Brood division in the postfledging period is a common avian behavior that is not well understood. Brood division has been reported in Golden-winged Warblers (Vermivora chrysoptera), but it is not known how common this behavior is, whether males and females exhibit different strategies related to parental care and habitat use, or how brood division might influence management strategies. We radiomarked fledglings and monitored divided broods of Golden-winged Warblers from fledging until independence from parental care at three sites in the western Great Lakes region from 2010 to 2012 to assess differences in strategies between male and female parents and to consider possible management implications. Male - and female-reared sub-broods exhibited different space use during the dependent post-fledging period despite similar fledgling survivial, cover-type use, and microhabitat use. By independence, female-reared sub-broods traveled over twice as far from the nest (mean = 461 ± 81) SE m) as male-reared sub-broods (164 ± 41 m). Additionally, female-reared sub-broods traveled over three times as far from the natal patch edge (35 ± 72 m) as male-reared sub-broods (108 ± 36 m). Without accounting for differential space use by male- and female-reared sub-broods, we would have reported broods traveling 292 (± 46 m) from the nest and 214 (± 40m) from the natal patch edge - distances that do not reflect how far females move sub-broods. Parental strategies differ between sexes with regard to movement patterns, and we recommend incorporating the differences in space use between sexes in future management plans for Golden-winged Warblers and other species that employ brood division. Specifically, management actions might be most effective when they are applied at spatial scales large enough to incorporate the habitat requirements of both sexes throughout the entire reproductive season.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Golden-winged Warbler ecology, conservation, and habitat management (Studies in Avian Biology, volume 49)","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"CRC Press","publisherLocation":"Boca Raton, FL","isbn":"978-1-4822-4068-9","usgsCitation":"Peterson, S.M., Streby, H.M., and Andersen, D., 2016, Management implications of brood division in Golden-winged Warblers, chap. 10 of Golden-winged Warbler ecology, conservation, and habitat management (Studies in Avian Biology, volume 49): Studies in Avian Biology, v. 49, p. 161-171.","productDescription":"10 p.","startPage":"161","endPage":"171","ipdsId":"IP-052104","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":344240,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":345563,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://hdl.handle.net/11299/189700"}],"volume":"49","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5977074fe4b0ec1a48889f67","contributors":{"authors":[{"text":"Peterson, Sean M.","contributorId":9354,"corporation":false,"usgs":false,"family":"Peterson","given":"Sean","email":"","middleInitial":"M.","affiliations":[{"id":13013,"text":"Department of Environmental Science, Policy and Management, University of California, Berkeley","active":true,"usgs":false},{"id":34539,"text":"Minnesota Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":709864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Streby, Henry M.","contributorId":11024,"corporation":false,"usgs":false,"family":"Streby","given":"Henry","email":"","middleInitial":"M.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":709865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":2168,"corporation":false,"usgs":true,"family":"Andersen","given":"David E.","email":"dea@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":34539,"text":"Minnesota Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":true,"id":709866,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178572,"text":"70178572 - 2016 - Mapping site index and volume increment from forest inventory, Landsat, and ecological variables in Tahoe National Forest, California, USA","interactions":[],"lastModifiedDate":"2017-01-03T16:02:50","indexId":"70178572","displayToPublicDate":"2016-11-29T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1170,"text":"Canadian Journal of Forest Research","active":true,"publicationSubtype":{"id":10}},"title":"Mapping site index and volume increment from forest inventory, Landsat, and ecological variables in Tahoe National Forest, California, USA","docAbstract":"High-resolution site index (SI) and mean annual increment (MAI) maps are desired for local forest management. We integrated field inventory, Landsat, and ecological variables to produce 30 m SI and MAI maps for the Tahoe National Forest (TNF) where different tree species coexist. We converted species-specific SI using adjustment factors. Then, the SI map was produced by (i) intensifying plots to expand the training sets to more climatic, topographic, soil, and forest reflective classes, (ii) using results from a stepwise regression to enable a weighted imputation that minimized the effects of outlier plots within classes, and (iii) local interpolation and strata median filling to assign values to pixels without direct imputations. The SI (reference age is 50 years) map had an R2 of 0.7637, a root-mean-square error (RMSE) of 3.60, and a mean absolute error (MAE) of 3.07 m. The MAI map was similarly produced with an R2 of 0.6882, an RMSE of 1.73, and a MAE of 1.20 m3·ha−1·year−1. Spatial patterns and trends of SI and MAI were analyzed to be related to elevation, aspect, slope, soil productivity, and forest type. The 30 m SI and MAI maps can be used to support decisions on fire, plantation, biodiversity, and carbon.
","language":"English","publisher":"NRC Research Press","doi":"10.1139/cjfr-2016-0209","usgsCitation":"Huang, S., Ramirez, C., Conway, S., Kennedy, K., Kohler, T., and Liu, J., 2016, Mapping site index and volume increment from forest inventory, Landsat, and ecological variables in Tahoe National Forest, California, USA: Canadian Journal of Forest Research, v. 47, no. 1, p. 113-124, https://doi.org/10.1139/cjfr-2016-0209.","productDescription":"12 p.","startPage":"113","endPage":"124","ipdsId":"IP-076665","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":470401,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://www.nrcresearchpress.com/doi/abs/10.1139/cjfr-2016-0209","text":"External Repository"},{"id":331299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"583ea1bfe4b0f0dc05ea54e1","contributors":{"authors":[{"text":"Huang, Shengli shuang@usgs.gov","contributorId":1926,"corporation":false,"usgs":true,"family":"Huang","given":"Shengli","email":"shuang@usgs.gov","affiliations":[],"preferred":true,"id":654460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ramirez, Carlos","contributorId":177061,"corporation":false,"usgs":false,"family":"Ramirez","given":"Carlos","email":"","affiliations":[],"preferred":false,"id":654461,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conway, Scott","contributorId":177062,"corporation":false,"usgs":false,"family":"Conway","given":"Scott","email":"","affiliations":[],"preferred":false,"id":654462,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kennedy, Kama","contributorId":177063,"corporation":false,"usgs":false,"family":"Kennedy","given":"Kama","email":"","affiliations":[],"preferred":false,"id":654463,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kohler, Tanya","contributorId":177064,"corporation":false,"usgs":false,"family":"Kohler","given":"Tanya","email":"","affiliations":[],"preferred":false,"id":654464,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Liu, Jinxun 0000-0003-0561-8988 jxliu@usgs.gov","orcid":"https://orcid.org/0000-0003-0561-8988","contributorId":3414,"corporation":false,"usgs":true,"family":"Liu","given":"Jinxun","email":"jxliu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":654465,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70178359,"text":"sir20165163 - 2016 - Borehole deviation and correction factor data for selected wells in the eastern Snake River Plain aquifer at and near the Idaho National Laboratory, Idaho","interactions":[],"lastModifiedDate":"2016-11-30T10:35:45","indexId":"sir20165163","displayToPublicDate":"2016-11-29T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5163","title":"Borehole deviation and correction factor data for selected wells in the eastern Snake River Plain aquifer at and near the Idaho National Laboratory, Idaho","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Energy, has maintained a water-level monitoring program at the Idaho National Laboratory (INL) since 1949. The purpose of the program is to systematically measure and report water-level data to assess the eastern Snake River Plain aquifer and long term changes in groundwater recharge, discharge, movement, and storage. Water-level data are commonly used to generate potentiometric maps and used to infer increases and (or) decreases in the regional groundwater system. Well deviation is one component of water-level data that is often overlooked and is the result of the well construction and the well not being plumb. Depending on measured slant angle, where well deviation generally increases linearly with increasing slant angle, well deviation can suggest artificial anomalies in the water table. To remove the effects of well deviation, the USGS INL Project Office applies a correction factor to water-level data when a well deviation survey indicates a change in the reference elevation of greater than or equal to 0.2 ft.
Borehole well deviation survey data were considered for 177 wells completed within the eastern Snake River Plain aquifer, but not all wells had deviation survey data available. As of 2016, USGS INL Project Office database includes: 57 wells with gyroscopic survey data; 100 wells with magnetic deviation survey data; 11 wells with erroneous gyroscopic data that were excluded; and, 68 wells with no deviation survey data available. Of the 57 wells with gyroscopic deviation surveys, correction factors for 16 wells ranged from 0.20 to 6.07 ft and inclination angles (SANG) ranged from 1.6 to 16.0 degrees. Of the 100 wells with magnetic deviation surveys, a correction factor for 21 wells ranged from 0.20 to 5.78 ft and SANG ranged from 1.0 to 13.8 degrees, not including the wells that did not meet the correction factor criteria of greater than or equal to 0.20 ft.
Forty-seven wells had gyroscopic and magnetic deviation survey data for the same well. Datasets for both survey types were compared for the same well to determine whether magnetic survey data were consistent with gyroscopic survey data. Of those 47 wells, 96 percent showed similar correction factor estimates (≤ 0.20 ft) for both magnetic and gyroscopic well deviation surveys. A linear comparison of correction factor estimates for both magnetic and gyroscopic deviation well surveys for all 47 wells indicate good linear correlation, represented by an r-squared of 0.88. The correction factor difference between the gyroscopic and magnetic surveys for 45 of 47 wells ranged from 0.00 to 0.18 ft, not including USGS 57 and USGS 125. Wells USGS 57 and USGS 125 show a correction factor difference of 2.16 and 0.36 ft, respectively; however, review of the data files suggest erroneous SANG data for both magnetic deviation well surveys. The difference in magnetic and gyroscopic well deviation SANG measurements, for all wells, ranged from 0.0 to 0.9 degrees. These data indicate good agreement between SANG data measured using the magnetic deviation survey methods and SANG data measured using gyroscopic deviation survey methods, even for surveys collected years apart.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165163","collaboration":"DOE/ID-22241Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
http://id.water.usgs.gov
During 3 years of study (2010–2012), the western Arctic Ocean was found to have unique aragonite saturation profiles with up to three distinct aragonite undersaturation zones. This complexity is produced as inflow of Atlantic-derived and Pacific-derived water masses mix with Arctic-derived waters, which are further modified by physiochemical and biological processes. The shallowest aragonite undersaturation zone, from the surface to ∼30 m depth is characterized by relatively low alkalinity and other dissolved ions. Besides local influence of biological processes on aragonite undersaturation of shallow coastal waters, the nature of this zone is consistent with dilution by sea-ice melt and invasion of anthropogenic CO2 from the atmosphere. A second undersaturated zone at ∼90–220 m depth (salinity ∼31.8–35.4) occurs within the Arctic Halocline and is characterized by elevated pCO2 and nutrients. The nature of this horizon is consistent with remineralization of organic matter on shallow continental shelves bordering the Canada Basin and the input of the nutrients and CO2 entrained by currents from the Pacific Inlet. Finally, the deepest aragonite undersaturation zone is at greater than 2000 m depth and is controlled by similar processes as deep aragonite saturation horizons in the Atlantic and Pacific Oceans. The comparatively shallow depth of this deepest aragonite saturation horizon in the Arctic is maintained by relatively low temperatures, and stable chemical composition. Understanding the mechanisms controlling the distribution of these aragonite undersaturation zones, and the time scales over which they operate will be crucial to refine predictive models.
","language":"English","publisher":"AGU","doi":"10.1002/2016JC011696","usgsCitation":"Wynn, J., Robbins, L.L., and Anderson, L., 2016, Processes of multibathyal aragonite undersaturation in the Arctic Ocean: Journal of Geophysical Research: Oceans, v. 121, no. 11, p. 8248-8267, https://doi.org/10.1002/2016JC011696.","productDescription":"20 p.","startPage":"8248","endPage":"8267","ipdsId":"IP-059393","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470402,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016jc011696","text":"Publisher Index Page"},{"id":331298,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Arctic Ocean","volume":"121","issue":"11","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-16","publicationStatus":"PW","scienceBaseUri":"583ea1bee4b0f0dc05ea54df","contributors":{"authors":[{"text":"Wynn, J.G.","contributorId":16215,"corporation":false,"usgs":true,"family":"Wynn","given":"J.G.","email":"","affiliations":[],"preferred":false,"id":654443,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robbins, L. L.","contributorId":71156,"corporation":false,"usgs":true,"family":"Robbins","given":"L.","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":654444,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, L.G.","contributorId":36727,"corporation":false,"usgs":true,"family":"Anderson","given":"L.G.","email":"","affiliations":[],"preferred":false,"id":654445,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178362,"text":"sir20165162 - 2016 - Characterization of peak streamflows and flood inundation of selected areas in Louisiana, Texas, Arkansas, and Mississippi from flood of March 2016","interactions":[],"lastModifiedDate":"2016-11-30T10:23:14","indexId":"sir20165162","displayToPublicDate":"2016-11-29T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5162","title":"Characterization of peak streamflows and flood inundation of selected areas in Louisiana, Texas, Arkansas, and Mississippi from flood of March 2016","docAbstract":"Heavy rainfall occurred across Louisiana, Texas, Arkansas, and Mississippi in March 2016 as a result of a slow-moving southward dip in the jetstream, funneling tropical moisture into parts of the Gulf Coast States and the Mississippi River Valley. The storm caused major flooding in the northwestern and southeastern parts of Louisiana and in eastern Texas. Flooding also occurred in the Mississippi River Valley in Arkansas and Mississippi. Over 26 inches of rain were reported near Monroe, Louisiana, over the duration of the storm. In March 2016, U.S. Geological Survey (USGS) hydrographers made more than 500 streamflow measurements in Louisiana, Texas, Arkansas, and Mississippi. Many of those streamflow measurements were made to verify the accuracy of stage-streamflow relations at gaging stations operated by the USGS. Peak streamflows were the highest on record at 14 locations, and streamflows at 29 locations ranked in the top five for the period of record at USGS streamflow-gaging stations analyzed for this report. Following the storm, USGS hydrographers documented 451 high-water marks in Louisiana and on the western side of the Sabine River in Texas. Many of these high-water marks were used to create 19 flood-inundation maps for selected areas of Louisiana and Texas that experienced flooding in March 2016.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165162","collaboration":"Prepared in cooperation with the Federal Emergency Management Administration","usgsCitation":"Breaker, B.K., Watson, K.M., Ensminger, P.A., Storm, J.B., and Rose, C.E., 2016,Characterization of peak streamflows and flood inundation of selected areas in Louisiana, Texas, Arkansas, and Mississippi from flood of March 2016: U.S. Geological Survey Scientific Investigations Report 2016–5162, 33 p. https://doi.org/10.3133/sir20165162.","productDescription":"Report: vi, 33 p.; Data Release","startPage":"1","endPage":"33","numberOfPages":"43","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-080223","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":331216,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5162/coverthb2.jpg"},{"id":331217,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5162/sir20165162.pdf","text":"Report","size":"12.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5162"},{"id":331218,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7T43R6C","text":"USGS data release - Flood inundation extent and depth in selected areas of Louisiana, Texas, and Mississippi in March 2016","description":"USGS data release"}],"country":"United States","state":"Arkansas, Louisiana, Mississippi, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88,\n 29\n ],\n [\n -88,\n 35\n ],\n [\n -95,\n 35\n ],\n [\n -95,\n 29\n ],\n [\n -88,\n 29\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
401 Hardin Road
Little Rock, AR 72211
In 2012, the U.S. Geological Survey, in cooperation with the Arkansas River Basin Regional Resource Planning Group, initiated a study of groundwater and surface-water interaction, water quality, and loading of dissolved solids, selenium, and uranium to Fountain Creek near Pueblo, Colorado, to improve understanding of sources and processes affecting loading of these constituents to streams in the Arkansas River Basin. Fourteen monitoring wells were installed in a series of three transects across Fountain Creek near Pueblo, and temporary streamgages were established at each transect to facilitate data collection for the study. Groundwater and surface-water interaction was characterized by using hydrogeologic mapping, groundwater and stream-surface levels, groundwater and stream temperatures, vertical hydraulic-head gradients and ratios of oxygen and hydrogen isotopes in the hyporheic zone, and streamflow mass-balance measurements. Water quality was characterized by collecting periodic samples from groundwater, surface water, and the hyporheic zone for analysis of dissolved solids, selenium, uranium, and other selected constituents and by evaluating the oxidation-reduction condition for each groundwater sample under different hydrologic conditions throughout the study period. Groundwater loads to Fountain Creek and in-stream loads were computed for the study area, and processes affecting loads of dissolved solids, selenium, and uranium were evaluated on the basis of geology, geochemical conditions, land and water use, and evapoconcentration.
During the study period, the groundwater-flow system generally contributed flow to Fountain Creek and its hyporheic zone (as a single system) except for the reach between the north and middle transects. However, the direction of flow between the stream, the hyporheic zone, and the near-stream aquifer was variable in response to streamflow and stage. During periods of low streamflow, Fountain Creek generally gained flow from groundwater. However, during periods of high streamflow, the hydraulic gradient between groundwater and the stream temporarily reversed, causing the stream to lose flow to groundwater.
Concentrations of dissolved solids, selenium, and uranium in groundwater generally had greater spatial variability than surface water or hyporheic-zone samples, and constituent concentrations in groundwater generally were greater than in surface water. Constituent concentrations in the hyporheic zone typically were similar to or intermediate between concentrations in groundwater and surface water. Concentrations of dissolved solids, selenium, uranium, and other constituents in groundwater samples collected from wells located on the east side of the north monitoring well transect were substantially greater than for other groundwater, surface-water, and hyporheic-zone samples. With one exception, groundwater samples collected from wells on the east side of the north transect exhibited oxic to mixed (oxic-anoxic) conditions, whereas most other groundwater samples exhibited anoxic to suboxic conditions. Concentrations of dissolved solids, selenium, and uranium in surface water generally increased in a downstream direction along Fountain Creek from the north transect to the south transect and exhibited an inverse relation to streamflow with highest concentration occurring during periods of low streamflow and lowest concentrations occurring during periods of high streamflow.
Groundwater loads of dissolved solids, selenium, and uranium to Fountain Creek were small because of the small amount of groundwater flowing to the stream under typical low-streamflow conditions. In-stream loads of dissolved solids, selenium, and uranium in Fountain Creek varied by date, primarily in relation to streamflow at each transect and were much larger than computed constituent loads from groundwater. In-stream loads generally decreased with decreases in streamflow and increased as streamflow increased. In-stream loads of dissolved solids and selenium increased between the north and middle transects but generally decreased between the middle and south transects. By contrast, uranium loads generally decreased between the north and middle transects but increased between the middle and south transects. In-stream load differences between transects appear primarily to be related to differences in streamflow. However, because groundwater typically flows to Fountain Creek under low-flow conditions, and groundwater has greater concentrations of dissolved solids, selenium, and uranium than surface water in Fountain Creek, increases in loads between transects likely are affected by inflow of groundwater to the stream, which can account for a substantial proportion of the in-stream load difference between transects. When loads decreased between transects, the primary cause likely was decreased streamflow as a result of losses to groundwater and flow through the hyporheic zone. However, localized groundwater inflow likely attenuated the magnitude by which the in-stream loads decreased.
The combination of localized soluble geologic sources and oxic conditions likely is the primary reason for the occurrence of high concentrations of dissolved solids, selenium, and uranium in groundwater on the east side of the north monitoring well transect. To evaluate conditions potentially responsible for differences in water quality and redox conditions, physical characteristics such as depth to water, saturated thickness, screen depth below the water table, screen height above bedrock, and aquifer hydraulic conductivity were compared by using Wilcoxon rank-sum tests. Results indicated no significant difference between depth to water, screen height above bedrock, and hydraulic conductivity for groundwater samples collected from wells on the east side of the north transect and groundwater samples from all other wells. However, saturated thickness and screen depth below the water table both were significantly smaller for groundwater samples collected from wells on the east side of the north transect than for groundwater samples from other wells, indicating that these characteristics might be related to the elevated constituent concentrations found at that location. Similarly, saturated thickness and screen depth below the water table were significantly smaller for groundwater samples under oxic or mixed (oxic-anoxic) conditions than for those under anoxic to suboxic conditions.
The greater constituent concentrations at wells on the east side of the north transect also could, in part, be related to groundwater discharge from an unnamed alluvial drainage located directly upgradient from that location. Although the quantity and quality of water discharging from the drainage is not known, the drainage appears to collect water from a residential area located upgradient to the east of the wells, and groundwater could become concentrated in nitrate and other dissolved constituents before flowing through the drainage. High levels of nitrate, whether from anthropogenic or natural geologic sources, could promote more soluble forms of selenium and other constituents by affecting the redox condition of groundwater. Whether oxic conditions at wells on the east side of the north transect are the result of physical characteristics or of groundwater inflow from the alluvial drainage, the oxic conditions appear to cause increased dissolution of minerals from the shallow shale bedrock at that location. Because ratios of hydrogen and oxygen isotopes indicate evaporation likely has not had a substantial effect on groundwater, constituent concentrations at that location likely are not the result of evapoconcentration.
Director, USGS Colorado Water Science Center
Box 25046, Mail Stop 415
Denver, CO 80225
This study produced a geospatial database for use in a decision support system by the Bolivian authorities to investigate further development and investment potentials in sustainable hydropower in Bolivia. The study assessed theoretical hydropower of all 1-kilometer (km) stream segments in the country using multisource satellite data and a hydrologic modeling approach. With the assessment covering the 2 million square kilometer (km2) region influencing Bolivia’s drainage network, the potential hydropower figures are based on theoretical yield assuming that the systems generating the power are 100 percent efficient. There are several factors to consider when determining the real-world or technical power potential of a hydropower system, and these factors can vary depending on local conditions. Since this assessment covers a large area, it was necessary to reduce these variables to the two that can be modeled consistently throughout the region, streamflow or discharge, and elevation drop or head. First, the Shuttle Radar Topography Mission high-resolution 30-meter (m) digital elevation model was used to identify stream segments with greater than 10 km2 of upstream drainage. We applied several preconditioning processes to the 30-m digital elevation model to reduce errors and improve the accuracy of stream delineation and head height estimation. A total of 316,500 1-km stream segments were identified and used in this study to assess the total theoretical hydropower potential of Bolivia. Precipitation observations from a total of 463 stations obtained from the Bolivian Servicio Nacional de Meteorología e Hidrología (Bolivian National Meteorology and Hydrology Service) and the Brazilian Agência Nacional de Águas (Brazilian National Water Agency) were used to validate six different gridded precipitation estimates for Bolivia obtained from various sources. Validation results indicated that gridded precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) reanalysis product (3B43) had the highest accuracies. The coarse-resolution (25-km) TRMM data were disaggregated to 5-km pixels using climatology information obtained from the Climate Hazards Group Infrared Precipitation with Stations dataset. About a 17-percent bias was observed in the disaggregated TRMM estimates, which was corrected using the station observations. The bias-corrected, disaggregated TRMM precipitation estimate was used to compute stream discharge using a regionalization approach. In regionalization approach, required homogeneous regions for Bolivia were derived from precipitation patterns and topographic characteristics using a k-means clustering approach. Using the discharge and head height estimates for each 1-km stream segment, we computed hydropower potential for 316,490 stream segments within Bolivia and that share borders with Bolivia. The total theoretical hydropower potential (TTHP) of these stream segments was found to be 212 gigawatts (GW). Out of this total, 77.4 GW was within protected areas where hydropower projects cannot be developed; hence, the remaining total theoretical hydropower in Bolivia (outside the protected areas) was estimated as 135 GW. Nearly 1,000 1-km stream segments, however, were within the boundaries of existing hydropower projects. The TTHP of these stream segments was nearly 1.4 GW, so the residual TTHP of the streams in Bolivia was estimated as 133 GW. Care should be exercised to understand and interpret the TTHP identified in this study because all the stream segments identified and assessed in this study cannot be harnessed to their full capacity; furthermore, factors such as required environmental flows, efficiency, economics, and feasibility need to be considered to better identify a more real-world hydropower potential. If environmental flow requirements of 20–40 percent are considered, the total theoretical power available reduces by 60–80 percent. In addition, a 0.72 efficiency factor further reduces the estimation by another 28 percent. This study provides the base theoretical hydropower potential for Bolivia, the next step is to identify optimal hydropower plant locations and factor in the principles to appraise a real-world power potential in Bolivia.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161156","collaboration":"Prepared in cooperation with the CAF – Development Bank of Latin America","usgsCitation":"Velpuri, N.M., Pervez, M.S., and Cushing, W.M., 2016, Hydropower assessment of Bolivia—A multisource satellite data and hydrologic modeling approach: U.S. Geological Survey Open-File Report 2016–1156, 65 p., https://dx.doi.org/10.3133/ofr20161156.","productDescription":"Report: x, 65 p.; Appendixes: 2-4","numberOfPages":"79","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-075626","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":331175,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1156/ofr20161156.pdf","text":"Report","size":"23.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1156"},{"id":331174,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1156/coverthb.jpg"},{"id":331176,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1156/downloads","text":"Appendixes 2–4","description":"OFR 2016–1156 Appendixes 2–4"}],"country":"Bolivia","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-62.84647,-22.03499],[-63.98684,-21.99364],[-64.37702,-22.79809],[-64.96489,-22.07586],[-66.27334,-21.83231],[-67.10667,-22.73592],[-67.82818,-22.87292],[-68.21991,-21.49435],[-68.75717,-20.37266],[-68.44223,-19.40507],[-68.96682,-18.98168],[-69.10025,-18.26013],[-69.59042,-17.58001],[-68.95964,-16.5007],[-69.38976,-15.66013],[-69.16035,-15.32397],[-69.33953,-14.9532],[-68.94889,-14.45364],[-68.92922,-13.60268],[-68.88008,-12.89973],[-68.66508,-12.5613],[-69.52968,-10.95173],[-68.78616,-11.03638],[-68.27125,-11.01452],[-68.04819,-10.71206],[-67.1738,-10.30681],[-66.64691,-9.93133],[-65.33844,-9.76199],[-65.44484,-10.51145],[-65.3219,-10.89587],[-65.40228,-11.56627],[-64.31635,-12.46198],[-63.1965,-12.62703],[-62.80306,-13.00065],[-62.12708,-13.19878],[-61.7132,-13.4892],[-61.08412,-13.47938],[-60.5033,-13.77595],[-60.4592,-14.35401],[-60.26433,-14.64598],[-60.25115,-15.07722],[-60.54297,-15.09391],[-60.15839,-16.25828],[-58.24122,-16.29957],[-58.38806,-16.87711],[-58.2808,-17.27171],[-57.73456,-17.55247],[-57.49837,-18.17419],[-57.67601,-18.96184],[-57.95,-19.4],[-57.8538,-19.97],[-58.16639,-20.1767],[-58.18347,-19.8684],[-59.11504,-19.35691],[-60.04356,-19.34275],[-61.78633,-19.63374],[-62.26596,-20.51373],[-62.29118,-21.05163],[-62.68506,-22.24903],[-62.84647,-22.03499]]]},\"properties\":{\"name\":\"Bolivia\"}}]}","contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Snakes have become successful invaders in a wide variety of ecosystems worldwide. In southern Florida, USA, the Burmese python (Python molurus bivittatus) has become established across thousands of square kilometers including all of Everglades National Park (ENP). Both experimental and correlative data have supported a relationship between Burmese python predation and declines or extirpations of mid- to large-sized mammals in ENP. In June 2013 a large python (4.32 m snout-vent length, 48.3 kg) was captured and removed from the park. Subsequent necropsy revealed a massive amount of fecal matter (79 cm in length, 6.5 kg) within the snake’s large intestine. A comparative examination of bone, teeth, and hooves extracted from the fecal contents revealed that this snake consumed three white-tailed deer (Odocoileus virginianus). This is the first report of an invasive Burmese python containing the remains of multiple white-tailed deer in its gut. Because the largest snakes native to southern Florida are not capable of consuming even mid-sized mammals, pythons likely represent a novel predatory threat to white-tailed deer in these habitats. This work highlights the potential impact of this large-bodied invasive snake and supports the need for more work on invasive predator-native prey relationships.
","language":"English","publisher":"REABIC","doi":"10.3391/bir.2016.5.4.02","usgsCitation":"Boback, S.M., Snow, R.W., Hsu, T., Peurach, S.C., Dove, C.J., and Reed, R., 2016, Supersize me: Remains of three white-tailed deer (Odocoileus virginianus) in an invasive Burmese python (Python molurus bivittatus) in Florida : BioInvasions Records, v. 5, no. 4, p. 197-203, https://doi.org/10.3391/bir.2016.5.4.02.","productDescription":"7 p.","startPage":"197","endPage":"203","ipdsId":"IP-072146","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":462027,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/bir.2016.5.4.02","text":"Publisher Index Page"},{"id":331233,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"583d502ce4b0d9329c80c58f","contributors":{"authors":[{"text":"Boback, Scott M.","contributorId":69370,"corporation":false,"usgs":false,"family":"Boback","given":"Scott","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":654318,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Snow, Ray W.","contributorId":76449,"corporation":false,"usgs":false,"family":"Snow","given":"Ray","email":"","middleInitial":"W.","affiliations":[{"id":13415,"text":"Everglades National Park","active":true,"usgs":false}],"preferred":false,"id":654319,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hsu, Teresa","contributorId":177027,"corporation":false,"usgs":false,"family":"Hsu","given":"Teresa","email":"","affiliations":[],"preferred":false,"id":654320,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peurach, Suzanne C. speurach@usgs.gov","contributorId":3064,"corporation":false,"usgs":true,"family":"Peurach","given":"Suzanne","email":"speurach@usgs.gov","middleInitial":"C.","affiliations":[],"preferred":true,"id":654321,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dove, Carla J.","contributorId":98577,"corporation":false,"usgs":true,"family":"Dove","given":"Carla","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":654322,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reed, Robert N. reedr@usgs.gov","contributorId":1686,"corporation":false,"usgs":true,"family":"Reed","given":"Robert N.","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":654323,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70178542,"text":"70178542 - 2016 - Climate drives shifts in grass reproductive phenology across the western USA","interactions":[],"lastModifiedDate":"2017-02-15T14:38:38","indexId":"70178542","displayToPublicDate":"2016-11-28T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2863,"text":"New Phytologist","active":true,"publicationSubtype":{"id":10}},"title":"Climate drives shifts in grass reproductive phenology across the western USA","docAbstract":"The biological and hydraulic performance of a portable floating fish collector (PFFC) located in the cul-de-sac of Cougar Dam and Reservoir, Oregon, was evaluated during 2015–16. The PFFC, first commissioned in May 2014, was modified during winter 2014–15 to address several deficiencies identified during operation and testing in 2014. These modifications included raising the water inflow structures to reduce the depth and volume of inflow to improve the internal hydraulic profiles, and moving the anchors so the PFFC could be positioned closer to the existing reservoir outlet, a water temperature control tower. The PFFC was positioned about 18 meters (m) upstream of the intake of the water temperature control tower and faced into the prevailing water current. Like several floating surface collectors operating in the Pacific Northwest at the time, the PFFC used pumps to draw water and fish over an inclined plane, past dewatering screens, and into a collection area. The portable and experimental nature of the PFFC required a smaller size, shallower entrance (about 2.5-m deep), and smaller inflow rate (72 cubic feet per second [ft3/s] inflow during the Low treatment, 122 ft3/s during the High treatment) than other collectors in the region.
The collection of the target species, juvenile Chinook salmon (Oncorhynchus tshawytscha), during 2015–16 was an order of magnitude larger than in 2014. Subyearling-age Chinook salmon comprised most of the catch (2,616 subyearling compared to 258 yearling) and was greatest during the spring during the High inflow treatment. Bycatch consisted predominantly of cyprinids and centrarchids. Trap mortality (fish found dead in the trap) of juvenile Chinook salmon, at 9.2 percent of the subyearlings and 5.0 percent of yearlings, was about 30 percent of the level in 2014. Fish mortality from handling the live catch was about 1 percent.
Data from fish tagged with passive integrated transponder (PIT) tags and those with acoustic+PIT tags released near the head of the reservoir indicated the catch rates of the PFFC were low. Eight of the 1,497 PIT-tagged fish and 5 of the 534 acoustic+PIT-tagged fish were collected by the PFFC. Fish collection efficiencies—the number collected by the PFFC out of the number detected at the head of the forebay (FCEFB) or in the cul-de-sac (FCECDS)—were 0.002 and 0.003 during the Low treatment and 0.008 and 0.009 during the High treatment. The low FCEs were attributed to the following factors:
The data suggest that the shallow entrance and low inflow rate reduced fish guidance near the PFFC entrance and the hydraulic characteristics resulting from the outflow plumes (and perhaps water entering the temperature control tower) attracted fish to that area. Catch of juvenile Chinook salmon likely would increase if the collector entrance were deepened, the inflow rate were increased, and measures were taken to constrain fish presence to the area upstream of the trap entrance.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161197","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Beeman, J.W., Evans, S.D., Haner, P.V., Hansel, H.C., Hansen, A.C., Hansen, G.S., Hatton, T.W., Kofoot, E.E., and Sprando, J.M., 2016, Evaluation of the biological and hydraulic performance of the portable floating fish collector at Cougar Reservoir and Dam, Oregon, September 2015–January 2016: U.S. Geological Survey Open-File Report 2016–1197, 98 p., https://doi.org/10.3133/ofr20161197.","productDescription":"x, 98 p.","numberOfPages":"112","onlineOnly":"Y","ipdsId":"IP-078812","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":331253,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1197/ofr20161197.pdf","text":"Report","size":"9.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1197"},{"id":331252,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1197/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Cougar Reservoir and Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.2507095336914,\n 44.065386786862234\n ],\n [\n -122.2507095336914,\n 44.13023159235851\n ],\n [\n -122.20401763916016,\n 44.13023159235851\n ],\n [\n -122.20401763916016,\n 44.065386786862234\n ],\n [\n -122.2507095336914,\n 44.065386786862234\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
http://wfrc.usgs.gov/
Drought is a global issue that is exacerbated by climate change and increasing anthropogenic water demands. The recent occurrence of drought in California provides an important opportunity to examine drought response across ecosystem classes (forests, shrublands, grasslands, and wetlands), which is essential to understand how climate influences ecosystem structure and function. We quantified ecosystem resistance to drought by comparing changes in satellite-derived estimates of water-use efficiency (WUE = net primary productivity [NPP]/evapotranspiration [ET]) under normal (i.e., baseline) and drought conditions (ΔWUE = WUE2014 − baseline WUE). With this method, areas with increasing WUE under drought conditions are considered more resilient than systems with declining WUE. Baseline WUE varied across California (0.08 to 3.85 g C/mm H2O) and WUE generally increased under severe drought conditions in 2014. Strong correlations between ΔWUE, precipitation, and leaf area index (LAI) indicate that ecosystems with a lower average LAI (i.e., grasslands) also had greater C-uptake rates when water was limiting and higher rates of carbon-uptake efficiency (CUE = NPP/LAI) under drought conditions. We also found that systems with a baseline WUE ≤ 0.4 exhibited a decline in WUE under drought conditions, suggesting that a baseline WUE ≤ 0.4 might be indicative of low drought resistance. Drought severity, precipitation, and WUE were identified as important drivers of shifts in ecosystem classes over the study period. These findings have important implications for understanding climate change effects on primary productivity and C sequestration across ecosystems and how this may influence ecosystem resistance in the future.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1561","usgsCitation":"Malone, S., Tulbure, M., Perez-Luque, A.J., Assal, T.J., Bremer, L., Drucker, D., Hillis, V., Varela, S., and Goulden, M., 2016, Drought resistance across California ecosystems: Evaluating changes in carbon dynamics using satellite imagery: Ecosphere, v. 7, no. 11, e01561: 19 p., https://doi.org/10.1002/ecs2.1561.","productDescription":"e01561: 19 p.","ipdsId":"IP-081593","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":462029,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1561","text":"Publisher Index Page"},{"id":341220,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"7","issue":"11","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-16","publicationStatus":"PW","scienceBaseUri":"5916c9b5e4b044b359e4869a","contributors":{"authors":[{"text":"Malone, Sparkle","contributorId":191999,"corporation":false,"usgs":false,"family":"Malone","given":"Sparkle","affiliations":[],"preferred":false,"id":695015,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tulbure, Mirela","contributorId":192000,"corporation":false,"usgs":false,"family":"Tulbure","given":"Mirela","email":"","affiliations":[],"preferred":false,"id":695016,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perez-Luque, Antonio J.","contributorId":192001,"corporation":false,"usgs":false,"family":"Perez-Luque","given":"Antonio","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":695023,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Assal, Timothy J. 0000-0001-6342-2954 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Vicken","contributorId":192004,"corporation":false,"usgs":false,"family":"Hillis","given":"Vicken","email":"","affiliations":[],"preferred":false,"id":695020,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Varela, Sara","contributorId":192005,"corporation":false,"usgs":false,"family":"Varela","given":"Sara","email":"","affiliations":[],"preferred":false,"id":695021,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Goulden, Michael","contributorId":192006,"corporation":false,"usgs":false,"family":"Goulden","given":"Michael","email":"","affiliations":[],"preferred":false,"id":695022,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70134475,"text":"70134475 - 2016 - Critical elements in Carlin, epithermal, and orogenic gold deposits","interactions":[],"lastModifiedDate":"2017-06-05T15:25:23","indexId":"70134475","displayToPublicDate":"2016-11-24T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Critical elements in Carlin, epithermal, and orogenic gold deposits","docAbstract":"Carlin, epithermal, and orogenic gold deposits, today mined almost exclusively for their gold content, have similar suites of anomalous trace elements that reflect similar low-salinity ore fluids and thermal conditions of metal transport and deposition. Many of these trace elements are commonly referred to as critical or near-critical elements or metals and have been locally recovered, although typically in small amounts, by historic mining activities. These elements include As, Bi, Hg, In, Sb, Se, Te, Tl, and W. Most of these elements are now solely recovered as by-products from the milling of large-tonnage, base metal-rich ore deposits, such as porphyry and volcanogenic massive sulfide deposits.
A combination of dominance of the world market by a single country for a single commodity and a growing demand for many of the critical to near-critical elements could lead to future recovery of such elements from select epithermal, orogenic, or Carlin-type gold deposits. Antimony continues to be recovered from some orogenic gold deposits and tellurium could potentially be a primary commodity from some such deposits. Tellurium and indium in sphalerite-rich ores have been recovered in the past and could be future commodities recovered from epithermal ores. Carlin-type gold deposits in Nevada are enriched in and may be a future source for As, Hg, Sb, and/or Tl. Some of the Devonian carbonaceous host rocks in the Carlin districts are sufficiently enriched in many trace elements, including Hg, Se, and V, such that they also could become resources. Thallium may be locally enriched to economic levels in Carlin-type deposits and it has been produced from Carlin-like deposits elsewhere in the world (e.g., Alsar, southern Macedonia; Lanmuchang, Guizhou province, China). Mercury continues to be recovered from shallow-level epithermal deposits, as well as a by-product of many Carlin-type deposits where refractory ore is roasted to oxidize carbon and pyrite, and mercury is then captured in air pollution control devices.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Rare Earth and Critical Elements in Ore Deposits","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Society of Economic Geologists","publisherLocation":"Littleton, CO","isbn":"978-1-62949-092-2","usgsCitation":"Goldfarb, R.J., Hofstra, A.H., and Simmons, S.F., 2016, Critical elements in Carlin, epithermal, and orogenic gold deposits, chap. of Rare Earth and Critical Elements in Ore Deposits, v. 18, p. 217-244.","productDescription":"28 p.","startPage":"217","endPage":"244","ipdsId":"IP-055437","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":342123,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59366da8e4b0f6c2d0d7d624","contributors":{"authors":[{"text":"Goldfarb, Richard J. goldfarb@usgs.gov","contributorId":1205,"corporation":false,"usgs":true,"family":"Goldfarb","given":"Richard","email":"goldfarb@usgs.gov","middleInitial":"J.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":525970,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hofstra, Albert H. 0000-0002-2450-1593 ahofstra@usgs.gov","orcid":"https://orcid.org/0000-0002-2450-1593","contributorId":1302,"corporation":false,"usgs":true,"family":"Hofstra","given":"Albert","email":"ahofstra@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":525971,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Simmons, Stuart F.","contributorId":127612,"corporation":false,"usgs":false,"family":"Simmons","given":"Stuart","email":"","middleInitial":"F.","affiliations":[{"id":7079,"text":"Energy and Geoscience Institute, University of Utah","active":true,"usgs":false}],"preferred":false,"id":697144,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178003,"text":"ofr20161178 - 2016 - Facilitating the inclusion of nonmarket values in Bureau of Land Management planning and project assessments—Final report","interactions":[],"lastModifiedDate":"2016-11-23T11:24:07","indexId":"ofr20161178","displayToPublicDate":"2016-11-23T11:40:00","publicationYear":"2016","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":"2016-1178","title":"Facilitating the inclusion of nonmarket values in Bureau of Land Management planning and project assessments—Final report","docAbstract":"This report summarizes the results of a series of field-based case studies conducted by the U.S. Geological Survey (USGS) to (1) evaluate the use of nonmarket values in Bureau of Land Management (BLM) planning and project assessments, (2) update existing technical resources for measuring those values, and (3) provide guidance to field staff on the use of nonmarket values. Four BLM pilot sites participated in this effort: Canyons of the Ancients National Monument in Colorado, Red Cliffs and Beaver Dam Wash National Conservation Areas in Utah, BLM’s Taos Field Office in New Mexico, and BLM's Tuscarora Field Office in Nevada. The focus of the case studies was on practical applications of nonmarket valuation. USGS worked directly with BLM field staff at the pilot sites to demonstrate the process of considering nonmarket values in BLM decisionmaking and document the questions, challenges, and opportunities that arise when tying economic language to projects.
As part of this effort, a Web-based toolkit, available at https://my.usgs.gov/benefit-transfer/, was updated and expanded to help facilitate benefit transfers (that is, the use of existing economic data to quantify nonmarket values) and qualitative discussions of nonmarket values. A total of 53 new or overlooked nonmarket valuation studies comprising 494 nonmarket value estimates for various recreational activities and the preservation of threatened, endangered, and rare species were added to existing databases within this Benefit Transfer Toolkit. In addition, four meta-regression functions focused on hunting, wildlife viewing, fishing, and trail use recreation were developed and added to the Benefit Transfer Toolkit.
Results of this effort demonstrate that there are two main roles for nonmarket valuation in BLM planning. The first is to improve the decisionmaking process by contributing to a more comprehensive comparison of economic benefits and cost when evaluating resource tradeoffs for National Environmental Policy Act analyses. The second is to use economic language and information on economic values, either qualitative or quantitative, to improve the ability to communicate the economic significance of the resources provided by BLM-managed lands.
Findings also indicate that the use of existing economic data to quantify nonmarket values (that is, benefit transfer) poses unique challenges because of the scarcity of both resource data and existing valuation studies focused on resources and sites managed by BLM. This highlights the need for improvements in the collection of resource data at BLM sites, especially visitor use data, as well as an opportunity for BLM’s Socioeconomics Program to strategically identify priority areas, in terms of both resources and geographic locations, where primary valuation studies could be conducted and the results used for future benefit transfers. Finally, whereas qualitative discussions of nonmarket values do not facilitate the comparison of monetized values, they can provide a manageable next step forward in providing more comprehensive information on nonmarket values for BLM plans and project assessments.
Center Director, USGS Fort Collins Science Center
2150 Centre Ave., Bldg. C
Box 25046, MS-939
Fort Collins, CO 80526-8118
The larvae of the Florida stone crab, Menippe mercenaria, migrate through a variety of habitats as they develop and, therefore, experience a broad range of environmental conditions through ontogeny. Environmental variability experienced by the larvae may result in distinct elemental signatures within the exoskeletons, which could provide a tool for tracking the environmental history of larval stone crab populations. A method was developed to examine trace-element ratios, specifically magnesium-to-calcium (Mg/Ca) and strontium-to-calcium (Sr/Ca) ratios, in the exoskeletons of M. mercenaria larvae. Two developmental stages of stone crab larvae were analyzed—stage III and stage V. Specimens were reared in a laboratory environment under stable conditions to quantify the average ratios of Mg/Ca and Sr/Ca of larval stone crab exoskeletons and to determine if the ratios differed through ontogeny. The elemental compositions (Ca, Mg, and Sr) in samples of stage III larvae (n = 50 per sample) from 11 different broods (mean Sr/Ca = 5.916 ± 0.161 millimole per mole [mmol mol−1]; mean Mg/Ca = 218.275 ± 59.957 mmol mol−1) and stage V larvae (n = 10 per sample) from 12 different broods (mean Sr/Ca = 6.110 ± 0.300 mmol mol−1; mean Mg/Ca = 267.081 ± 67.211 mmol mol–1) were measured using inductively coupled plasma optical emission spectrometry (ICP–OES). The ratio of Sr/Ca significantly increased from stage III to stage V larvae, suggesting an ontogenic shift in Sr/Ca ratios between larval stages. The ratio of Mg/Ca did not change significantly between larval stages, but variability among broods was high. The method used to examine the trace-element ratios provided robust, highly reproducible estimates of Sr/Ca and Mg/Ca ratios in the larvae of M. mercenaria, demonstrating that ICP–OES can be used to determine the trace-element composition of chitinous organisms like the Florida stone crab.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161148","usgsCitation":"Gravinese, P.M., Flannery, J.A., and Toth, L.T., 2016, A methodology for quantifying trace elements in the exoskeletons of the Florida stone crab (Menippe mercenaria) larvae using inductively coupled plasma optical emission spectrometry (ICP–OES): U.S. Geological Survey Open-File Report 2016–1148, 12 p., https://doi.org/10.3133/ofr20161148.","productDescription":"vi, 12 p.","numberOfPages":"19","onlineOnly":"Y","ipdsId":"IP-074067","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":331181,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1148/coverthb.jpg"},{"id":331182,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1148/ofr20161148.pdf","text":"Report","size":"2.65 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1148"}],"contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
http://coastal.er.usgs.gov/
The hydrogeology and hydrologic characteristics of the Ozark Plateaus aquifer system were characterized as part of ongoing U.S. Geological Survey efforts to assess groundwater availability across the Nation. The need for such a study in the Ozark Plateaus physiographic province (Ozark Plateaus) is highlighted by increasing demand on groundwater resources by the 5.3 million people of the Ozark Plateaus, water-level declines in some areas, and potential impacts of climate change on groundwater availability. The subject study integrates knowledge gained through local investigation within a regional perspective to develop a regional conceptual model of groundwater flow in the Ozark Plateaus aquifer system (Ozark system), a key phase of groundwater availability assessment. The Ozark system extends across much of southern Missouri and northwestern and north-central Arkansas and smaller areas of southeastern Kansas and northeastern Oklahoma. The region is one of the major karst landscapes in the United States, and karst aquifers are predominant in the Ozark system. Groundwater flow is ultimately controlled by aquifer and confining unit lithologies and stratigraphic relations, geologic structure, karst development, and the character of surficial lithologies and regolith mantle. The regolith mantle is a defining element of Ozark Plateaus karst, affecting recharge, karst development, and vulnerability to surface-derived contaminants. Karst development is more advanced—as evidenced by larger springs, hydraulic characteristics, and higher well yields—in the Salem Plateau and in the northern part of the Springfield Plateau (generally north of the Arkansas-Missouri border) as compared with the southern part of the Springfield Plateau in Arkansas, largely due to thinner, less extensive regolith and purer carbonate lithology.
Precipitation is the ultimate source of all water to the Ozark system, and the hydrologic budget for the Ozark system includes inputs from recharge, losing-stream sections, and groundwater inflows and losses of water to gaining-stream sections, groundwater withdrawals, and surface-water and groundwater outflows to neighboring systems. Groundwater recharge, estimated by a soil-water-balance model, represents about 24 percent, or 11 inches, of 43.9 inches annual precipitation. Recharge is spatially variable, being greater in the northern Springfield Plateau and Salem Plateau than in the southern Springfield Plateau (generally south of the Arkansas border) because of differences in regolith mantle extent and thickness and carbonate lithology and hydraulic properties. Increased precipitation and decreased agricultural land use during the period 1951 through 2011 increased recharge by approximately 5 percent. Although all Ozark streams have losing, neutral, and gaining sections, they are dominantly gaining and are a net sink for groundwater with nearly 90 percent of groundwater recharge returned to springs and streams. Groundwater pumping is a small but important loss of water in the Ozark system hydrologic budget; water-level declines and local cones of depression have been observed around pumping centers and strong concerns exist over potential effects on stream and spring flow.
Data indicate that societal needs for freshwater resources in the Ozark Plateaus will continue to increase and will do so in the context of changing climate and hydrology. Groundwater will continue to be an important part of supporting these societal needs and also local ecosystems. The unique character and hydrogeologic variability across the Ozark system will control how the system responds to future stress. Groundwater of the Ozark system in the northern study area is more dynamic, has greater storage and larger flux, and has greater potential for further development than in the part of the study area south of the Arkansas-Missouri border. Further south in Arkansas, a line exists, roughly defined as 5 miles south of the Springfield Plateau-Boston Mountains boundary, beyond which further extensive municipal or commercial development appears unlikely under current economic and resource-need conditions. A small part of the Ozark system groundwater budget is currently drafted for use, leaving an apparently large component available for further development and use—particularly in the northern Springfield Plateau and Salem Plateau; however, the effects of increased pumping on groundwater’s role in maintaining ecosystems and ecosystem services are not quantitatively well understood, and the close relation between groundwater and surface water highlights the importance of further quantitative assessment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165137","collaboration":"Prepared in cooperation with the Groundwater Resources Program","usgsCitation":"Hays, P.D., Knierim, K.J., Breaker, Brian, Westerman, D.A., and Clark, B.R., 2016, Hydrogeology and hydrologic conditions of the Ozark Plateaus aquifer system: U.S. Geological Survey Scientific Investigations Report 2016–5137, 61 p., https://dx.doi.org/10.3133/sir20165137. \n\n","productDescription":"Report: vii, 61 p.; Appendixes: 1-2","numberOfPages":"73","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-071467","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":331147,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5137/coverthb.jpg"},{"id":331148,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5137/sir20165137.pdf","description":"SIR 2016–5137"},{"id":331149,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5137/downloads","text":"Appendix 1 & 2","description":"SIR 2016–5137 Appendix 1 & 2"}],"country":"United States","state":"Arkansas, Kansas, Missouri, Oklahoma","otherGeospatial":"Ozark Plateaus Aquifer System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.1263427734375,\n 38.81831117374662\n ],\n [\n -90.3076171875,\n 38.89103282648846\n ],\n [\n -90.4559326171875,\n 38.8225909761771\n ],\n [\n -90.692138671875,\n 38.70694605159386\n ],\n [\n -90.8734130859375,\n 38.556757147352215\n ],\n [\n -91.0546875,\n 38.59970036588819\n ],\n [\n -91.2689208984375,\n 38.66835610151506\n ],\n [\n -91.3897705078125,\n 38.71551876930462\n ],\n [\n -91.571044921875,\n 38.68122173079789\n ],\n [\n -91.7083740234375,\n 38.70265930723801\n ],\n [\n -91.9171142578125,\n 38.65119833229951\n ],\n [\n -92.07092285156249,\n 38.57823196583313\n ],\n [\n -92.197265625,\n 38.586820096127674\n ],\n [\n -92.3345947265625,\n 38.6897975322717\n ],\n [\n -92.4884033203125,\n 38.90813299596705\n ],\n [\n -92.57080078125,\n 38.96368010198575\n ],\n [\n -92.74108886718749,\n 38.98503278695909\n ],\n [\n -92.87841796875,\n 38.997841307500714\n ],\n [\n -92.92236328125,\n 39.091699613104595\n ],\n [\n -92.9058837890625,\n 39.17691709496078\n ],\n [\n -92.8619384765625,\n 39.2407625100131\n ],\n [\n -92.9608154296875,\n 39.317300373271024\n ],\n [\n -93.0926513671875,\n 39.38101803294523\n ],\n [\n -93.2025146484375,\n 39.40224434029275\n ],\n [\n -93.350830078125,\n 39.30454987014581\n ],\n [\n -93.482666015625,\n 39.287545585410435\n ],\n [\n -93.70788574218749,\n 39.223742741391305\n ],\n [\n -93.98803710937499,\n 39.15136267949029\n ],\n [\n -95.2349853515625,\n 38.25112269630296\n ],\n [\n -95.2789306640625,\n 36.45221769643571\n ],\n [\n -95.0152587890625,\n 35.30840140169162\n ],\n [\n -94.19128417968749,\n 35.35321610123823\n ],\n [\n -93.834228515625,\n 35.47409160773029\n ],\n [\n -93.482666015625,\n 35.40696093270201\n ],\n [\n -93.16955566406249,\n 35.25459097465022\n ],\n [\n -93.065185546875,\n 35.15584570226544\n ],\n [\n -92.6641845703125,\n 35.08845057036537\n ],\n [\n -92.55432128906249,\n 34.95349314197422\n ],\n [\n -92.427978515625,\n 34.836349990763864\n ],\n [\n -92.1148681640625,\n 34.8183131456094\n ],\n [\n -90.3515625,\n 35.97800618085566\n ],\n [\n -89.35729980468749,\n 37.01132594307015\n ],\n [\n -89.4561767578125,\n 37.25656608611523\n ],\n [\n -89.4287109375,\n 37.37015718405753\n ],\n [\n -89.46716308593749,\n 37.45741810262938\n ],\n [\n -89.5166015625,\n 37.58811876638322\n ],\n [\n -89.56054687499999,\n 37.71859032558816\n ],\n [\n -89.70886230468749,\n 37.82280243352756\n ],\n [\n -89.8187255859375,\n 37.900865092570065\n ],\n [\n -89.9560546875,\n 37.97451499202459\n ],\n [\n -90.164794921875,\n 38.07836562996712\n ],\n [\n -90.2801513671875,\n 38.14751758025121\n ],\n [\n -90.3680419921875,\n 38.268375880204744\n ],\n [\n -90.32409667968749,\n 38.40194908237822\n ],\n [\n -90.252685546875,\n 38.53097889440024\n ],\n [\n -90.2032470703125,\n 38.62974534092597\n ],\n [\n -90.19775390625,\n 38.70694605159386\n ],\n [\n -90.1263427734375,\n 38.81831117374662\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
401 Hardin Road
Little Rock, AR 72211
There are many types of changes occurring over the Earth's landscapes that can be detected and monitored using Landsat data. Here we focus on monitoring “within-state,” gradual changes in vegetation in contrast with traditional monitoring of “abrupt” land-cover conversions. Gradual changes result from a variety of processes, such as vegetation growth and succession, damage from insects and disease, responses to shifts in climate, and other factors. Despite the prevalence of gradual changes across the landscape, they are largely ignored by the remote sensing community. Gradual changes are best characterized and monitored using time-series analysis, and with the successful launch of Landsat 8 we now have appreciable data continuity that extends the Landsat legacy across the previous 43 years. In this study, we conducted three related analyses: (1) comparison of spectral values acquired by Landsats 7 and 8, separated by eight days, to ensure compatibility for time-series evaluation; (2) tracking of multitemporal signatures for different change processes across Landsat 5, 7, and 8 sensors using anniversary-date imagery; and (3) tracking the same type of processes using all available acquisitions. In this investigation, we found that data representing natural vegetation from Landsats 5, 7, and 8 were comparable and did not indicate a need for major modification prior to use for long-term monitoring. Analyses using anniversary-date imagery can be very effective for assessing long term patterns and trends occurring across the landscape, and are especially good for providing insights regarding trends related to long-term and continuous trends of growth or decline. We found that use of all available data provided a much more comprehensive level of understanding of the trends occurring, providing information about rate, duration, and intra- and inter-annual variability that could not be readily gleaned from the anniversary date analyses. We observed that using all available clear Landsat 5–8 observations with the new Continuous Change Detection and Classification (CCDC) algorithm was very effective for illuminating vegetation trends. There are a number of potential challenges for assessing gradual changes, including atmospheric impacts, algorithm development and visualization of the changes. One of the biggest challenges for studying gradual change will be the lack of appropriate data for validating results and products.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2016.02.060","usgsCitation":"Vogelmann, J., Gallant, A.L., Shi, H., and Zhu, Z., 2016, Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data: Remote Sensing of Environment, v. 185, p. 258-270, https://doi.org/10.1016/j.rse.2016.02.060.","productDescription":"13 p.","startPage":"258","endPage":"270","ipdsId":"IP-066052","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470406,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2016.02.060","text":"Publisher Index Page"},{"id":341856,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"185","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"592e84b8e4b092b266f10d2c","contributors":{"authors":[{"text":"Vogelmann, James 0000-0002-0804-5823 vogel@usgs.gov","orcid":"https://orcid.org/0000-0002-0804-5823","contributorId":192352,"corporation":false,"usgs":true,"family":"Vogelmann","given":"James","email":"vogel@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":696377,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696378,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","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":696379,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhu, Zhe 0000-0001-8283-6407 zhezhu@usgs.gov","orcid":"https://orcid.org/0000-0001-8283-6407","contributorId":168792,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhe","email":"zhezhu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696380,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70178529,"text":"70178529 - 2016 - Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative","interactions":[],"lastModifiedDate":"2017-01-17T19:03:06","indexId":"70178529","displayToPublicDate":"2016-11-23T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative","docAbstract":"The U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) initiative is a\nnew end-to-end capability to continuously track and characterize changes in land cover, use, and condition\nto better support research and applications relevant to resource management and environmental\nchange. Among the LCMAP product suite are annual land cover maps that will be available to the public.\nThis paper describes an approach to optimize the selection of training and auxiliary data for deriving the\nthematic land cover maps based on all available clear observations from Landsats 4–8. Training data were\nselected from map products of the U.S. Geological Survey’s Land Cover Trends project. The Random Forest\nclassifier was applied for different classification scenarios based on the Continuous Change Detection and\nClassification (CCDC) algorithm. We found that extracting training data proportionally to the occurrence\nof land cover classes was superior to an equal distribution of training data per class, and suggest using a\ntotal of 20,000 training pixels to classify an area about the size of a Landsat scene. The problem of unbalanced\ntraining data was alleviated by extracting a minimum of 600 training pixels and a maximum of\n8000 training pixels per class. We additionally explored removing outliers contained within the training\ndata based on their spectral and spatial criteria, but observed no significant improvement in classification\nresults. We also tested the importance of different types of auxiliary data that were available for the conterminous\nUnited States, including: (a) five variables used by the National Land Cover Database, (b) three\nvariables from the cloud screening ‘‘Function of mask” (Fmask) statistics, and (c) two variables from the\nchange detection results of CCDC. We found that auxiliary variables such as a Digital Elevation Model and\nits derivatives (aspect, position index, and slope), potential wetland index, water probability, snow probability,\nand cloud probability improved the accuracy of land cover classification. Compared to the original\nstrategy of the CCDC algorithm (500 pixels per class), the use of the optimal strategy improved the classification\naccuracies substantially (15-percentage point increase in overall accuracy and 4-percentage\npoint increase in minimum accuracy).","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.isprsjprs.2016.11.004","usgsCitation":"Zhu, Z., Gallant, A.L., Woodcock, C., Pengra, B., Olofsson, P., Loveland, T., Jin, S., Dahal, D., Yang, L., and Auch, R.F., 2016, Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative: ISPRS Journal of Photogrammetry and Remote Sensing, v. 122, p. 206-221, https://doi.org/10.1016/j.isprsjprs.2016.11.004.","productDescription":"16 p.","startPage":"206","endPage":"221","numberOfPages":"16","ipdsId":"IP-080672","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470405,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.isprsjprs.2016.11.004","text":"Publisher Index Page"},{"id":331219,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"122","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5836b8dde4b0d9329c801c53","contributors":{"authors":[{"text":"Zhu, Zhe 0000-0001-8283-6407 zhezhu@usgs.gov","orcid":"https://orcid.org/0000-0001-8283-6407","contributorId":168792,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhe","email":"zhezhu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":654293,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":654287,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woodcock, Curtis","contributorId":166666,"corporation":false,"usgs":false,"family":"Woodcock","given":"Curtis","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":654502,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pengra, Bruce 0000-0003-2497-8284 bpengra@usgs.gov","orcid":"https://orcid.org/0000-0003-2497-8284","contributorId":5132,"corporation":false,"usgs":true,"family":"Pengra","given":"Bruce","email":"bpengra@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":654291,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olofsson, Pontus","contributorId":131007,"corporation":false,"usgs":false,"family":"Olofsson","given":"Pontus","email":"","affiliations":[{"id":7208,"text":"Department of Earth and Environment, Boston University","active":true,"usgs":false}],"preferred":false,"id":654290,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Loveland, Thomas R. 0000-0003-3114-6646","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":121503,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas R.","affiliations":[],"preferred":false,"id":654289,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jin, Suming 0000-0001-9919-8077 sjin@usgs.gov","orcid":"https://orcid.org/0000-0001-9919-8077","contributorId":4397,"corporation":false,"usgs":true,"family":"Jin","given":"Suming","email":"sjin@usgs.gov","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":654288,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dahal, Devendra 0000-0001-9594-1249 ddahal@usgs.gov","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":5622,"corporation":false,"usgs":true,"family":"Dahal","given":"Devendra","email":"ddahal@usgs.gov","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":654286,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Yang, Limin 0000-0002-2843-6944 lyang@usgs.gov","orcid":"https://orcid.org/0000-0002-2843-6944","contributorId":4305,"corporation":false,"usgs":true,"family":"Yang","given":"Limin","email":"lyang@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":654292,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Auch, Roger F. 0000-0002-5382-5044 auch@usgs.gov","orcid":"https://orcid.org/0000-0002-5382-5044","contributorId":667,"corporation":false,"usgs":true,"family":"Auch","given":"Roger","email":"auch@usgs.gov","middleInitial":"F.","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":654285,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70176239,"text":"fs20163068 - 2016 - Water resources of West Baton Rouge Parish, Louisiana","interactions":[],"lastModifiedDate":"2016-11-23T11:53:40","indexId":"fs20163068","displayToPublicDate":"2016-11-23T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-3068","title":"Water resources of West Baton Rouge Parish, Louisiana","docAbstract":"Information concerning the availability, use, and quality of water in West Baton Rouge Parish, Louisiana, is critical for proper water-resource management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. Information on the availability, past and current use, use trends, and water quality from groundwater and surface-water sources in the parish is presented. Previously published reports and data stored in the U.S. Geological Survey’s National Water Information System (http://waterdata.usgs.gov/nwis) are the primary sources of the information presented here.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163068","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"White, V.E., and Prakken, L.B., 2016, Water resources of West Baton Rouge Parish, Louisiana: U.S. Geological Survey Fact Sheet 2016–3068, 6 p., https://dx.doi.org/10.3133/fs20163068.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-073099","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":330988,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3068/coverthb.jpg"},{"id":330989,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3068/fs20163068.pdf","text":"Fact Sheet","size":"1.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016–3068"}],"country":"United States","state":"Louisiana","county":"West Baton Rouge Parish","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-91.3051,30.6529],[-91.2993,30.6516],[-91.2977,30.6493],[-91.2972,30.6401],[-91.2999,30.631],[-91.3057,30.6182],[-91.3132,30.6018],[-91.3164,30.5903],[-91.3159,30.5816],[-91.3117,30.5752],[-91.3085,30.5739],[-91.2947,30.5711],[-91.2666,30.571],[-91.2502,30.5632],[-91.2459,30.5559],[-91.2438,30.55],[-91.2444,30.5454],[-91.246,30.5395],[-91.2588,30.5294],[-91.2811,30.5189],[-91.2848,30.5158],[-91.2843,30.5098],[-91.28,30.5052],[-91.2684,30.5047],[-91.2503,30.5097],[-91.2094,30.5229],[-91.201,30.5178],[-91.1973,30.5073],[-91.196,30.4396],[-91.1997,30.42],[-91.2152,30.3939],[-91.2338,30.3757],[-91.2412,30.362],[-91.2418,30.3579],[-91.236,30.3446],[-91.2307,30.3414],[-91.2228,30.3409],[-91.2042,30.3454],[-91.1873,30.3468],[-91.1619,30.3421],[-91.1508,30.3375],[-91.145,30.3315],[-91.1419,30.3237],[-91.3144,30.3246],[-91.3202,30.3443],[-91.3371,30.3526],[-91.3714,30.3874],[-91.3947,30.3956],[-91.3947,30.4094],[-91.4127,30.4322],[-91.4143,30.4318],[-91.4524,30.4743],[-91.4535,30.4753],[-91.4604,30.4707],[-91.4853,30.4972],[-91.4815,30.4972],[-91.4821,30.5114],[-91.4147,30.5118],[-91.4152,30.5191],[-91.4147,30.5255],[-91.4147,30.5406],[-91.4078,30.5406],[-91.4056,30.5557],[-91.4009,30.5621],[-91.3993,30.569],[-91.3977,30.569],[-91.3945,30.569],[-91.3648,30.5689],[-91.3653,30.579],[-91.3653,30.5845],[-91.3653,30.5877],[-91.3631,30.59],[-91.3498,30.6041],[-91.3355,30.616],[-91.3201,30.6329],[-91.3174,30.637],[-91.312,30.6484],[-91.3338,30.6539],[-91.3312,30.6585],[-91.3051,30.6529]]]},\"properties\":{\"name\":\"West Baton Rouge\",\"state\":\"LA\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120,
Baton Rouge, LA 70816
A data set was acquired on a shallow mudflat in south San Francisco Bay that featured simultaneous, co-located optical and acoustic sensors for subsequent estimation of suspended sediment concentrations (SSC). The optical turbidity sensor output was converted to SSC via an empirical relation derived at a nearby site using bottle sample estimates of SSC. The acoustic data was obtained using an acoustic Doppler velocimeter. Backscatter and noise were combined to develop another empirical relation between the optical estimates of SSC and the relative backscatter from the acoustic velocimeter. The optical and acoustic approaches both reproduced similar general trends in the data and have merit. Some seasonal variation in the dataset was evident, with the two methods differing by greater or lesser amounts depending on which portion of the record was examined. It is hypothesized that this is the result of flocculation, affecting the two signals by different degrees, and that the significance or mechanism of the flocculation has some seasonal variability. In the earlier portion of the record (March), there is a clear difference that appears in the acoustic approach between ebb and flood periods, and this is not evident later in the record (May). The acoustic method has promise but it appears that characteristics of flocs that form and break apart may need to be accounted for to improve the power of the method. This may also be true of the optical method: both methods involve assuming that the sediment characteristics (size, size distribution, and shape) are constant.
","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"Proceedings of the 35th International Conference on Coastal Engineering","largerWorkSubtype":{"id":19,"text":"Conference Paper"},"conferenceTitle":"35th International Conference on Coastal Engineering","conferenceDate":"November 17-20, 2016","conferenceLocation":"Antalya, Turkey","language":"English","usgsCitation":"Öztürk, M., and Work, P.A., 2016, Acoustic Doppler velocimeter backscatter for quantification of suspended sediment concentration in South San Francisco Bay, USA, in Proceedings of the 35th International Conference on Coastal Engineering, Antalya, Turkey, November 17-20, 2016, 13 p.","productDescription":"13 p.","ipdsId":"IP-068263","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":340083,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"South San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.00292968749999,\n 37.31775185163688\n ],\n [\n -121.84936523437499,\n 37.31775185163688\n ],\n [\n -121.84936523437499,\n 38.156156969924915\n ],\n [\n -123.00292968749999,\n 38.156156969924915\n ],\n [\n -123.00292968749999,\n 37.31775185163688\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58fb1a4ce4b0c3010a8087b9","contributors":{"authors":[{"text":"Öztürk, Mehmet mozturk@usgs.gov","contributorId":168560,"corporation":false,"usgs":true,"family":"Öztürk","given":"Mehmet","email":"mozturk@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":692415,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Work, Paul A. 0000-0002-2815-8040 pwork@usgs.gov","orcid":"https://orcid.org/0000-0002-2815-8040","contributorId":168561,"corporation":false,"usgs":true,"family":"Work","given":"Paul","email":"pwork@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":626466,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70178535,"text":"70178535 - 2016 - Projected gains and losses of wildlife habitat from bioenergy-induced landscape change","interactions":[],"lastModifiedDate":"2018-12-20T13:08:40","indexId":"70178535","displayToPublicDate":"2016-11-23T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1718,"text":"GCB Bioenergy","active":true,"publicationSubtype":{"id":10}},"title":"Projected gains and losses of wildlife habitat from bioenergy-induced landscape change","docAbstract":"Domestic and foreign renewable energy targets and financial incentives have increased demand for woody biomass and bioenergy in the southeastern United States. This demand is expected to be met through purpose-grown agricultural bioenergy crops, short-rotation tree plantations, thinning and harvest of planted and natural forests, and forest harvest residues. With results from a forest economics model, spatially explicit state-and-transition simulation models, and species–habitat models, we projected change in habitat amount for 16 wildlife species caused by meeting a renewable fuel target and expected demand for wood pellets in North Carolina, USA. We projected changes over 40 years under a baseline ‘business-as-usual’ scenario without bioenergy production and five scenarios with unique feedstock portfolios. Bioenergy demand had potential to influence trends in habitat availability for some species in our study area. We found variation in impacts among species, and no scenario was the ‘best’ or ‘worst’ across all species. Our models projected that shrub-associated species would gain habitat under some scenarios because of increases in the amount of regenerating forests on the landscape, while species restricted to mature forests would lose habitat. Some forest species could also lose habitat from the conversion of forests on marginal soils to purpose-grown feedstocks. The conversion of agricultural lands on marginal soils to purpose-grown feedstocks increased habitat losses for one species with strong associations with pasture, which is being lost to urbanization in our study region. Our results indicate that landscape-scale impacts on wildlife habitat will vary among species and depend upon the bioenergy feedstock portfolio. Therefore, decisions about bioenergy and wildlife will likely involve trade-offs among wildlife species, and the choice of focal species is likely to affect the results of landscape-scale assessments. We offer general principals to consider when crafting lists of focal species for bioenergy impact assessments at the landscape scale.
Lake Michigan supports popular fisheries for Chinook Salmon Oncorhynchus tshawytscha that have been sustained by stocking since the late 1960s. Natural recruitment of Chinook Salmon in Lake Michigan has increased in the past few decades and currently contributes more than 50% of Chinook Salmon recruits. We hypothesized that selective forces differ for naturalized populations born in the wild and hatchery populations, resulting in divergent life history characteristics with implications for Chinook Salmon population production and the Lake Michigan fishery. First, we conducted a historical analysis to determine if life history characteristics changed through time as the Chinook Salmon population became increasingly naturalized. Next, we conducted a 2-year field study of naturalized and hatchery stocked Chinook Salmon spawning populations to quantify differences in fecundity, egg size, timing of spawning, and size at maturity. In general, our results did not indicate significant life history divergence between naturalized and hatchery-stocked Chinook Salmon populations in Lake Michigan. Although historical changes in adult sex ratio were correlated with the proportion of naturalized individuals, changes in weight at maturity were better explained by density-dependent factors. The field study revealed no divergence in fecundity, timing of spawning, or size at maturity, and only small differences in egg size (hatchery > naturalized). For the near future, our results suggest that the limited life history differences observed between Chinook Salmon of naturalized and hatchery origin will not lead to large differences in characteristics important to the dynamics of the population or fishery.
","language":"English","publisher":"Taylor & Francis","publisherLocation":"Abingdon, UK","doi":"10.1080/02755947.2016.1204392","usgsCitation":"Kerns, J., Rogers, M.W., Bunnell, D., Claramunt, R., and Collingsworth, P.D., 2016, Comparing life history characteristics of Lake Michigan’s naturalized and stocked Chinook Salmon: North American Journal of Fisheries Management, v. 36, no. 5, p. 1106-1118, https://doi.org/10.1080/02755947.2016.1204392.","productDescription":"13 p.","startPage":"1106","endPage":"1118","numberOfPages":"13","ipdsId":"IP-073139","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":331215,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.803466796875,\n 45.336701909968134\n ],\n [\n -85.36376953125,\n 44.824708282300236\n ],\n [\n -85.71533203125,\n 44.41808794374846\n ],\n [\n -86.143798828125,\n 44.10336537791152\n ],\n [\n -86.12182617187499,\n 43.32517767999296\n ],\n [\n -85.924072265625,\n 42.68243539838623\n ],\n [\n -85.98999023437499,\n 42.35042512243457\n ],\n [\n -86.253662109375,\n 42.09822241118974\n ],\n [\n -86.6162109375,\n 41.63186741069748\n ],\n [\n -87.022705078125,\n 41.46742831254425\n ],\n [\n -87.593994140625,\n 41.45919537950706\n ],\n [\n -87.879638671875,\n 41.72213058512578\n ],\n [\n -87.989501953125,\n 42.15525946577863\n ],\n [\n -88.143310546875,\n 42.601619944327965\n ],\n [\n -88.11035156249999,\n 43.100982876188546\n ],\n [\n -87.967529296875,\n 43.67581809328341\n ],\n [\n -87.8466796875,\n 44.19795903948531\n ],\n [\n -87.6708984375,\n 44.53567453241317\n ],\n [\n -88.0224609375,\n 44.43377984606822\n ],\n [\n -88.1982421875,\n 44.56699093657141\n ],\n [\n -88.05541992187499,\n 45.058001435398275\n ],\n [\n -87.802734375,\n 45.336701909968134\n ],\n [\n -87.275390625,\n 45.66780526567164\n ],\n [\n -87.0556640625,\n 46.057985244793024\n ],\n [\n -86.7919921875,\n 46.23305294479828\n ],\n [\n -86.099853515625,\n 46.255846818480315\n ],\n [\n -85.25390625,\n 46.31658418182218\n ],\n [\n -84.74853515625,\n 46.14178273759234\n ],\n [\n -84.495849609375,\n 45.75985868785574\n ],\n [\n -84.803466796875,\n 45.336701909968134\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-31","publicationStatus":"PW","scienceBaseUri":"5836b8dce4b0d9329c801c51","contributors":{"authors":[{"text":"Kerns, Janice A","contributorId":150933,"corporation":false,"usgs":false,"family":"Kerns","given":"Janice A","affiliations":[{"id":18145,"text":"Wisconsin Cooperative Fishery Research Unit - 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2016 - Stage-discharge relations and annual nitrogen and phosphorus load estimates for stream sites in the Elk River Basin, 2006–2008 ","interactions":[],"lastModifiedDate":"2016-11-23T11:43:49","indexId":"ds1015","displayToPublicDate":"2016-11-22T13:30:00","publicationYear":"2016","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":"1015","title":"Stage-discharge relations and annual nitrogen and phosphorus load estimates for stream sites in the Elk River Basin, 2006–2008 ","docAbstract":"The U.S. Geological Survey, in cooperation with the Tennessee Department of Environment and Conservation (TDEC), measured continuous discharge at 4 water-quality monitoring sites and developed stage-discharge ratings for 10 additional water-quality monitoring sites in the Elk River Basin during 2006 through 2008. The discharge data were collected to support stream load assessments by TDEC. Annual nitrogen and phosphorus loads were estimated for the four sites where continuous daily discharge records were collected. Reported loads for the period 2006 through 2008 are not representative of long-term mean annual conditions at the sites in this study, however, because of severe drought conditions in the Elk River Basin during this period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1015","collaboration":"Prepared in cooperation with the Tennessee Department of Environment and Conservation","usgsCitation":"Hoos, A.B., Williams, S.D., and Wolfe, W.J., 2016, Stage-discharge relations and annual nitrogen and phosphorus load estimates for stream sites in the Elk River Basin, 2006–2008: U.S. Geological Survey Data Series 1015, 9 p., https://dx.doi.org/10.3133/ds1015.","productDescription":"Report: v, 9 p.; Table 3","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-041936","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":331068,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1015/coverthb.jpg"},{"id":331069,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1015/ds1015.pdf","text":"Report","size":"753 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Data Series 1015"},{"id":331070,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/1015/ds1015_table3.xlsx","text":"Table 3 ","size":"101 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Table 3","linkHelpText":"- Stage-discharge ratings for 10 partial-record stage-discharge sites in the Elk River Basin"}],"country":"United States","state":"Alabama, Tennessee","otherGeospatial":"Elk River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.93505859374999,\n 35.94243575255426\n ],\n [\n -85.5615234375,\n 35.902399875143615\n ],\n [\n -85.53955078125,\n 35.86679541749027\n ],\n [\n -85.5230712890625,\n 35.82672127366604\n ],\n [\n -85.5230712890625,\n 35.746512259918504\n ],\n [\n -85.5340576171875,\n 35.64836915737426\n ],\n [\n -85.5670166015625,\n 35.53222622770337\n ],\n [\n -85.58349609375,\n 35.429344044107154\n ],\n [\n -85.6439208984375,\n 35.32633026307483\n ],\n [\n -85.74829101562499,\n 35.178298352001214\n ],\n [\n -85.9130859375,\n 35.03449433167976\n ],\n [\n -86.0888671875,\n 34.912962495216966\n ],\n [\n -86.253662109375,\n 34.8183131456094\n ],\n [\n -86.5008544921875,\n 34.72355492704221\n ],\n [\n -86.90185546874999,\n 34.615126683462194\n ],\n [\n -87.044677734375,\n 34.610605760914666\n ],\n [\n -87.264404296875,\n 34.56538299699511\n ],\n [\n -87.3907470703125,\n 34.615126683462194\n ],\n [\n -87.451171875,\n 34.72355492704221\n ],\n [\n -87.4896240234375,\n 34.854382885097905\n ],\n [\n -87.462158203125,\n 35.0254981588326\n ],\n [\n -87.418212890625,\n 35.19625600786368\n ],\n [\n -87.4072265625,\n 35.483038134069574\n ],\n [\n -87.2589111328125,\n 35.545635932499415\n ],\n [\n -87.1160888671875,\n 35.66622234103479\n ],\n [\n -86.934814453125,\n 35.75988604933661\n ],\n [\n -86.80847167968749,\n 35.80444911191491\n ],\n [\n -86.5283203125,\n 35.86679541749027\n ],\n [\n -85.93505859374999,\n 35.94243575255426\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Chief, Lower Mississippi-Gulf Water Science Center-Tennessee
640 Grassmere Park
Suite 100
Nashville, TN 37211
http://tn.water.usgs.gov
Results of a flood-hazard analysis conducted by the U.S. Geological Survey, in cooperation with the Argonne National Laboratory, for four headwater streams within the Argonne National Laboratory property indicate that the 1-percent and 0.2-percent annual exceedance probability floods would cause multiple roads to be overtopped. Results indicate that most of the effects on the infrastructure would be from flooding of Freund Brook. Flooding on the Northeast and Southeast Drainage Ways would be limited to overtopping of one road crossing for each of those streams. The Northwest Drainage Way would be the least affected with flooding expected to occur in open grass or forested areas.
The Argonne Site Sustainability Plan outlined the development of hydrologic and hydraulic models and the creation of flood-plain maps of the existing site conditions as a first step in addressing resiliency to possible climate change impacts as required by Executive Order 13653 “Preparing the United States for the Impacts of Climate Change.” The Hydrological Simulation Program-FORTRAN is the hydrologic model used in the study, and the Hydrologic Engineering Center‒River Analysis System (HEC–RAS) is the hydraulic model. The model results were verified by comparing simulated water-surface elevations to observed water-surface elevations measured at a network of five crest-stage gages on the four study streams. The comparison between crest-stage gage and simulated elevations resulted in an average absolute difference of 0.06 feet and a maximum difference of 0.19 feet.
In addition to the flood-hazard model development and mapping, a qualitative stream assessment was conducted to evaluate stream channel and substrate conditions in the study reaches. This information can be used to evaluate erosion potential.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165132","collaboration":"Prepared in cooperation with the Argonne National Laboratory","usgsCitation":"Soong, D.T., Murphy, E.A., Straub, T.D., and Zeeb, H.L., 2016, Flood-hazard analysis of four headwater streams draining the Argonne National Laboratory property, DuPage County, Illinois: U.S. Geological Survey Scientific Investigations Report 2016-5132, 57 p., https://dx.doi.org/10.3133/sir20165132.","productDescription":"vii, 57 p.","numberOfPages":"69","onlineOnly":"Y","ipdsId":"IP-075928","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":331075,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5132/sir20165132.pdf","text":"Report","size":"67.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5132"},{"id":331074,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5132/coverthb.jpg"}],"country":"United States","state":"Illinois","county":"DuPage County","otherGeospatial":"Sawmill Creek Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.02177429199219,\n 41.6872711837914\n ],\n [\n -88.02177429199219,\n 41.77873679916478\n ],\n [\n -87.9287338256836,\n 41.77873679916478\n ],\n [\n -87.9287338256836,\n 41.6872711837914\n ],\n [\n -88.02177429199219,\n 41.6872711837914\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Illinois-Iowa Water Science Center
U.S. Geological Survey
405 North Goodwin Avenue
Urbana, Illinois 61801
http://il.water.usgs.gov
High-resolution airborne magnetic and gravity gradiometry data provide the geophysical framework for evaluating the exploration potential of hidden iron oxide deposits in Mesoproterozoic basement rocks of southeast Missouri. The data are used to calculate mineral prospectivity for iron oxide-apatite (IOA) ± rare earth element (REE) and iron oxide-copper-gold (IOCG) deposits. Results delineate the geophysical footprints of all known iron oxide deposits and reveal several previously unrecognized prospective areas. The airborne data are also inverted to three-dimensional density and magnetic susceptibility models over four concealed deposits at Pea Ridge (IOA ± REE), Boss (IOCG), Kratz Spring (IOA), and Bourbon (IOCG). The Pea Ridge susceptibility model shows a magnetic source that is vertically extensive and traceable to a depth of greater than 2 km. A smaller density source, located within the shallow Precambrian basement, is partly coincident with the magnetic source at Pea Ridge. In contrast, the Boss models show a large (625-m-wide), vertically extensive, and coincident dense and magnetic stock with shallower adjacent lobes that extend more than 2,600 m across the shallow Precambrian paleosurface. The Kratz Spring deposit appears to be a smaller volume of iron oxides and is characterized by lower density and less magnetic rock compared to the other iron deposits. A prospective area identified south of the Kratz Spring deposit shows the largest volume of coincident dense and nonmagnetic rock in the subsurface, and is interpreted as prospective for a hematite-dominant lithology that extends from the top of the Precambrian to depths exceeding 2 km. The Bourbon deposit displays a large bowl-shaped volume of coincident high density and high-magnetic susceptibility rock, and a geometry that suggests the iron mineralization is vertically restricted to the upper parts of the Precambrian basement. In order to underpin the evaluation of the prospectivity and three-dimensional models, an extensive statistical summary of density and apparent magnetic susceptibility measurements is presented that includes data on several hundred samples taken from the deposits, altered wall rocks, and unaltered country rocks.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.2113/econgeo.111.8.1859","usgsCitation":"McCafferty, A.E., Phillips, J., and Driscoll, R.L., 2016, Magnetic and gravity gradiometry framework for Mesoproterozoic iron oxide-apatite and iron oxide-copper-gold deposits, southeast Missouri, USA: Economic Geology, v. 111, no. 8, https://doi.org/10.2113/econgeo.111.8.1859.","productDescription":"24 p.","startPage":"1882","ipdsId":"IP-069306","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":438504,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78P5XM4","text":"USGS data release","linkHelpText":"Helicopter magnetic and gravity gradiometry survey over the Pea Ridge iron mine and surrounding area, southeast Missouri, 2014"},{"id":331203,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.65869140625,\n 35.88905007936091\n ],\n [\n -92.65869140625,\n 38.788345355085625\n ],\n [\n -89.05517578125,\n 38.788345355085625\n ],\n [\n -89.05517578125,\n 35.88905007936091\n ],\n [\n -92.65869140625,\n 35.88905007936091\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"111","issue":"8","edition":"1859","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-16","publicationStatus":"PW","scienceBaseUri":"58356728e4b0070c0abfb6d2","contributors":{"authors":[{"text":"McCafferty, Anne E. 0000-0001-5574-9201 anne@usgs.gov","orcid":"https://orcid.org/0000-0001-5574-9201","contributorId":1120,"corporation":false,"usgs":true,"family":"McCafferty","given":"Anne","email":"anne@usgs.gov","middleInitial":"E.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":654215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Phillips, Jeffrey 0000-0002-6459-2821 jeff@usgs.gov","orcid":"https://orcid.org/0000-0002-6459-2821","contributorId":127453,"corporation":false,"usgs":true,"family":"Phillips","given":"Jeffrey","email":"jeff@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":654216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Driscoll, Rhonda L. 0000-0001-7725-8956 rdriscoll@usgs.gov","orcid":"https://orcid.org/0000-0001-7725-8956","contributorId":745,"corporation":false,"usgs":true,"family":"Driscoll","given":"Rhonda","email":"rdriscoll@usgs.gov","middleInitial":"L.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":654217,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}