure and diversity patterns, evaluate genetic evidence for population size changes, and determine if genetic data support the existence of Semipalmated Sandpiper subspecies were performed. Genetic structure based only on the mtDNA data was observed, whereas the microsatellite loci provided no evidence of genetic differentiation. Differentiation among locations and regions reflected allele frequency differences rather than separate phylogenetic groups, and similar levels of genetic diversity were noted. Combined, the two data sets provided no evidence to support the existence of subspecies and were not useful for determining migratory connectivity between breeding sites and wintering grounds. Birds from western and central groups displayed signatures of population expansions, whereas the eastern group was more consistent with a stable overall population. Results of this analysis suggest that the eastern group was the source of individuals that colonized the central and western regions currently utilized by Semipalmated Sandpipers.
","language":"English","publisher":"The Waterbird Society","doi":"10.1675/063.036.0206","usgsCitation":"Miller, M.P., Gratto-Trevor, C., Haig, S.M., Mizrahi, D.S., Mitchell, M.M., and Mullins, T., 2013, Population genetics and evaluation of genetic evidence for subspecies in the Semipalmated Sandpiper (Calidris pusilla): Waterbirds, v. 36, no. 2, p. 166-178, https://doi.org/10.1675/063.036.0206.","productDescription":"13 p.","startPage":"166","endPage":"178","ipdsId":"IP-042836","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":473660,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1675/063.036.0206","text":"Publisher Index Page"},{"id":381723,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"North America","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -178.8,25.3 ], [ -178.8,83.2 ], [ -51.3,83.2 ], [ -51.3,25.3 ], [ -178.8,25.3 ] ] ] } } ] }","volume":"36","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51ef97d6e4b0b09fbe58f159","contributors":{"authors":[{"text":"Miller, Mark P. 0000-0003-1045-1772 mpmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-1045-1772","contributorId":1967,"corporation":false,"usgs":true,"family":"Miller","given":"Mark","email":"mpmiller@usgs.gov","middleInitial":"P.","affiliations":[{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"preferred":true,"id":480555,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gratto-Trevor, Cheri","contributorId":58539,"corporation":false,"usgs":true,"family":"Gratto-Trevor","given":"Cheri","affiliations":[],"preferred":false,"id":480559,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haig, Susan M. 0000-0002-6616-7589 susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":480554,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mizrahi, David S.","contributorId":11100,"corporation":false,"usgs":true,"family":"Mizrahi","given":"David","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":480556,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mitchell, Melanie M.","contributorId":38045,"corporation":false,"usgs":true,"family":"Mitchell","given":"Melanie","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":480558,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mullins, Thomas D.","contributorId":12819,"corporation":false,"usgs":true,"family":"Mullins","given":"Thomas D.","affiliations":[],"preferred":false,"id":480557,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70047143,"text":"ofr20131146 - 2013 - Geochronologic and geochemical data from Mesozoic rocks in the Black Mountain area northeast of Victorville, San Bernardino County, California","interactions":[],"lastModifiedDate":"2023-06-05T15:09:34.640808","indexId":"ofr20131146","displayToPublicDate":"2013-07-22T15:55:00","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":"2013-1146","title":"Geochronologic and geochemical data from Mesozoic rocks in the Black Mountain area northeast of Victorville, San Bernardino County, California","docAbstract":"We present geochronologic and geochemical data for Mesozoic rocks in the Black Mountain area northeast of Victorville, California, to supplement previous geologic mapping. These data, together with previously published results, limit the depositional age of the sedimentary Fairview Valley Formation to Early Jurassic, refine the ages and chemical compositions of selected units in the overlying Jurassic Sidewinder Volcanics and of related intrusive units, and limit the age of some post-Sidewinder faulting in the Black Mountain area to a brief interval in the Late Jurassic. The new information contributes to a more complete understanding of the Mesozoic magmatic and tectonic evolution of the western Mojave Desert and surrounding regions.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131146","usgsCitation":"Stone, P., Barth, A.P., Wooden, J., Fohey-Breting, N.K., Vazquez, J.A., and Priest, S.S., 2013, Geochronologic and geochemical data from Mesozoic rocks in the Black Mountain area northeast of Victorville, San Bernardino County, California: U.S. Geological Survey Open-File Report 2013-1146, iv, 31 p., https://doi.org/10.3133/ofr20131146.","productDescription":"iv, 31 p.","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":275252,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131146.gif"},{"id":275250,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1146/","linkFileType":{"id":5,"text":"html"}},{"id":275251,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1146/of2013-1146.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","county":"San Bernardino","otherGeospatial":"Black Mountain Area","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -117.5823,34.07 ], [ -117.5823,34.98 ], [ -117.347,34.98 ], [ -117.347,34.07 ], [ -117.5823,34.07 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51ee4655e4b00ffbed48f849","contributors":{"authors":[{"text":"Stone, Paul 0000-0002-1439-0156 pastone@usgs.gov","orcid":"https://orcid.org/0000-0002-1439-0156","contributorId":273,"corporation":false,"usgs":true,"family":"Stone","given":"Paul","email":"pastone@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":481156,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barth, Andrew P.","contributorId":94547,"corporation":false,"usgs":true,"family":"Barth","given":"Andrew","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":481160,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wooden, Joseph L.","contributorId":32209,"corporation":false,"usgs":true,"family":"Wooden","given":"Joseph L.","affiliations":[],"preferred":false,"id":481159,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fohey-Breting, Nicole K.","contributorId":102363,"corporation":false,"usgs":true,"family":"Fohey-Breting","given":"Nicole","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":481161,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vazquez, Jorge A. 0000-0003-2754-0456 jvazquez@usgs.gov","orcid":"https://orcid.org/0000-0003-2754-0456","contributorId":4458,"corporation":false,"usgs":true,"family":"Vazquez","given":"Jorge","email":"jvazquez@usgs.gov","middleInitial":"A.","affiliations":[{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":481157,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Priest, Susan S. spriest@usgs.gov","contributorId":30204,"corporation":false,"usgs":true,"family":"Priest","given":"Susan","email":"spriest@usgs.gov","middleInitial":"S.","affiliations":[],"preferred":false,"id":481158,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70047141,"text":"ds775 - 2013 - High-water marks from tropical storm Irene for selected river reaches in northwestern Massachusetts, August 2011","interactions":[],"lastModifiedDate":"2026-05-20T16:57:28.965786","indexId":"ds775","displayToPublicDate":"2013-07-22T14:10:00","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":"775","title":"High-water marks from tropical storm Irene for selected river reaches in northwestern Massachusetts, August 2011","docAbstract":"A Presidential Disaster Declaration was issued for Massachusetts, with a focus on the northwestern counties, following flooding from tropical storm Irene on August 28–29, 2011. Three to 10 inches of rain fell during the storm on soils that were susceptible to flash flooding because of wet antecedent conditions. The gage height at one U.S. Geological Survey (USGS) streamgage rose nearly 20 feet in less than 4 hours because of the combination of saturated soils and intense rainfall. Eight of 16 USGS long-term streamgages in western Massachusetts set new peaks of record on August 28 or 29, 2011. To document the historic water levels of the streamflows from tropical storm Irene, the USGS identified, flagged, and surveyed 323 high-water marks in the Deerfield and Hudson- Hoosic River basins in northwestern Massachusetts. Areas targeted for high-water marks were generally upstream and downstream from structures along selected river reaches. Elevations from high-water marks can be used to confirm peak river stages or help compute peak streamflows, to calibrate hydraulic models, or to update flood-inundation and recovery maps. For areas in western Massachusetts that flooded as a result of tropical storm Irene, high-water marks surveyed for this study have helped to confirm or determine instantaneous peak river gage heights at several USGS streamgages.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds775","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Bent, G.C., Medalie, L., and Nielsen, M.G., 2013, High-water marks from tropical storm Irene for selected river reaches in northwestern Massachusetts, August 2011: U.S. Geological Survey Data Series 775, Report: iv, 13 p.; Appendix 1: XLS file; Appendix 2: KMZ file, https://doi.org/10.3133/ds775.","productDescription":"Report: iv, 13 p.; Appendix 1: XLS file; Appendix 2: KMZ file","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":275233,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/775/pdf/ds775_report_508.pdf"},{"id":275236,"rank":1,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/775/appendix/USGS_Data_Series_775_Appendix_2_HWMs.kmz"},{"id":275235,"rank":2,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/775/appendix/USGS_Data_Series_775_Appendix_1.xlsx"},{"id":275234,"rank":4,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/775/"},{"id":275237,"rank":5,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds775.jpg"},{"id":504564,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_98685.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Massachusetts","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -73.493042,42.012571 ], [ -73.493042,42.710696 ], [ -72.463074,42.710696 ], [ -72.463074,42.012571 ], [ -73.493042,42.012571 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51ee4655e4b00ffbed48f84d","contributors":{"authors":[{"text":"Bent, Gardner C. 0000-0002-5085-3146 gbent@usgs.gov","orcid":"https://orcid.org/0000-0002-5085-3146","contributorId":1864,"corporation":false,"usgs":true,"family":"Bent","given":"Gardner","email":"gbent@usgs.gov","middleInitial":"C.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":481153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Medalie, Laura 0000-0002-2440-2149 lmedalie@usgs.gov","orcid":"https://orcid.org/0000-0002-2440-2149","contributorId":3657,"corporation":false,"usgs":true,"family":"Medalie","given":"Laura","email":"lmedalie@usgs.gov","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":481154,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nielsen, Martha G. 0000-0003-3038-9400 mnielsen@usgs.gov","orcid":"https://orcid.org/0000-0003-3038-9400","contributorId":4169,"corporation":false,"usgs":true,"family":"Nielsen","given":"Martha","email":"mnielsen@usgs.gov","middleInitial":"G.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":481155,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70046965,"text":"70046965 - 2013 - Relating Yellow Rail (Coturnicops noveboracensis) occupancy to habitat and landscape features in the context of fire","interactions":[],"lastModifiedDate":"2017-09-08T09:12:23","indexId":"70046965","displayToPublicDate":"2013-07-22T13:44:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3731,"text":"Waterbirds","onlineIssn":"19385390","printIssn":"15244695","active":true,"publicationSubtype":{"id":10}},"title":"Relating Yellow Rail (Coturnicops noveboracensis) occupancy to habitat and landscape features in the context of fire","docAbstract":"The Yellow Rail (Coturnicops noveboracensis) is a focal species of concern associated with shallowly flooded emergent wetlands, most commonly sedge (Carex spp.) meadows. Their populations are believed to be limited by loss or degradation of wetland habitat due to drainage, altered hydrology, and fire suppression, factors that have often resulted in encroachment of shrubs into sedge meadows and change in vegetative cover. Nocturnal call-playback surveys for Yellow Rails were conducted over 3 years at Seney National Wildlife Refuge in the Upper Peninsula of Michigan. Effects of habitat structure and landscape variables on the probability of use by Yellow Rails were assessed at two scales, representing a range of home range sizes, using generalized linear mixed models. At the 163-m (8-ha) scale, year with quadratic models of maximum and mean water depths best explained the data. At the 300-m (28-ha) scale, the best model contained year and time since last fire (≤ 1, 2–5, and > 10 years). The probability of use by Yellow Rails was 0.285 ± 0.132 (SE) for points burned 2-5 years ago, 0.253 ± 0.097 for points burned ≤ 1 year ago, and 0.028 ± 0.019 for points burned > 10 years ago. Habitat differences relative to fire history and comparisons between sites with and without Yellow Rails indicated that Yellow Rails used areas with the deepest litter and highest ground cover, and relatively low shrub cover and heights, as well as landscapes having greater sedge-grass cover and less lowland woody or upland cover types. Burning every 2-5 years appears to provide the litter, ground-level cover, and woody conditions attractive to Yellow Rails. Managers seeking to restore and sustain these wetland systems would benefit from further investigations into how flooding and fire create habitat conditions attractive to breeding Yellow Rails","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Waterbirds","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"The Waterbird Society","doi":"10.1675/063.036.0209","usgsCitation":"Austin, J., and Buhl, D., 2013, Relating Yellow Rail (Coturnicops noveboracensis) occupancy to habitat and landscape features in the context of fire: Waterbirds, v. 36, no. 2, p. 199-213, https://doi.org/10.1675/063.036.0209.","productDescription":"15 p.","startPage":"199","endPage":"213","ipdsId":"IP-039078","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":473663,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1675/063.036.0209","text":"Publisher Index Page"},{"id":275190,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":274839,"type":{"id":15,"text":"Index Page"},"url":"https://www.bioone.org/doi/pdf/10.1675/063.036.0209"},{"id":275184,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1675/063.036.0209"}],"country":"United States","state":"Michigan","otherGeospatial":"Seney National Wildlife Refuge","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -86.27,46.16 ], [ -86.27,46.77 ], [ -84.95,46.77 ], [ -84.95,46.16 ], [ -86.27,46.16 ] ] ] } } ] }","volume":"36","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51ee465be4b00ffbed48f875","contributors":{"authors":[{"text":"Austin, Jane E.","contributorId":43094,"corporation":false,"usgs":true,"family":"Austin","given":"Jane E.","affiliations":[],"preferred":false,"id":480725,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buhl, Deborah A. 0000-0002-8563-5990","orcid":"https://orcid.org/0000-0002-8563-5990","contributorId":26250,"corporation":false,"usgs":true,"family":"Buhl","given":"Deborah A.","affiliations":[],"preferred":false,"id":480724,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70046801,"text":"70046801 - 2013 - Predicting breeding shorebird distributions on the Arctic Coastal Plain of Alaska","interactions":[],"lastModifiedDate":"2017-11-22T10:19:51","indexId":"70046801","displayToPublicDate":"2013-07-22T13:22:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Predicting breeding shorebird distributions on the Arctic Coastal Plain of Alaska","docAbstract":"The Arctic Coastal Plain (ACP) of Alaska is an important region for millions of migrating and nesting shorebirds. However, this region is threatened by climate change and increased human development (e.g., oil and gas production) that have the potential to greatly impact shorebird populations and breeding habitat in the near future. Because historic data on shorebird distributions in the ACP are very coarse and incomplete, we sought to develop detailed, contemporary distribution maps so that the potential impacts of climate-mediated changes and development could be ascertained. To do this, we developed and mapped habitat suitability indices for eight species of shorebirds (Black-bellied Plover [Pluvialis squatarola], American Golden-Plover [Pluvialis dominica], Semipalmated Sandpiper [Calidris pusilla], Pectoral Sandpiper [Calidris melanotos], Dunlin [Calidris alpina], Long-billed Dowitcher [Limnodromus scolopaceus], Red-necked Phalarope [Phalaropus lobatus], and Red Phalarope [Phalaropus fulicarius]) that commonly breed within the ACP of Alaska. These habitat suitability models were based on 767 plots surveyed during nine years between 1998 and 2008 (surveys were not conducted in 2003 and 2005), using single-visit rapid area searches during territory establishment and incubation (8 June, 1 July). Species specific habitat suitability indices were developed and mapped using presence-only modeling techniques (partitioned Mahalanobis distance) and landscape environmental variables. For most species, habitat suitability was greater at lower elevations (i.e., near the coast and river deltas) and lower within upland habitats. Accuracy of models was high for all species, ranging from 65 -98%. Our models predicted that the largest fraction of suitable habitat for the majority of species occurred within the National Petroleum Reserve-Alaska, with highly suitable habitat also occurring within coastal areas of the Arctic National Wildlife Refuge west to Prudhoe Bay.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecosphere","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Ecological Society of America","doi":"10.1890/ES12-00292.1","usgsCitation":"Saalfeld, S., Lanctot, R.B., Brown, S.C., Saalfeld, D.T., Johnson, J., Andres, B.A., and Bart, J.R., 2013, Predicting breeding shorebird distributions on the Arctic Coastal Plain of Alaska: Ecosphere, v. 4, no. 1, 17 p., https://doi.org/10.1890/ES12-00292.1.","productDescription":"17 p.","numberOfPages":"17","ipdsId":"IP-043081","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":473664,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1890/es12-00292.1","text":"Publisher Index Page"},{"id":275229,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":274693,"type":{"id":15,"text":"Index Page"},"url":"https://www.esajournals.org/doi/pdf/10.1890/ES12-00292.1"},{"id":275228,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1890/ES12-00292.1"}],"country":"United States","state":"Alaska","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -160.29,69.08 ], [ -160.29,71.41 ], [ -142.34,71.41 ], [ -142.34,69.08 ], [ -160.29,69.08 ] ] ] } } ] }","volume":"4","issue":"1","noUsgsAuthors":false,"publicationDate":"2013-01-31","publicationStatus":"PW","scienceBaseUri":"51ee465ae4b00ffbed48f86d","contributors":{"authors":[{"text":"Saalfeld, Sarah T.","contributorId":41721,"corporation":false,"usgs":true,"family":"Saalfeld","given":"Sarah T.","affiliations":[],"preferred":false,"id":480298,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lanctot, Richard B.","contributorId":31894,"corporation":false,"usgs":true,"family":"Lanctot","given":"Richard","email":"","middleInitial":"B.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false},{"id":7029,"text":"Queen's University, Kingston, Ontario, Canada","active":true,"usgs":false},{"id":17786,"text":"Carleton University","active":true,"usgs":false},{"id":135,"text":"Biological Resources Division","active":false,"usgs":true}],"preferred":false,"id":480296,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Stephen C.","contributorId":38457,"corporation":false,"usgs":false,"family":"Brown","given":"Stephen","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":480297,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Saalfeld, David T.","contributorId":49685,"corporation":false,"usgs":true,"family":"Saalfeld","given":"David","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":480299,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, James A.","contributorId":84649,"corporation":false,"usgs":true,"family":"Johnson","given":"James A.","affiliations":[],"preferred":false,"id":480302,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Andres, Brad A.","contributorId":68811,"corporation":false,"usgs":true,"family":"Andres","given":"Brad","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":480300,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bart, Jonathan R.","contributorId":74273,"corporation":false,"usgs":true,"family":"Bart","given":"Jonathan","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":480301,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70045973,"text":"70045973 - 2013 - Predicting locations of rare aquatic species’ habitat with a combination of species-specific and assemblage-based models","interactions":[],"lastModifiedDate":"2013-07-22T11:47:36","indexId":"70045973","displayToPublicDate":"2013-07-22T11:34:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Predicting locations of rare aquatic species’ habitat with a combination of species-specific and assemblage-based models","docAbstract":"Aim: Rare aquatic species are a substantial component of biodiversity, and their conservation is a major objective of many management plans. However, they are difficult to assess, and their optimal habitats are often poorly known. Methods to effectively predict the likely locations of suitable rare aquatic species habitats are needed. We combine two modelling approaches to predict occurrence and general abundance of several rare fish species. Location: Allegheny watershed of western New York State (USA) Methods: Our method used two empirical neural network modelling approaches (species specific and assemblage based) to predict stream-by-stream occurrence and general abundance of rare darters, based on broad-scale habitat conditions. Species-specific models were developed for longhead darter (Percina macrocephala), spotted darter (Etheostoma maculatum) and variegate darter (Etheostoma variatum) in the Allegheny drainage. An additional model predicted the type of rare darter-containing assemblage expected in each stream reach. Predictions from both models were then combined inclusively and exclusively and compared with additional independent data. Results Example rare darter predictions demonstrate the method's effectiveness. Models performed well (R2 ≥ 0.79), identified where suitable darter habitat was most likely to occur, and predictions matched well to those of collection sites. Additional independent data showed that the most conservative (exclusive) model slightly underestimated the distributions of these rare darters or predictions were displaced by one stream reach, suggesting that new darter habitat types were detected in the later collections. Main conclusions Broad-scale habitat variables can be used to effectively identify rare species' habitats. Combining species-specific and assemblage-based models enhances our ability to make use of the sparse data on rare species and to identify habitat units most likely and least likely to support those species. This hybrid approach may assist managers with the prioritization of habitats to be examined or conserved for rare species.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Diversity and Distributions","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1111/ddi.12059","usgsCitation":"McKenna, J., Carlson, D.M., and Payne-Wynne, M.L., 2013, Predicting locations of rare aquatic species’ habitat with a combination of species-specific and assemblage-based models: Diversity and Distributions, v. 19, no. 5-6, p. 503-517, https://doi.org/10.1111/ddi.12059.","productDescription":"15 p.","startPage":"503","endPage":"517","numberOfPages":"15","ipdsId":"IP-039413","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":473665,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.12059","text":"Publisher Index Page"},{"id":275216,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":275215,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/ddi.12059"}],"country":"United States","state":"New York","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -79.881592,41.459195 ], [ -79.881592,42.228517 ], [ -78.222656,42.228517 ], [ -78.222656,41.459195 ], [ -79.881592,41.459195 ] ] ] } } ] }","volume":"19","issue":"5-6","noUsgsAuthors":false,"publicationDate":"2013-05-06","publicationStatus":"PW","scienceBaseUri":"51ee465be4b00ffbed48f871","contributors":{"authors":[{"text":"McKenna, James E.","contributorId":9217,"corporation":false,"usgs":true,"family":"McKenna","given":"James E.","affiliations":[],"preferred":false,"id":478619,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carlson, Douglas M.","contributorId":91001,"corporation":false,"usgs":false,"family":"Carlson","given":"Douglas","email":"","middleInitial":"M.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":478621,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Payne-Wynne, Molly L.","contributorId":33604,"corporation":false,"usgs":true,"family":"Payne-Wynne","given":"Molly","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":478620,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70047133,"text":"70047133 - 2013 - Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota","interactions":[],"lastModifiedDate":"2013-07-22T11:19:03","indexId":"70047133","displayToPublicDate":"2013-07-22T11:08:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota","docAbstract":"Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"MDPI AG","doi":"10.3390/rs5073212","usgsCitation":"Corcoran, J.M., Knight, J.F., and Gallant, A.L., 2013, Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota: Remote Sensing, v. 5, no. 7, p. 3212-3238, https://doi.org/10.3390/rs5073212.","productDescription":"27 p.","startPage":"3212","endPage":"3238","ipdsId":"IP-042123","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":473666,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs5073212","text":"Publisher Index Page"},{"id":275209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":275208,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.3390/rs5073212"}],"country":"United States","state":"Minnesota","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -97.24,43.5 ], [ -97.24,49.38 ], [ -89.49,49.38 ], [ -89.49,43.5 ], [ -97.24,43.5 ] ] ] } } ] }","volume":"5","issue":"7","noUsgsAuthors":false,"publicationDate":"2013-07-04","publicationStatus":"PW","scienceBaseUri":"51ee4655e4b00ffbed48f851","contributors":{"authors":[{"text":"Corcoran, Jennifer M.","contributorId":66575,"corporation":false,"usgs":true,"family":"Corcoran","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":481152,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knight, Joseph F.","contributorId":55311,"corporation":false,"usgs":true,"family":"Knight","given":"Joseph","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":481151,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gallant, Alisa L. 0000-0002-3029-6637 gallant@usgs.gov","orcid":"https://orcid.org/0000-0002-3029-6637","contributorId":2940,"corporation":false,"usgs":true,"family":"Gallant","given":"Alisa","email":"gallant@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":481150,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70047131,"text":"ds773 - 2013 - Archive of digital boomer subbottom data collected during USGS cruises 99FGS01 and 99FGS02 offshore southeast and southwest Florida, July and November, 1999","interactions":[],"lastModifiedDate":"2026-05-20T16:53:43.131085","indexId":"ds773","displayToPublicDate":"2013-07-22T10:18:00","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":"773","title":"Archive of digital boomer subbottom data collected during USGS cruises 99FGS01 and 99FGS02 offshore southeast and southwest Florida, July and November, 1999","docAbstract":"In July (19 - 26) and November (17 - 18) of 1999, the USGS, in cooperation with the Florida Geological Survey (FGS), conducted two geophysical surveys in: (1) the Atlantic Ocean offshore of Florida's east coast from Orchid to Jupiter, FL, and (2) the Gulf of Mexico offshore of Venice, FL. This report serves as an archive of unprocessed digital boomer subbottom data, trackline maps, navigation files, GIS files, Field Activity Collection System (FACS) logs, and formal Federal Geographic Data Committee (FGDC) metadata. Filtered and gained (showing a relative increase in signal amplitude) digital images of the subbottom profiles are also provided.\n\nThe USGS St. Petersburg Coastal and Marine Science Center (SPCMSC) assigns a unique identifier to each cruise or field activity. For example, identifiers 99FGS01 and 99FGS02 refer to field data collected in 1999 for cooperative work with the FGS. The numbers 01 and 02 indicate the data were collected during the first and second field activities for that project in that calendar year. Refer to http://walrus.wr.usgs.gov/infobank/programs/html/definition/activity.html for a detailed description of the method used to assign the field activity identification (ID).","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds773","usgsCitation":"Forde, A.S., Dadisman, S.V., Wiese, D.S., and Phelps, D.C., 2013, Archive of digital boomer subbottom data collected during USGS cruises 99FGS01 and 99FGS02 offshore southeast and southwest Florida, July and November, 1999: U.S. Geological Survey Data Series 773, HTML Document; 1 DVD, https://doi.org/10.3133/ds773.","productDescription":"HTML Document; 1 DVD","onlineOnly":"N","additionalOnlineFiles":"Y","temporalStart":"1999-07-01","temporalEnd":"1999-11-30","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":504562,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_98684.htm","text":"southwest Florida study area","linkFileType":{"id":5,"text":"html"}},{"id":504561,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_98683.htm","text":"southeast Florida study area","linkFileType":{"id":5,"text":"html"}},{"id":275203,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/773/title.html"},{"id":275202,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/773/"},{"id":275204,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds773.PNG"}],"country":"United States","state":"Florida","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -83.0,26.166667 ], [ -83.0,28.5 ], [ -79.833333,28.5 ], [ -79.833333,26.166667 ], [ -83.0,26.166667 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51ee464ee4b00ffbed48f841","contributors":{"authors":[{"text":"Forde, Arnell S. 0000-0002-5581-2255 aforde@usgs.gov","orcid":"https://orcid.org/0000-0002-5581-2255","contributorId":376,"corporation":false,"usgs":true,"family":"Forde","given":"Arnell","email":"aforde@usgs.gov","middleInitial":"S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":481143,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dadisman, Shawn V. sdadisman@usgs.gov","contributorId":2207,"corporation":false,"usgs":true,"family":"Dadisman","given":"Shawn","email":"sdadisman@usgs.gov","middleInitial":"V.","affiliations":[],"preferred":true,"id":481144,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wiese, Dana S. dwiese@usgs.gov","contributorId":2476,"corporation":false,"usgs":true,"family":"Wiese","given":"Dana","email":"dwiese@usgs.gov","middleInitial":"S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":481145,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Phelps, Daniel C.","contributorId":88194,"corporation":false,"usgs":true,"family":"Phelps","given":"Daniel","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":481146,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70047130,"text":"ds772 - 2013 - Archive of digital chirp subbottom profile data collected during USGS cruise 11BIM01 offshore of the Chandeleur Islands, Louisiana, June 2011","interactions":[],"lastModifiedDate":"2026-05-20T16:49:07.349048","indexId":"ds772","displayToPublicDate":"2013-07-22T09:46:00","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":"772","title":"Archive of digital chirp subbottom profile data collected during USGS cruise 11BIM01 offshore of the Chandeleur Islands, Louisiana, June 2011","docAbstract":"From June 3 to 13, 2011, the U.S. Geological Survey conducted a geophysical survey to investigate the geologic controls on barrier island framework and long-term sediment transport along the oil spill mitigation sand berm constructed at the north end and just offshore of the Chandeleur Islands, LA. This effort is part of a broader USGS study, which seeks to better understand barrier island evolution over medium time scales (months to years). This report serves as an archive of unprocessed digital chirp subbottom data, trackline maps, navigation files, Geographic Information System (GIS) files, Field Activity Collection System (FACS) logs, and formal Federal Geographic Data Committee (FGDC) metadata. Gained (showing a relative increase in signal amplitude) digital images of the seismic profiles are also provided.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds772","usgsCitation":"Forde, A.S., Dadisman, S.V., Miselis, J.L., Flocks, J.G., and Wiese, D.S., 2013, Archive of digital chirp subbottom profile data collected during USGS cruise 11BIM01 Offshore of the Chandeleur Islands, Louisiana, June 2011: U.S. Geological Survey Data Series 772, HTML Document; 7 DVDs, https://doi.org/10.3133/ds772.","productDescription":"HTML Document; 7 DVDs","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2011-06-01","temporalEnd":"2011-06-30","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":504560,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_98682.htm","linkFileType":{"id":5,"text":"html"}},{"id":275199,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/772/title.html"},{"id":275198,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/772/"},{"id":275200,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds772.PNG"}],"country":"United States","state":"Louisiana","otherGeospatial":"Chandeleur Islands","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -89.033333,29.866667 ], [ -89.033333,30.116667 ], [ -88.683333,30.116667 ], [ -88.683333,29.866667 ], [ -89.033333,29.866667 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51ee4654e4b00ffbed48f845","contributors":{"authors":[{"text":"Forde, Arnell S. 0000-0002-5581-2255 aforde@usgs.gov","orcid":"https://orcid.org/0000-0002-5581-2255","contributorId":376,"corporation":false,"usgs":true,"family":"Forde","given":"Arnell","email":"aforde@usgs.gov","middleInitial":"S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":481138,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dadisman, Shawn V. sdadisman@usgs.gov","contributorId":2207,"corporation":false,"usgs":true,"family":"Dadisman","given":"Shawn","email":"sdadisman@usgs.gov","middleInitial":"V.","affiliations":[],"preferred":true,"id":481140,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miselis, Jennifer L. 0000-0002-4925-3979 jmiselis@usgs.gov","orcid":"https://orcid.org/0000-0002-4925-3979","contributorId":3914,"corporation":false,"usgs":true,"family":"Miselis","given":"Jennifer","email":"jmiselis@usgs.gov","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":481142,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flocks, James G. 0000-0002-6177-7433 jflocks@usgs.gov","orcid":"https://orcid.org/0000-0002-6177-7433","contributorId":816,"corporation":false,"usgs":true,"family":"Flocks","given":"James","email":"jflocks@usgs.gov","middleInitial":"G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":481139,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wiese, Dana S. dwiese@usgs.gov","contributorId":2476,"corporation":false,"usgs":true,"family":"Wiese","given":"Dana","email":"dwiese@usgs.gov","middleInitial":"S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":481141,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70047129,"text":"ofr20131136 - 2013 - Review of revised Klamath River Total Maximum Daily Load models from Link River Dam to Keno Dam, Oregon","interactions":[],"lastModifiedDate":"2013-07-22T09:29:47","indexId":"ofr20131136","displayToPublicDate":"2013-07-22T09:22:00","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":"2013-1136","title":"Review of revised Klamath River Total Maximum Daily Load models from Link River Dam to Keno Dam, Oregon","docAbstract":"Flow and water-quality models are being used to support the development of Total Maximum Daily Load (TMDL) plans for the Klamath River downstream of Upper Klamath Lake (UKL) in south-central Oregon. For riverine reaches, the RMA-2 and RMA-11 models were used, whereas the CE-QUAL-W2 model was used to simulate pooled reaches. The U.S. Geological Survey (USGS) was asked to review the most upstream of these models, from Link River Dam at the outlet of UKL downstream through the first pooled reach of the Klamath River from Lake Ewauna to Keno Dam. Previous versions of these models were reviewed in 2009 by USGS. Since that time, important revisions were made to correct several problems and address other issues. This review documents an assessment of the revised models, with emphasis on the model revisions and any remaining issues.\n\nThe primary focus of this review is the 19.7-mile Lake Ewauna to Keno Dam reach of the Klamath River that was simulated with the CE-QUAL-W2 model. Water spends far more time in the Lake Ewauna to Keno Dam reach than in the 1-mile Link River reach that connects UKL to the Klamath River, and most of the critical reactions affecting water quality upstream of Keno Dam occur in that pooled reach. This model review includes assessments of years 2000 and 2002 current conditions scenarios, which were used to calibrate the model, as well as a natural conditions scenario that was used as the reference condition for the TMDL and was based on the 2000 flow conditions. The natural conditions scenario included the removal of Keno Dam, restoration of the Keno reef (a shallow spot that was removed when the dam was built), removal of all point-source inputs, and derivation of upstream boundary water-quality inputs from a previously developed UKL TMDL model.\n\nThis review examined the details of the models, including model algorithms, parameter values, and boundary conditions; the review did not assess the draft Klamath River TMDL or the TMDL allocations. Attention to the details of a model is one of the best ways to identify potential problems, correct them if possible, and begin to assess the magnitude of potential model errors and uncertainty. Model users need to determine the level of acceptable uncertainty associated with their objectives, identify all sources of potential uncertainty (model uncertainty, data uncertainty, etc.), and assess their approach and results accordingly. In the draft Klamath River TMDL, the Oregon Department of Environmental Quality identified the upstream boundary conditions as the largest source of uncertainty for both the current and natural conditions scenarios, not the model algorithms or choice of model parameters. We agree that the upstream boundary conditions are one of the largest, if not the largest, source of model uncertainty; therefore, the derivation of upstream boundary conditions may be more important to the TMDL than some other model-related issues identified in this review.\n\nThe revised models contain a number of changes, some of which were done to solve small problems and are largely inconsequential to model results, but others of which are important and affect model predictions of instream concentrations. A consistent version of the model is now applied to all scenarios, and an error in the source code was corrected that had inadvertently discarded 20 percent of the incoming solar radiation in the original model. The baseline light-extinction coefficient for water was decreased and set to a consistent and defensible value across all models of reservoir reaches. Inconsistencies among the values of certain parameters in the original models, such as the ammonia nitrification rate and the decomposition rates of organic matter, have been eliminated, although the reasoning behind the final selections was not documented. The dependence of the rate of sediment oxygen demand (SOD) on temperature was modified such that the SOD rate was substantially decreased at temperatures less than 20°C, causing the model to predict higher dissolved oxygen (DO) concentrations in spring, autumn, and winter. Although that change to the temperature dependence function was done to make the function more similar to the model’s default, this change was not accompanied by any documentation of recalibration or sensitivity exercises. The maximum SOD rate for the 2002 current conditions scenario was decreased from 3.0 grams per square meter per day (g/m2/d) in the original model to 2.0 g/m2/d in the revised model, a considerable adjustment that appears to have been needed to offset effects of a change to another variable (O2LIM) that would have resulted in a substantial increase in the effective SOD rate for 2002. A 50-percent decrease in the SOD rate over a 2-year period, however, is not likely to be mirrored by field measurements, so this change may be compensating for some process that is not represented correctly in the DO budget for the current conditions scenarios.\n\nSeveral important changes were made to the natural conditions scenario. First, the elevation of the Keno reef was corrected; the elevation specified in the original model was 1 foot too high, which affected the volume of the pooled reach and the travel time through it. The most important changes to this scenario were to the upstream boundary inputs of organic matter and algae, which affect incoming fluxes of nitrogen and phosphorus. Algal biomass inputs were increased by approximately 60 percent during summer because of a change in the way those inputs were derived from results of the UKL TMDL model. Non-algal organic matter inputs were decreased, particularly in summer to correct a problem attributed to double-counting of phosphorus in the original inputs. The distribution of non-algal organic matter was changed from 20 percent dissolved in the original model to 90 percent dissolved in the revised model in response to review comments and published data. The overall sum of algal biomass and non-living organic matter was decreased, which resulted in lower inputs of total phosphorus and nitrogen. Total phosphorus inputs were less than 0.03 mg/L, and although the inputs were derived from selected results of the UKL TMDL model, these concentrations seem too low to be representative of a historically eutrophic system surrounded by extensive wetlands, peat soils, and a groundwater system high in phosphorus. The draft TMDL states that the upstream boundary conditions are the greatest source of uncertainty, greater than any uncertainty associated with the models. Efforts to improve existing models of algal growth and nutrient cycling in UKL, therefore, would provide a substantial benefit to downstream modeling efforts on the Klamath River.\n\nAlthough many improvements were made in revising the Klamath River TMDL models, some issues and uncertainties remain. Several errors in the model source code remain, but do not affect model results for this application as long as certain options and rates are not changed; future users of these models should be aware of these issues. Although the distribution of dissolved and particulate organic matter was modified for the natural conditions scenario, that distribution was not changed for the current conditions scenarios. Recent data on that distribution and the likely rates of organic matter decomposition could be used to improve these models in the future. Nitrate predictions at Keno (Highway 66) still are too high for the current conditions scenarios; future efforts should re-evaluate the model’s denitrification rates and the release rate of ammonia from anoxic sediments. Possibly the most important of the remaining issues are tied to the two-state (healthy/unhealthy) hypothesis for the algae population that was coded into the model. Some of the rates and conversion functions could be refined to make them more acceptable; currently, the published literature does not support the concept of moderately low dissolved-oxygen concentrations as a stressor of algae in the ranges used by the model. More research is needed before these algorithms can be truly tested. The algorithms currently appear to help the model fit the patterns in the available data, and that is useful and perhaps sufficient for some purposes, but those algorithms are not truly predictive or reliable for certain purposes until they can be tested through well-designed experiments and research.\n\nIn summary, the TMDL models used to simulate Link and Klamath Rivers from Link River Dam to Keno Dam were revised to fix several problems and address various issues. The resulting models are an improvement over those that were reviewed by USGS in 2009, and represent a useful advance in the simulation of a complex system that is difficult to model. However, several issues remain that cause increased uncertainty in the model results. Depending on the objectives of the modeling, now or in the future, these remaining issues could be more or less important. For the Klamath River TMDL, the upstream boundary conditions may be a larger source of uncertainty than the concerns with model algorithms and model parameters identified in this review. Efforts to re-evaluate the available models of algal growth and nutrient cycling in UKL would be highly beneficial to downstream modeling efforts in the Klamath River.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131136","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Rounds, S.A., and Sullivan, A.B., 2013, Review of revised Klamath River Total Maximum Daily Load models from Link River Dam to Keno Dam, Oregon: U.S. Geological Survey Open-File Report 2013-1136, vi, 31 p., https://doi.org/10.3133/ofr20131136.","productDescription":"vi, 31 p.","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":275196,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131136.PNG"},{"id":275195,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1136/pdf/ofr20131136.pdf"},{"id":275194,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1136/"}],"country":"United States","state":"Oregon;California","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.5,40.5 ], [ -124.5,43.0 ], [ -120.75,43.0 ], [ -120.75,40.5 ], [ -124.5,40.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51ee465be4b00ffbed48f879","contributors":{"authors":[{"text":"Rounds, Stewart A. 0000-0002-8540-2206 sarounds@usgs.gov","orcid":"https://orcid.org/0000-0002-8540-2206","contributorId":905,"corporation":false,"usgs":true,"family":"Rounds","given":"Stewart","email":"sarounds@usgs.gov","middleInitial":"A.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":481136,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sullivan, Annett B. 0000-0001-7783-3906 annett@usgs.gov","orcid":"https://orcid.org/0000-0001-7783-3906","contributorId":56317,"corporation":false,"usgs":true,"family":"Sullivan","given":"Annett","email":"annett@usgs.gov","middleInitial":"B.","affiliations":[],"preferred":false,"id":481137,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70047122,"text":"ofr20131106 - 2013 - Streamflow characterization and summary of water-quality data collection during the Mississippi River flood, April through July 2011","interactions":[],"lastModifiedDate":"2013-07-19T10:16:02","indexId":"ofr20131106","displayToPublicDate":"2013-07-19T09:55:00","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":"2013-1106","title":"Streamflow characterization and summary of water-quality data collection during the Mississippi River flood, April through July 2011","docAbstract":"From April through July 2011, the U.S. Geological Survey collected surface-water samples from 69 water-quality stations and 3 flood-control structures in 4 major subbasins of the Mississippi River Basin to characterize the water quality during the 2011 Mississippi River flood. Most stations were sampled at least monthly for field parameters suspended sediment, nutrients, and selected pesticides. Samples were collected at daily to biweekly frequencies at selected sites in the case of suspended sediment. Hydro-carbon analysis was performed on samples collected at two sites in the Atchafalaya River Basin to assess the water-quality implications of opening the Morganza Floodway. Water-quality samples obtained during the flood period were collected at flows well above normal streamflow conditions at the majority of the stations throughout the Mississippi River Basin and its subbasins.\n\nHeavy rainfall and snowmelt resulted in high streamflow in the Mississippi River Basin from April through July 2011. The Ohio River Subbasin contributed to most of the flow in the lower Mississippi-Atchafalaya River Subbasin during the months of April and May because of widespread rainfall, whereas snowmelt and precipitation from the Missouri River Subbasin and the upper Mississippi River Subbasin contributed to most of the flow in the lower Mississippi-Atchafalaya River Subbasin during June and July. Peak streamflows from the 2011 flood were higher than peak streamflow during previous historic floods at most the selected streamgages in the Mississippi River Basin. In the Missouri River Subbasin, the volume of water moved during the 1952 flood was greater than the amount move during the 2011 flood.\n\nMedian concentrations of suspended sediment and total phosphorus were higher in the Missouri River Subbasin during the flood when compared to the other three subbasins. Surface water in the upper Mississippi River Subbasin contained higher median concentrations of total nitrogen, nitrate, orthophosphate, and atrazine during the flood period.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131106","collaboration":"National Stream Quality Accounting Network; National Water-Quality Assessment Program","usgsCitation":"Welch, H.L., and Barnes, K., 2013, Streamflow characterization and summary of water-quality data collection during the Mississippi River flood, April through July 2011: U.S. Geological Survey Open-File Report 2013-1106, v, 27 p.; 8 Appendixes, https://doi.org/10.3133/ofr20131106.","productDescription":"v, 27 p.; 8 Appendixes","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2011-03-01","temporalEnd":"2011-07-31","costCenters":[{"id":394,"text":"Mississippi Water Science Center","active":true,"usgs":true}],"links":[{"id":275179,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131106.gif"},{"id":275171,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1106/appendix/Appendix01.xlsx"},{"id":275169,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1106/"},{"id":275170,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1106/pdf/ofr2013-1106.pdf"},{"id":275172,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1106/appendix/Appendix02.xlsx"},{"id":275173,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1106/appendix/Appendix03.xlsx"},{"id":275174,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1106/appendix/Appendix04.xlsx"},{"id":275175,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1106/appendix/Appendix05.xlsx"},{"id":275176,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1106/appendix/Appendix06.xlsx"},{"id":275177,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1106/appendix/Appendix07.xlsx"},{"id":275178,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1106/appendix/Appendix08.xlsx"}],"country":"United States;Canada","otherGeospatial":"Mississippi River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -130.0,20.0 ], [ -130.0,55.0 ], [ -65.0,55.0 ], [ -65.0,20.0 ], [ -130.0,20.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51ea86c5e4b03397884d3984","contributors":{"authors":[{"text":"Welch, Heather L. 0000-0001-8370-7711 hllott@usgs.gov","orcid":"https://orcid.org/0000-0001-8370-7711","contributorId":552,"corporation":false,"usgs":true,"family":"Welch","given":"Heather","email":"hllott@usgs.gov","middleInitial":"L.","affiliations":[{"id":105,"text":"Alabama Water Science Center","active":true,"usgs":true}],"preferred":true,"id":481128,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnes, Kimberlee K.","contributorId":41476,"corporation":false,"usgs":true,"family":"Barnes","given":"Kimberlee K.","affiliations":[],"preferred":false,"id":481129,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70047113,"text":"ofr20131124 - 2013 - Topographic and hydrographic GIS datasets for the Afghan Geological Survey and U.S. Geological Survey 2013 mineral areas of interest","interactions":[],"lastModifiedDate":"2013-07-18T15:35:59","indexId":"ofr20131124","displayToPublicDate":"2013-07-18T15:27:00","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":"2013-1124","title":"Topographic and hydrographic GIS datasets for the Afghan Geological Survey and U.S. Geological Survey 2013 mineral areas of interest","docAbstract":"Afghanistan is endowed with a vast amount of mineral resources, and it is believed that the current economic state of the country could be greatly improved through investment in the extraction and production of these resources. In 2007, the “Preliminary Non-Fuel Resource Assessment of Afghanistan 2007” was completed by members of the U.S. Geological Survey and Afghan Geological Survey (Peters and others, 2007). The assessment delineated 20 mineralized areas for further study using a geologic-based methodology. In 2011, a follow-on data product, “Summaries and Data Packages of Important Areas for Mineral Investment and Production Opportunities of Nonfuel Minerals in Afghanistan,” was released (Peters and others, 2011). As part of this more recent work, geologic, geohydrologic, and hyperspectral studies were carried out in the areas of interest (AOIs) to assess the location and characteristics of the mineral resources. The 2011 publication included a dataset of 24 identified AOIs containing subareas, a corresponding digital elevation model (DEM), elevation contours, areal extent, and hydrography for each AOI. In 2012, project scientists identified five new AOIs and two subareas in Afghanistan. These new areas are Ahankashan, Kandahar, Parwan, North Bamyan, and South Bamyan. The two identified subareas include Obatu-Shela and Sekhab-ZamtoKalay, both located within the larger Kandahar AOI. In addition, an extended Kandahar AOI is included in the project for water resource modeling purposes. The dataset presented in this publication consists of the areal extent of the five new AOIs, two subareas, and the extended Kandahar AOI, elevation contours at 100-, 50-, and 25-meter intervals, an enhanced DEM, and a hydrographic dataset covering the extent of the new study area. The resulting raster and vector layers are intended for use by government agencies, developmental organizations, and private companies in Afghanistan to assist with mineral assessments, monitoring, management, and investment.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131124","collaboration":"Prepared in cooperation with the Afghan Geological Survey under the auspices of the U.S. Department of Defense Task Force for Business and Stability Operations","usgsCitation":"Casey, B.N., and Chirico, P., 2013, Topographic and hydrographic GIS datasets for the Afghan Geological Survey and U.S. Geological Survey 2013 mineral areas of interest: U.S. Geological Survey Open-File Report 2013-1124, Report: vi, 18 p.; Downloads Directory, https://doi.org/10.3133/ofr20131124.","productDescription":"Report: vi, 18 p.; Downloads Directory","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":275158,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2013/1124/Downloads"},{"id":275159,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131124.gif"},{"id":275156,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1124/"},{"id":275157,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1124/pdf/ofr2013-1124.pdf"}],"country":"Afghanistan","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 60.52,29.38 ], [ 60.52,38.49 ], [ 74.89,38.49 ], [ 74.89,29.38 ], [ 60.52,29.38 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51e90055e4b0e157e9e86eea","contributors":{"authors":[{"text":"Casey, Brittany N.","contributorId":69037,"corporation":false,"usgs":true,"family":"Casey","given":"Brittany","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":481085,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chirico, Peter G.","contributorId":27086,"corporation":false,"usgs":true,"family":"Chirico","given":"Peter G.","affiliations":[],"preferred":false,"id":481084,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70188809,"text":"70188809 - 2013 - New thermochronometric constraints on the Tertiary landscape evolution of the central and eastern Grand Canyon, Arizona","interactions":[],"lastModifiedDate":"2017-06-27T11:07:55","indexId":"70188809","displayToPublicDate":"2013-07-18T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"New thermochronometric constraints on the Tertiary landscape evolution of the central and eastern Grand Canyon, Arizona","docAbstract":"Thermal histories are modeled from new apatite (U-Th)/He and apatite fission-track data in order to quantitatively constrain the landscape evolution of the Grand Canyon region. Fifty new samples and their associated thermochronometric ages are presented here. Samples span from Lee’s Ferry in the east to Quartermaster Canyon in the west and include four age-elevation transects into Grand Canyon and borehole samples from the Coconino Plateau. Twenty-seven samples are inversely modeled to provide continuous thermal histories. This represents the most extensive and complete dataset on patterns of long-term exhumation in the Grand Canyon region, and it enables us to constrain the timing and magnitude of erosion and also discriminate between canyon incision and broader planation. The new data suggest that the early Cenozoic landscape in eastern Grand Canyon was low in relief and does not indicate the presence of an early Cenozoic precursor to the modern Grand Canyon. However, there is evidence for the incision of a smaller-scale canyon across the Kaibab Uplift at 28–20 Ma. This middle-Cenozoic denudation event was accompanied by the removal of a majority of remaining Mesozoic strata west of the Kaibab Uplift. In contrast, just upstream in the area of Lee’s Ferry, ∼2 km of Mesozoic strata remained over the middle Cenozoic and were removed after 10 Ma.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES00842.1","usgsCitation":"Lee, J.P., Stockli, D.F., Kelley, S., Pederson, J., Karlstrom, K.E., and Ehlers, T., 2013, New thermochronometric constraints on the Tertiary landscape evolution of the central and eastern Grand Canyon, Arizona: Geosphere, v. 9, no. 2, p. 216-228, https://doi.org/10.1130/GES00842.1.","productDescription":"13 p.","startPage":"216","endPage":"228","ipdsId":"IP-039069","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":473671,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges00842.1","text":"Publisher Index Page"},{"id":342893,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.04632568359375,\n 35.576916524038616\n ],\n [\n -111.29974365234375,\n 35.576916524038616\n ],\n [\n -111.29974365234375,\n 37.00255267215955\n ],\n [\n -114.04632568359375,\n 37.00255267215955\n ],\n [\n -114.04632568359375,\n 35.576916524038616\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59521d28e4b062508e3c36cd","contributors":{"authors":[{"text":"Lee, John P. jplee@usgs.gov","contributorId":3291,"corporation":false,"usgs":true,"family":"Lee","given":"John","email":"jplee@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":true,"id":700458,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stockli, Daniel F.","contributorId":78073,"corporation":false,"usgs":true,"family":"Stockli","given":"Daniel","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":700674,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelley, S.A.","contributorId":31151,"corporation":false,"usgs":true,"family":"Kelley","given":"S.A.","email":"","affiliations":[],"preferred":false,"id":700675,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pederson, J.","contributorId":11413,"corporation":false,"usgs":true,"family":"Pederson","given":"J.","email":"","affiliations":[],"preferred":false,"id":700676,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Karlstrom, K. E.","contributorId":45713,"corporation":false,"usgs":true,"family":"Karlstrom","given":"K.","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":700677,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ehlers, T.A.","contributorId":193510,"corporation":false,"usgs":false,"family":"Ehlers","given":"T.A.","email":"","affiliations":[],"preferred":false,"id":700678,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70188030,"text":"70188030 - 2013 - Forest cutting and impacts on carbon in the eastern United States","interactions":[],"lastModifiedDate":"2017-05-31T16:13:26","indexId":"70188030","displayToPublicDate":"2013-07-18T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Forest cutting and impacts on carbon in the eastern United States","docAbstract":"Forest cutting is a major anthropogenic disturbance that affects forest carbon (C) storage and fluxes. Yet its characteristics and impacts on C cycling are poorly understood over large areas. Using recent annualized forest inventory data, we estimated cutting-related loss of live biomass in the eastern United States was 168 Tg C yr−1 from 2002 to 2010 (with C loss per unit forest area of 1.07 Mg ha−1 yr−1), which is equivalent to 70% of the total U.S. forest C sink or 11% of the national annual CO2 emissions from fossil-fuel combustion over the same period. We further revealed that specific cutting-related C loss varied with cutting intensities, forest types, stand ages, and geographic locations. Our results provide new insights to the characteristics of forest harvesting activities in the eastern United States and highlight the significance of partial cutting to regional and national carbon budgets.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/srep03547","usgsCitation":"Zhou, D., Liu, S., Oeding, J., and Zhao, S., 2013, Forest cutting and impacts on carbon in the eastern United States: Scientific Reports, v. 3, Article 3547: 7 p., https://doi.org/10.1038/srep03547.","productDescription":"Article 3547: 7 p.","ipdsId":"IP-052367","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":473672,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/srep03547","text":"Publisher Index Page"},{"id":341887,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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A 0% increase in the tolerance score (TIVo/TIVe = 1) for nitrate corresponded to a nitrate concentration of 0.19 mg/l (compared with a USEPA summer nitrate criterion of 0.17 mg/l) in the urban area and 0.31 mg/l (compared with a USEPA summer nitrate criterion of 0.86 mg/l) in the agricultural area. Fish nutrient tolerance values offer the ability to evaluate nutrient enrichment based on a quantitative approach that can provide insights into biologically relevant nutrient criteria.
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2013 - U.S. Geological Survey water-resource monitoring activities in support of the Wyoming Landscape Conservation Initiative","interactions":[],"lastModifiedDate":"2013-07-17T13:01:18","indexId":"70047088","displayToPublicDate":"2013-07-17T12:53:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":234,"text":"WLCI Fact Sheet","active":false,"publicationSubtype":{"id":3}},"seriesNumber":"4","title":"U.S. Geological Survey water-resource monitoring activities in support of the Wyoming Landscape Conservation Initiative","docAbstract":"The quality of the Nation’s water resources are vital to the health and well-being of both our communities and the natural landscapes we value. 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The U.S. Geological Survey collects and shares data nationwide, but how those data are used is often site specific; this variety of data assists natural-resource managers in addressing unique, local, and regional challenges.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","usgsCitation":"Soileau, S., and Miller, K., 2013, U.S. Geological Survey water-resource monitoring activities in support of the Wyoming Landscape Conservation Initiative: WLCI Fact Sheet 4, 2 p.","productDescription":"2 p.","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":275119,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/70047088.gif"},{"id":275117,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/wlci/fs/4/"},{"id":275118,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/wlci/fs/4/WLCI_fs_4.pdf"}],"country":"United States","state":"Wyoming","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -111.0569,40.9947 ], [ -111.0569,45.0059 ], [ -104.0522,45.0059 ], [ -104.0522,40.9947 ], [ -111.0569,40.9947 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51e7aed8e4b080b82b09c61e","contributors":{"authors":[{"text":"Soileau, Suzanna 0000-0002-4331-0098","orcid":"https://orcid.org/0000-0002-4331-0098","contributorId":57349,"corporation":false,"usgs":true,"family":"Soileau","given":"Suzanna","affiliations":[],"preferred":false,"id":481032,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Kirk","contributorId":81891,"corporation":false,"usgs":true,"family":"Miller","given":"Kirk","affiliations":[],"preferred":false,"id":481033,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70046847,"text":"70046847 - 2013 - Hysteresis in suspended sediment to turbidity relations due to changing particle size distributions","interactions":[],"lastModifiedDate":"2013-10-23T13:59:24","indexId":"70046847","displayToPublicDate":"2013-07-17T11:11:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Hysteresis in suspended sediment to turbidity relations due to changing particle size distributions","docAbstract":"Turbidity (T) is the most ubiquitous of surrogate technologies used to estimate suspended-sediment concentration (SSC). The effects of sediment size on turbidity are well documented; however, effects from changes in particle size distributions (PSD) are rarely evaluated. Hysteresis in relations of SSC-to-turbidity (SSC~T) for single stormflow events was observed and quantified for a data set of 195 concurrent measurements of SSC, turbidity, discharge, velocity, and volumetric PSD collected during five stormflows in 2009–2010 on Yellow River at Gees Mill Road in metropolitan Atlanta, Georgia. Regressions of SSC-normalized turbidity (T/SSC) on concurrently measured PSD percentiles show an inverse, exponential influence of particle size on turbidity that is not constant across the size range of the PSD. The majority of the influence of PSD on T/SSC is from particles of fine-silt and smaller sizes (finer than 16 microns). This study shows that small changes in the often assumed stability of the PSD are significant to SSC~T relations. Changes of only 5 microns in the fine silt and smaller size fractions of suspended sediment PSD can produce hysteresis in the SSC~T rating that can increase error and produce bias. Observed SSC~T hysteresis may be an indicator of changes in sediment properties during stormflows and of potential changes in sediment sources. Trends in the PSD time series indicate that sediment transport is capacity-limited for sand-sized sediment in the channel and supply-limited for fine silt and smaller sediment from the hillslope.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Water Resources Research","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Geophysical Union","doi":"10.1002/wrcr.20394","usgsCitation":"Landers, M.N., and Sturm, T.W., 2013, Hysteresis in suspended sediment to turbidity relations due to changing particle size distributions: Water Resources Research, v. 49, no. 9, p. 5487-5500, https://doi.org/10.1002/wrcr.20394.","productDescription":"14 p.","startPage":"5487","endPage":"5500","numberOfPages":"14","ipdsId":"IP-040416","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":275109,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":275108,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/wrcr.20394"}],"scale":"100000","country":"United States","state":"Georgia","otherGeospatial":"Yellow River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -84.256897,33.648922 ], [ -84.256897,34.100434 ], [ -83.909626,34.100434 ], [ -83.909626,33.648922 ], [ -84.256897,33.648922 ] ] ] } } ] }","volume":"49","issue":"9","noUsgsAuthors":false,"publicationDate":"2013-09-09","publicationStatus":"PW","scienceBaseUri":"51e7aed6e4b080b82b09c606","contributors":{"authors":[{"text":"Landers, Mark N. 0000-0002-3014-0480 landers@usgs.gov","orcid":"https://orcid.org/0000-0002-3014-0480","contributorId":1103,"corporation":false,"usgs":true,"family":"Landers","given":"Mark","email":"landers@usgs.gov","middleInitial":"N.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":480452,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sturm, Terry W.","contributorId":36445,"corporation":false,"usgs":true,"family":"Sturm","given":"Terry","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":480453,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}