Air is entrained in water as it is flows through the spillways of dams, which causes an increase in the concentration of total dissolved gas in the water downstream from the dams. The elevated concentrations of total dissolved gas can adversely affect fish and other freshwater aquatic life. An analysis of total-dissolved-gas and water-temperature data collected at eight monitoring stations on the lower Columbia River in Oregon and Washington in 2012 indicated the following:
\nThe synoptic mass balance approach is often used to evaluate constituent mass loading in streams affected by mine drainage. Spatial profiles of constituent mass load are used to identify sources of contamination and prioritize sites for remedial action. This paper presents a field scale study in which replicate synoptic sampling campaigns are used to quantify the aggregate uncertainty in constituent load that arises from (1) laboratory analyses of constituent and tracer concentrations, (2) field sampling error, and (3) temporal variation in concentration from diel constituent cycles and/or source variation. Consideration of these factors represents an advance in the application of the synoptic mass balance approach by placing error bars on estimates of constituent load and by allowing all sources of uncertainty to be quantified in aggregate; previous applications of the approach have provided only point estimates of constituent load and considered only a subset of the possible errors. Given estimates of aggregate uncertainty, site specific data and expert judgement may be used to qualitatively assess the contributions of individual factors to uncertainty. This assessment can be used to guide the collection of additional data to reduce uncertainty. Further, error bars provided by the replicate approach can aid the investigator in the interpretation of spatial loading profiles and the subsequent identification of constituent source areas within the watershed.
The replicate sampling approach is applied to Peru Creek, a stream receiving acidic, metal-rich effluent from the Pennsylvania Mine. Other sources of acidity and metals within the study reach include a wetland area adjacent to the mine and tributary inflow from Cinnamon Gulch. Analysis of data collected under low-flow conditions indicates that concentrations of Al, Cd, Cu, Fe, Mn, Pb, and Zn in Peru Creek exceed aquatic life standards. Constituent loading within the study reach is dominated by effluent from the Pennsylvania Mine, with over 50% of the Cd, Cu, Fe, Mn, and Zn loads attributable to a collapsed adit near the top of the study reach. These estimates of mass load may underestimate the effect of the Pennsylvania Mine as leakage from underground mine workings may contribute to metal loads that are currently attributed to the wetland area. This potential leakage confounds the evaluation of remedial options and additional research is needed to determine the magnitude and location of the leakage.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2013.02.031","usgsCitation":"Runkel, R.L., Walton-Day, K., Kimball, B.A., Verplanck, P.L., and Nimick, D.A., 2013, Estimating instream constituent loads using replicate synoptic sampling, Peru Creek, Colorado: Journal of Hydrology, v. 489, p. 26-41, https://doi.org/10.1016/j.jhydrol.2013.02.031.","productDescription":"16 p.","startPage":"26","endPage":"41","ipdsId":"IP-044174","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":272199,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Peru Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.8287239074707,\n 39.59451160220633\n ],\n [\n -105.8287239074707,\n 39.61144109709137\n ],\n [\n -105.80074310302734,\n 39.61144109709137\n ],\n [\n -105.80074310302734,\n 39.59451160220633\n ],\n [\n -105.8287239074707,\n 39.59451160220633\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"489","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd5804e4b0b290850f7d13","contributors":{"authors":[{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":476577,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walton-Day, Katherine 0000-0002-9146-6193","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":68339,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","affiliations":[],"preferred":false,"id":476579,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kimball, Briant A. bkimball@usgs.gov","contributorId":533,"corporation":false,"usgs":true,"family":"Kimball","given":"Briant","email":"bkimball@usgs.gov","middleInitial":"A.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":476576,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Verplanck, Philip L. 0000-0002-3653-6419 plv@usgs.gov","orcid":"https://orcid.org/0000-0002-3653-6419","contributorId":728,"corporation":false,"usgs":true,"family":"Verplanck","given":"Philip","email":"plv@usgs.gov","middleInitial":"L.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":476578,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nimick, David A. dnimick@usgs.gov","contributorId":421,"corporation":false,"usgs":true,"family":"Nimick","given":"David","email":"dnimick@usgs.gov","middleInitial":"A.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":573,"text":"Special Applications Science Center","active":true,"usgs":true}],"preferred":true,"id":476575,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70045947,"text":"70045947 - 2013 - Return period adjustment for runoff coefficients based on analysis in undeveloped Texas watersheds","interactions":[],"lastModifiedDate":"2013-05-14T15:09:44","indexId":"70045947","displayToPublicDate":"2013-05-14T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2362,"text":"Journal of Irrigation and Drainage Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Return period adjustment for runoff coefficients based on analysis in undeveloped Texas watersheds","docAbstract":"The rational method for peak discharge (Qp) estimation was introduced in the 1880s. The runoff coefficient (C) is a key parameter for the rational method that has an implicit meaning of rate proportionality, and the C has been declared a function of the annual return period by various researchers. Rate-based runoff coefficients as a function of the return period, C(T), were determined for 36 undeveloped watersheds in Texas using peak discharge frequency from previously published regional regression equations and rainfall intensity frequency for return periods T of 2, 5, 10, 25, 50, and 100 years. The C(T) values and return period adjustments C(T)/C(T=10 year) determined in this study are most applicable to undeveloped watersheds. The return period adjustments determined for the Texas watersheds in this study and those extracted from prior studies of non-Texas data exceed values from well-known literature such as design manuals and textbooks. Most importantly, the return period adjustments exceed values currently recognized in Texas Department of Transportation design guidance when T>10 years.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Irrigation and Drainage Engineering","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"ASCE","doi":"10.1061/(ASCE)IR.1943-4774.0000571","usgsCitation":"Dhakal, N., Fang, X., Asquith, W.H., Cleveland, T., and Thompson, D.B., 2013, Return period adjustment for runoff coefficients based on analysis in undeveloped Texas watersheds: Journal of Irrigation and Drainage Engineering, v. 139, no. 6, p. 476-482, https://doi.org/10.1061/(ASCE)IR.1943-4774.0000571.","productDescription":"7 p.","startPage":"476","endPage":"482","ipdsId":"IP-042345","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":272266,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":272265,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1061/(ASCE)IR.1943-4774.0000571"}],"country":"United States","state":"Texas","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -106.6,25.8 ], [ -106.6,36.5 ], [ -93.5,36.5 ], [ -93.5,25.8 ], [ -106.6,25.8 ] ] ] } } ] }","volume":"139","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd70e0e4b0b29085107537","contributors":{"authors":[{"text":"Dhakal, Nirajan","contributorId":93796,"corporation":false,"usgs":true,"family":"Dhakal","given":"Nirajan","email":"","affiliations":[],"preferred":false,"id":478594,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fang, Xing","contributorId":27134,"corporation":false,"usgs":true,"family":"Fang","given":"Xing","email":"","affiliations":[],"preferred":false,"id":478591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Asquith, William H. 0000-0002-7400-1861 wasquith@usgs.gov","orcid":"https://orcid.org/0000-0002-7400-1861","contributorId":1007,"corporation":false,"usgs":true,"family":"Asquith","given":"William","email":"wasquith@usgs.gov","middleInitial":"H.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478590,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cleveland, Theodore G.","contributorId":88029,"corporation":false,"usgs":true,"family":"Cleveland","given":"Theodore G.","affiliations":[],"preferred":false,"id":478593,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thompson, David B.","contributorId":79954,"corporation":false,"usgs":true,"family":"Thompson","given":"David","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":478592,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70045677,"text":"70045677 - 2013 - Evaluation of a new model of aeolian transport in the presence of vegetation","interactions":[],"lastModifiedDate":"2013-05-14T11:05:21","indexId":"70045677","displayToPublicDate":"2013-05-14T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2318,"text":"Journal of Geophysical Research F: Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of a new model of aeolian transport in the presence of vegetation","docAbstract":"Aeolian transport is an important characteristic of many arid and semiarid regions worldwide that affects dust emission and ecosystem processes. The purpose of this paper is to evaluate a recent model of aeolian transport in the presence of vegetation. This approach differs from previous models by accounting for how vegetation affects the distribution of shear velocity on the surface rather than merely calculating the average effect of vegetation on surface shear velocity or simply using empirical relationships. Vegetation, soil, and meteorological data at 65 field sites with measurements of horizontal aeolian flux were collected from the Western United States. Measured fluxes were tested against modeled values to evaluate model performance, to obtain a set of optimum model parameters, and to estimate the uncertainty in these parameters. The same field data were used to model horizontal aeolian flux using three other schemes. Our results show that the model can predict horizontal aeolian flux with an approximate relative error of 2.1 and that further empirical corrections can reduce the approximate relative error to 1.0. The level of error is within what would be expected given uncertainties in threshold shear velocity and wind speed at our sites. The model outperforms the alternative schemes both in terms of approximate relative error and the number of sites at which threshold shear velocity was exceeded. These results lend support to an understanding of the physics of aeolian transport in which (1) vegetation's impact on transport is dependent upon the distribution of vegetation rather than merely its average lateral cover and (2) vegetation impacts surface shear stress locally by depressing it in the immediate lee of plants rather than by changing the bulk surface's threshold shear velocity. Our results also suggest that threshold shear velocity is exceeded more than might be estimated by single measurements of threshold shear stress and roughness length commonly associated with vegetated surfaces, highlighting the variation of threshold shear velocity with space and time in real landscapes.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Geophysical Research F: Earth Surface","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"AGU","doi":"10.1002/jgrf.20040","usgsCitation":"Li, J., Okin, G.S., Herrick, J.E., Belnap, J., Miller, M.E., Vest, K., and Draut, A.E., 2013, Evaluation of a new model of aeolian transport in the presence of vegetation: Journal of Geophysical Research F: Earth Surface, v. 118, no. 1, p. 288-306, https://doi.org/10.1002/jgrf.20040.","productDescription":"9 p.","startPage":"288","endPage":"306","ipdsId":"IP-025701","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":473829,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jgrf.20040","text":"Publisher Index Page"},{"id":272217,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":272215,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/jgrf.20040"}],"volume":"118","issue":"1","noUsgsAuthors":false,"publicationDate":"2013-03-26","publicationStatus":"PW","scienceBaseUri":"53cd584ee4b0b290850f802d","chorus":{"doi":"10.1002/jgrf.20040","url":"http://dx.doi.org/10.1002/jgrf.20040","publisher":"Wiley-Blackwell","authors":"Li Junran, Okin Gregory S., Herrick Jeffrey E., Belnap Jayne, Miller Mark E., Vest Kimberly, Draut Amy E.","journalName":"Journal of Geophysical Research: Earth Surface","publicationDate":"3/2013","auditedOn":"3/7/2016"},"contributors":{"authors":[{"text":"Li, Junran","contributorId":23418,"corporation":false,"usgs":true,"family":"Li","given":"Junran","affiliations":[],"preferred":false,"id":478037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Okin, Gregory S.","contributorId":50025,"corporation":false,"usgs":true,"family":"Okin","given":"Gregory","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":478039,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herrick, Jeffrey E.","contributorId":26054,"corporation":false,"usgs":false,"family":"Herrick","given":"Jeffrey","email":"","middleInitial":"E.","affiliations":[{"id":12627,"text":"USDA-ARS Jornada Experimental Range, New Mexico State University, Las Cruces, NM 88003-8003, USA","active":true,"usgs":false}],"preferred":false,"id":478038,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Belnap, Jayne 0000-0001-7471-2279 jayne_belnap@usgs.gov","orcid":"https://orcid.org/0000-0001-7471-2279","contributorId":1332,"corporation":false,"usgs":true,"family":"Belnap","given":"Jayne","email":"jayne_belnap@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":478036,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miller, Mark E.","contributorId":91580,"corporation":false,"usgs":false,"family":"Miller","given":"Mark","email":"","middleInitial":"E.","affiliations":[{"id":6959,"text":"National Park Service Southeast Utah Group","active":true,"usgs":false}],"preferred":false,"id":478041,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vest, Kimberly","contributorId":83818,"corporation":false,"usgs":true,"family":"Vest","given":"Kimberly","email":"","affiliations":[],"preferred":false,"id":478040,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Draut, Amy E.","contributorId":92215,"corporation":false,"usgs":true,"family":"Draut","given":"Amy","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":478042,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70045516,"text":"70045516 - 2013 - Fat or lean: adjustment of endogenous energy stores to predictable and unpredictable changes in allostatic load","interactions":[],"lastModifiedDate":"2013-05-14T16:15:51","indexId":"70045516","displayToPublicDate":"2013-05-14T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1711,"text":"Functional Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Fat or lean: adjustment of endogenous energy stores to predictable and unpredictable changes in allostatic load","docAbstract":"1. The ability to store energy endogenously is an important ecological mechanism that allows animals to buffer predictable and unpredictable variation in allostatic load. The secretion of glucocorticoids, which reflects changes in allostatic load, is suggested to play a major role in the adjustment of endogenous stores to these varying conditions.\n2. Although crucially important, the relationship between allostatic load and energy stores remains largely unexplored. Two contrasting hypotheses describe how stores may be adjusted: animals may use low allostatic loads to increase stores to a maximum possible (‘fat and fit’), or they can attain a lean physique due to fitness advantages of a low body mass (‘lean and fit’).\n3. We compiled observational and experimental data available for a long-lived seabird to examine the relationship between glucocorticoids and stored energy at two life history stages (incubation and chick-rearing). Data were collected across multiple years and colonies in the North Pacific, thereby reflecting the wide range of environmental conditions birds' encounter in the marine environment. During experimental manipulations, allostatic load was minimized by supplementing food to free-living birds.\n4. We found that the relationship between allostatic load and energy stores was clearly curvilinear at both life history stages. Observational data suggested that energy stores remained relatively stable under low allostatic load and decreased under high loads. Experimental data showed that birds did not maximize energy stores under favourable conditions but maintained energy stores below a physiologically attainable level.\n5. Energy stores remained consistently lower during chick-rearing compared to incubation across the wide range of variations in allostatic load suggesting that stage-specific trade-offs limit the accumulation of energy during favourable environmental conditions. Secretion of glucocorticoids did not appear to mediate this shift in energy stores between the life history stages.\n6. Overall, results of this study support the ‘lean and fit’ hypothesis. We conclude that increased energy stores may not necessarily reflect better environmental conditions experienced by individuals or predict their higher fitness. A major advantage of adopting a lean physique when environmental conditions allow may be the avoidance of additional energetic costs for moving a heavy body. In breeding seabirds, this advantage may be more important during chick-rearing. In the focal species, the secretion of glucocorticoids might be involved in regulation of energy stores within a life history stage but does not appear to mediate an adaptive shift in energy stores between the incubating and chick-rearing stages of reproduction.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Functional Ecology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1111/j.1365-2435.2012.02058.x","usgsCitation":"Schultner, J., Kitaysky, A.S., Welcker, J., and Hatch, S., 2013, Fat or lean: adjustment of endogenous energy stores to predictable and unpredictable changes in allostatic load: Functional Ecology, v. 27, no. 1, p. 45-55, https://doi.org/10.1111/j.1365-2435.2012.02058.x.","productDescription":"11 p.","startPage":"45","endPage":"55","ipdsId":"IP-042338","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":499925,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://zotero.org/groups/5435545/items/GFLXV55I","text":"External Repository"},{"id":272276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":272275,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/j.1365-2435.2012.02058.x"}],"volume":"27","issue":"1","noUsgsAuthors":false,"publicationDate":"2012-10-08","publicationStatus":"PW","scienceBaseUri":"53cd590de4b0b290850f87c3","contributors":{"authors":[{"text":"Schultner, Jannik","contributorId":77028,"corporation":false,"usgs":true,"family":"Schultner","given":"Jannik","email":"","affiliations":[],"preferred":false,"id":477704,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kitaysky, Alexander S.","contributorId":13884,"corporation":false,"usgs":true,"family":"Kitaysky","given":"Alexander","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":477701,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Welcker, Jorg","contributorId":25441,"corporation":false,"usgs":true,"family":"Welcker","given":"Jorg","email":"","affiliations":[],"preferred":false,"id":477703,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hatch, Scott","contributorId":16268,"corporation":false,"usgs":true,"family":"Hatch","given":"Scott","affiliations":[],"preferred":false,"id":477702,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70045952,"text":"fs20133018 - 2013 - Integrated synoptic surveys using an autonomous underwater vehicle and manned boats","interactions":[],"lastModifiedDate":"2026-06-05T16:32:33.209631","indexId":"fs20133018","displayToPublicDate":"2013-05-14T00:00:00","publicationYear":"2013","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":"2013-3018","title":"Integrated synoptic surveys using an autonomous underwater vehicle and manned boats","docAbstract":"Traditional surface-water surveys are being combined with autonomous technology to produce integrated surveys of bathymetry, water quality, and velocity in inland lakes and reservoirs. This new technology provides valuable, high-resolution, integrated data that allow a systems-based approach to understanding common environmental problems. This fact sheet presents several example applications of integrated surveys within inland lakes and coastal Lake Michigan and Lake Erie.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20133018","usgsCitation":"Jackson, P., 2013, Integrated synoptic surveys using an autonomous underwater vehicle and manned boats: U.S. Geological Survey Fact Sheet 2013-3018, 4 p., https://doi.org/10.3133/fs20133018.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":505104,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_98479.htm","text":"Milwaukee Harbor, Wisconsin","linkFileType":{"id":5,"text":"html"}},{"id":505103,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_98478.htm","text":"Villa Angela Beach, Cuyahoga County, Ohio","linkFileType":{"id":5,"text":"html"}},{"id":505102,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_98477.htm","text":"Wonder Lake, McHenry County, Illinois","linkFileType":{"id":5,"text":"html"}},{"id":272257,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2013/3018/"},{"id":272258,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2013/3018/pdf/FS2013-3018.pdf","text":"Report","size":"9.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":272259,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs20133018.gif"}],"country":"United States","otherGeospatial":"Lake Erie, Lake Michigan","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd62b1e4b0b290850fe5ab","contributors":{"authors":[{"text":"Jackson, P. Ryan","contributorId":68571,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","middleInitial":"Ryan","affiliations":[],"preferred":false,"id":478595,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70045078,"text":"70045078 - 2013 - Estimating economic losses from earthquakes using an empirical approach","interactions":[],"lastModifiedDate":"2013-05-12T21:46:04","indexId":"70045078","displayToPublicDate":"2013-05-12T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Estimating economic losses from earthquakes using an empirical approach","docAbstract":"We extended the U.S. Geological Survey's Prompt Assessment of Global Earthquakes for Response (PAGER) empirical fatality estimation methodology proposed by Jaiswal et al. (2009) to rapidly estimate economic losses after significant earthquakes worldwide. The requisite model inputs are shaking intensity estimates made by the ShakeMap system, the spatial distribution of population available from the LandScan database, modern and historic country or sub-country population and Gross Domestic Product (GDP) data, and economic loss data from Munich Re's historical earthquakes catalog. We developed a strategy to approximately scale GDP-based economic exposure for historical and recent earthquakes in order to estimate economic losses. The process consists of using a country-specific multiplicative factor to accommodate the disparity between economic exposure and the annual per capita GDP, and it has proven successful in hindcast-ing past losses. Although loss, population, shaking estimates, and economic data used in the calibration process are uncertain, approximate ranges of losses can be estimated for the primary purpose of gauging the overall scope of the disaster and coordinating response. The proposed methodology is both indirect and approximate and is thus best suited as a rapid loss estimation model for applications like the PAGER system.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Earthquake Spectra","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"EERI","doi":"10.1193/1.4000104","usgsCitation":"Jaiswal, K., and Wald, D.J., 2013, Estimating economic losses from earthquakes using an empirical approach: Earthquake Spectra, v. 29, no. 1, p. 309-324, https://doi.org/10.1193/1.4000104.","productDescription":"16 p.","startPage":"309","endPage":"324","ipdsId":"IP-037500","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":272191,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":272190,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1193/1.4000104"}],"volume":"29","issue":"1","noUsgsAuthors":false,"publicationDate":"2013-02-01","publicationStatus":"PW","scienceBaseUri":"5190abcee4b05ebc8f7cc329","contributors":{"authors":[{"text":"Jaiswal, Kishor kjaiswal@usgs.gov","contributorId":861,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":false,"id":476745,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":476744,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70045934,"text":"70045934 - 2013 - Simulating mechanisms for dispersal, production and stranding of small forage fish in temporary wetland habitats","interactions":[],"lastModifiedDate":"2013-05-11T23:50:49","indexId":"70045934","displayToPublicDate":"2013-05-11T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Simulating mechanisms for dispersal, production and stranding of small forage fish in temporary wetland habitats","docAbstract":"Movement strategies of small forage fish (<8 cm total length) between temporary and permanent wetland habitats affect their overall population growth and biomass concentrations, i.e., availability to predators. These fish are often the key energy link between primary producers and top predators, such as wading birds, which require high concentrations of stranded fish in accessible depths. Expansion and contraction of seasonal wetlands induce a sequential alternation between rapid biomass growth and concentration, creating the conditions for local stranding of small fish as they move in response to varying water levels. To better understand how landscape topography, hydrology, and fish behavior interact to create high densities of stranded fish, we first simulated population dynamics of small fish, within a dynamic food web, with different traits for movement strategy and growth rate, across an artificial, spatially explicit, heterogeneous, two-dimensional marsh slough landscape, using hydrologic variability as the driver for movement. Model output showed that fish with the highest tendency to invade newly flooded marsh areas built up the largest populations over long time periods with stable hydrologic patterns. A higher probability to become stranded had negative effects on long-term population size, and offset the contribution of that species to stranded biomass. The model was next applied to the topography of a 10 km × 10 km area of Everglades landscape. The details of the topography were highly important in channeling fish movements and creating spatiotemporal patterns of fish movement and stranding. This output provides data that can be compared in the future with observed locations of fish biomass concentrations, or such surrogates as phosphorus ‘hotspots’ in the marsh.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecological Modelling","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2012.11.001","usgsCitation":"Yurek, S., DeAngelis, D., Trexler, J.C., Jopp, F., and Donalson, D.D., 2013, Simulating mechanisms for dispersal, production and stranding of small forage fish in temporary wetland habitats: Ecological Modelling, v. 250, p. 391-401, https://doi.org/10.1016/j.ecolmodel.2012.11.001.","productDescription":"11 p.","startPage":"391","endPage":"401","ipdsId":"IP-038780","costCenters":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"links":[{"id":272189,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":272188,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.ecolmodel.2012.11.001"}],"volume":"250","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518f5a51e4b05ebc8f7cc30a","contributors":{"authors":[{"text":"Yurek, Simeon 0000-0002-6209-7915 syurek@usgs.gov","orcid":"https://orcid.org/0000-0002-6209-7915","contributorId":103167,"corporation":false,"usgs":true,"family":"Yurek","given":"Simeon","email":"syurek@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":478555,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":88015,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald L.","affiliations":[],"preferred":false,"id":478554,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Trexler, Joel C.","contributorId":36267,"corporation":false,"usgs":false,"family":"Trexler","given":"Joel","email":"","middleInitial":"C.","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":478551,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jopp, Fred","contributorId":62336,"corporation":false,"usgs":true,"family":"Jopp","given":"Fred","email":"","affiliations":[],"preferred":false,"id":478552,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Donalson, Douglas D.","contributorId":74660,"corporation":false,"usgs":true,"family":"Donalson","given":"Douglas","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":478553,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70045932,"text":"ofr20121038 - 2013 - Tidal wetlands of the Yaquina and Alsea River estuaries, Oregon: Geographic Information Systems layer development and recommendations for National Wetlands Inventory revisions","interactions":[],"lastModifiedDate":"2016-05-04T14:44:24","indexId":"ofr20121038","displayToPublicDate":"2013-05-10T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-1038","title":"Tidal wetlands of the Yaquina and Alsea River estuaries, Oregon: Geographic Information Systems layer development and recommendations for National Wetlands Inventory revisions","docAbstract":"Geographic Information Systems (GIS) layers of current, and likely former, tidal wetlands in two Oregon estuaries were generated by enhancing the 2010 National Wetlands Inventory (NWI) data with expert local field knowledge, Light Detection and Ranging-derived elevations, and 2009 aerial orthophotographs. Data were generated for two purposes: First, to enhance the NWI by recommending revised Cowardin classifications for certain NWI wetlands within the study area; and second, to generate GIS data for the 1999 Yaquina and Alsea River Basins Estuarine Wetland Site Prioritization study. Two sets of GIS products were generated: (1) enhanced NWI shapefiles; and (2) shapefiles of prioritization sites. The enhanced NWI shapefiles contain recommended changes to the Cowardin classification (system, subsystem, class, and/or modifiers) for 286 NWI polygons in the Yaquina estuary (1,133 acres) and 83 NWI polygons in the Alsea estuary (322 acres). These enhanced NWI shapefiles also identify likely former tidal wetlands that are classified as upland in the current NWI (64 NWI polygons totaling 441 acres in the Yaquina estuary; 16 NWI polygons totaling 51 acres in the Alsea estuary). The former tidal wetlands were identified to assist strategic planning for tidal wetland restoration. Cowardin classifications for the former tidal wetlands were not provided, because their current hydrology is complex owing to dikes, tide gates, and drainage ditches. The scope of this project did not include the field evaluation that would be needed to determine whether the former tidal wetlands are currently wetlands, and if so, determine their correct Cowardin classification. The prioritization site shapefiles contain 49 prioritization sites totaling 2,177 acres in the Yaquina estuary, and 39 prioritization sites totaling 1,045 acres in the Alsea estuary. The prioritization sites include current and former (for example, diked) tidal wetlands, and provide landscape units appropriate for basin-scale wetland restoration and conservation action planning. Several new prioritization sites (not included in the 1999 prioritization) were identified in each estuary, consisting of NWI polygons formerly classified as nontidal wetland or upland. The GIS products of this project improve the accuracy and utility of the NWI data, and provide useful tools for estuarine resource management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121038","collaboration":"Prepared in cooperation with Green Point Consulting and the U.S. Environmental Protection Agency","usgsCitation":"Brophy, L.S., Reusser, D.A., and Janousek, C.N., 2013, Tidal wetlands of the Yaquina and Alsea River estuaries, Oregon: Geographic Information Systems layer development and recommendations for National Wetlands Inventory revisions: U.S. Geological Survey Open-File Report 2012-1038, vi, 60 p., https://doi.org/10.3133/ofr20121038.","productDescription":"vi, 60 p.","numberOfPages":"68","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":272177,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20121038.gif"},{"id":272323,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1038/"},{"id":272176,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1038/pdf/ofr2012-1038.pdf","text":"Report","size":"18.86 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"country":"United States","state":"Oregon","otherGeospatial":"Yaquina And Alsea Estuaries","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.16,44.16 ], [ -124.16,44.5 ], [ -123.5,44.5 ], [ -123.5,44.16 ], [ -124.16,44.16 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518e08f7e4b05ebc8f7cc2de","contributors":{"authors":[{"text":"Brophy, Laura S.","contributorId":47266,"corporation":false,"usgs":false,"family":"Brophy","given":"Laura","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":478548,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reusser, Deborah A. dreusser@usgs.gov","contributorId":2423,"corporation":false,"usgs":true,"family":"Reusser","given":"Deborah","email":"dreusser@usgs.gov","middleInitial":"A.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":547809,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Janousek, Christopher N. 0000-0003-2124-6715","orcid":"https://orcid.org/0000-0003-2124-6715","contributorId":103951,"corporation":false,"usgs":false,"family":"Janousek","given":"Christopher","email":"","middleInitial":"N.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":478549,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70045936,"text":"ofr20131084 - 2013 - Digital tabulation of stratigraphic data from oil and gas wells in Cuyama Valley and surrounding areas, central California","interactions":[],"lastModifiedDate":"2013-05-10T15:33:06","indexId":"ofr20131084","displayToPublicDate":"2013-05-10T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1084","title":"Digital tabulation of stratigraphic data from oil and gas wells in Cuyama Valley and surrounding areas, central California","docAbstract":"Stratigraphic information from 391 oil and gas exploration wells from Cuyama Valley, California, and surrounding areas are herein compiled in digital form from reports that were released originally in paper form. The Cuyama Basin is located within the southeasternmost part of the Coast Ranges and north of the western Transverse Ranges, west of the San Andreas fault. Knowledge of the location and elevation of stratigraphic tops of formations throughout the basin is a first step toward understanding depositional trends and the structural evolution of the basin through time, and helps in understanding the slip history and partitioning of slip on San Andreas and related faults.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131084","usgsCitation":"Sweetkind, D., Bova, S.C., Langenheim, V., Shumaker, L., and Scheirer, D., 2013, Digital tabulation of stratigraphic data from oil and gas wells in Cuyama Valley and surrounding areas, central California: U.S. Geological Survey Open-File Report 2013-1084, Report: vii, 44 p.; Appendix 1, https://doi.org/10.3133/ofr20131084.","productDescription":"Report: vii, 44 p.; Appendix 1","numberOfPages":"51","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":272184,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131084.gif"},{"id":272183,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1084/Appendix1.xlsx"},{"id":272181,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1084/"},{"id":272182,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1084/OF13-1084_508.pdf"}],"country":"United States","state":"California","otherGeospatial":"Cuyama Valley","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -120.5,34.58 ], [ -120.5,35.5 ], [ -119,35.5 ], [ -119,34.58 ], [ -120.5,34.58 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518e08d1e4b05ebc8f7cc2d2","contributors":{"authors":[{"text":"Sweetkind, Donald S.","contributorId":18732,"corporation":false,"usgs":true,"family":"Sweetkind","given":"Donald S.","affiliations":[],"preferred":false,"id":478559,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bova, Shiera C.","contributorId":45607,"corporation":false,"usgs":true,"family":"Bova","given":"Shiera","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":478560,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langenheim, Victoria E. 0000-0003-2170-5213 zulanger@usgs.gov","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":1526,"corporation":false,"usgs":true,"family":"Langenheim","given":"Victoria E.","email":"zulanger@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":478557,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shumaker, Lauren E.","contributorId":99666,"corporation":false,"usgs":true,"family":"Shumaker","given":"Lauren E.","affiliations":[],"preferred":false,"id":478561,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Scheirer, Daniel S. dscheirer@usgs.gov","contributorId":2325,"corporation":false,"usgs":true,"family":"Scheirer","given":"Daniel S.","email":"dscheirer@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":478558,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70045890,"text":"ofr20121215 - 2013 - Remote sensing survey of Chinese tallow tree in the Toledo Bend Reservoir area, Louisiana and Texas","interactions":[],"lastModifiedDate":"2018-01-05T10:27:56","indexId":"ofr20121215","displayToPublicDate":"2013-05-08T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-1215","title":"Remote sensing survey of Chinese tallow tree in the Toledo Bend Reservoir area, Louisiana and Texas","docAbstract":"We applied Hyperion sensor satellite data acquired by the National Aeronautics and Space Administration’s Earth Observing-1 (EO-1) satellite in conjunction with reconnaissance surveys to map the occurrences of the invasive Chinese tallow tree (Triadica sebifera) in the Toledo Bend Reservoir study area of northwestern Louisiana and northeastern Texas. The rationale for application of high spectral resolution EO-1 Hyperion data was based on the successful use of Hyperion data in the mapping of Chinese tallow tree in southwestern Louisiana in 2005. In contrast to the single Hyperion image used in the 2005 project, more than 20 EO-1 Hyperion and Advanced Land Imager (ALI) images of the study area were collected in 2009 and 2010 during the fall senescence when Chinese tallow tree leaves turn red. Atmospherically corrected reflectance spectra of Hyperion imagery collected at ground and aerial observation locations provided the input datasets used in the program for spectral discrimination analysis. Discrimination analysis was used to identify spectral indicator sets to best explain variance contained in the input databases. The expectation was that at least one set of Hyperion-based indicator spectra would uniquely identify occurrences of red-leaf Chinese tallow tree; however, no combination of Hyperion-based reflectance datasets produced a unique identifier.\n\nThe inability to discover a unique spectral indicator resulted primarily from relatively sparse coverage by red-leaf Chinese tallow tree within the study area (percentage of coverage was less than 5 percent per 30- by 30-meter Hyperion pixel). To enhance the performance of the spectral discrimination analysis, leaf and canopy spectra of Chinese tallow tree were added to the input datasets to guide the indicator selection. In addition, input databases were segregated by land class obtained from an ALI-based landcover classification in order to reduce the input variance and to promote spectral discrimination of red-leaf Chinese tallow tree. Although no unique spectral identifier for red-leaf Chinese tallow tree was uncovered with these enhanced methods, in some cases predicted spatial patterns throughout the Hyperion images revealed alignment with vegetation associations within each land class that was often observed to contain Chinese tallow trees. These instances were associated particularly with the addition of helicopter-based spectra to the input databases. It was attempted to extend such predictions of likely occurrences of Chinese tallow tree by mapping six of the nine Hyperion swaths and four of the nine land classes, but this attempt produced uncertain results that could not be fully evaluated for accuracy. Even though the final mapping showed promise in identifying likely Chinese tallow tree occurrences, the low percentage of occurrences hindered mapping performance and validation. Results of the mapping suggested that successful detection of Chinese tallow tree in the study area would require a spectral sensor similar to the Hyperion but with a higher ground-level spatial resolution.\n\nAlthough the Hyperion-based spectral mapping did not provide the desired results, the associated field (ground and aerial) surveys did provide for a qualitative assessment of the overall Chinese tallow tree distribution within the study area. Ground and aerial surveys suggested that Chinese tallow tree occurrences were uncommon and were without an observed pattern in relation to proximity to the Toledo Bend Reservoir. Although uncommon and scattered, Chinese tallow trees and shrubs most commonly existed along forest edges, water edges, and fence lines, probably most in line with seed dispersal by birds. Chinese tallow trees were observed to be more densely dispersed within some scrublands and grasslands than were observed in pine, hardwood, and mixed forests.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121215","collaboration":"Prepared in cooperation with the Toledo Bend Project","usgsCitation":"Ramsey, E., Rangoonwala, A., Bannister, T., and Suzuoki, Y., 2013, Remote sensing survey of Chinese tallow tree in the Toledo Bend Reservoir area, Louisiana and Texas: U.S. Geological Survey Open-File Report 2012-1215, xi, 74 p.; Table 14; Database, https://doi.org/10.3133/ofr20121215.","productDescription":"xi, 74 p.; Table 14; Database","numberOfPages":"89","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":272068,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20121215.gif"},{"id":272066,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2012/1215/Table14_RedTallowMapping.xlsx"},{"id":272064,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1215/"},{"id":272067,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/of/2012/1215/Database/ToledoBend_click"},{"id":272065,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1215/OFR%202012-1215.pdf"}],"country":"United States","state":"Louisiana;Texas","county":"Toledo Bend Reservoir","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -94.1,31.1 ], [ -94.1,32.0 ], [ -93.5,32.0 ], [ -93.5,31.1 ], [ -94.1,31.1 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518b65e6e4b0037667dbc7eb","contributors":{"authors":[{"text":"Ramsey, Elijah W. III 0000-0002-4518-5796","orcid":"https://orcid.org/0000-0002-4518-5796","contributorId":72769,"corporation":false,"usgs":true,"family":"Ramsey","given":"Elijah W.","suffix":"III","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":478492,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rangoonwala, Amina 0000-0002-0556-0598 rangoonwalaa@usgs.gov","orcid":"https://orcid.org/0000-0002-0556-0598","contributorId":3455,"corporation":false,"usgs":true,"family":"Rangoonwala","given":"Amina","email":"rangoonwalaa@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":478490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bannister, Terri","contributorId":82836,"corporation":false,"usgs":true,"family":"Bannister","given":"Terri","email":"","affiliations":[],"preferred":false,"id":478493,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Suzuoki, Yukihiro","contributorId":25283,"corporation":false,"usgs":true,"family":"Suzuoki","given":"Yukihiro","email":"","affiliations":[],"preferred":false,"id":478491,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70045914,"text":"sir20135086 - 2013 - Methods for estimating annual exceedance-probability discharges for streams in Iowa, based on data through water year 2010","interactions":[],"lastModifiedDate":"2013-05-08T20:55:26","indexId":"sir20135086","displayToPublicDate":"2013-05-08T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-5086","title":"Methods for estimating annual exceedance-probability discharges for streams in Iowa, based on data through water year 2010","docAbstract":"A statewide study was performed to develop regional regression equations for estimating selected annual exceedance-probability statistics for ungaged stream sites in Iowa. The study area comprises streamgages located within Iowa and 50 miles beyond the State’s borders. Annual exceedance-probability estimates were computed for 518 streamgages by using the expected moments algorithm to fit a Pearson Type III distribution to the logarithms of annual peak discharges for each streamgage using annual peak-discharge data through 2010. The estimation of the selected statistics included a Bayesian weighted least-squares/generalized least-squares regression analysis to update regional skew coefficients for the 518 streamgages. Low-outlier and historic information were incorporated into the annual exceedance-probability analyses, and a generalized Grubbs-Beck test was used to detect multiple potentially influential low flows. Also, geographic information system software was used to measure 59 selected basin characteristics for each streamgage.\n\nRegional regression analysis, using generalized least-squares regression, was used to develop a set of equations for each flood region in Iowa for estimating discharges for ungaged stream sites with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities, which are equivalent to annual flood-frequency recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively. A total of 394 streamgages were included in the development of regional regression equations for three flood regions (regions 1, 2, and 3) that were defined for Iowa based on landform regions and soil regions.\n\nAverage standard errors of prediction range from 31.8 to 45.2 percent for flood region 1, 19.4 to 46.8 percent for flood region 2, and 26.5 to 43.1 percent for flood region 3. The pseudo coefficients of determination for the generalized least-squares equations range from 90.8 to 96.2 percent for flood region 1, 91.5 to 97.9 percent for flood region 2, and 92.4 to 96.0 percent for flood region 3. The regression equations are applicable only to stream sites in Iowa with flows not significantly affected by regulation, diversion, channelization, backwater, or urbanization and with basin characteristics within the range of those used to develop the equations.\n\nThese regression equations will be implemented within the U.S. Geological Survey StreamStats Web-based geographic information system tool. StreamStats allows users to click on any ungaged site on a river and compute estimates of the eight selected statistics; in addition, 90-percent prediction intervals and the measured basin characteristics for the ungaged sites also are provided by the Web-based tool. StreamStats also allows users to click on any streamgage in Iowa and estimates computed for these eight selected statistics are provided for the streamgage.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135086","collaboration":"Prepared in cooperation with the Iowa Department of Transportation and the Iowa Highway Research Board (Project TR-519)","usgsCitation":"Eash, D.A., Barnes, K., and Veilleux, A.G., 2013, Methods for estimating annual exceedance-probability discharges for streams in Iowa, based on data through water year 2010: U.S. Geological Survey Scientific Investigations Report 2013-5086, viii, 63 p.; Downloads Directory, https://doi.org/10.3133/sir20135086.","productDescription":"viii, 63 p.; Downloads Directory","numberOfPages":"76","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalEnd":"2010-10-01","ipdsId":"IP-032892","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"links":[{"id":272115,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135086.gif"},{"id":272113,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5086/sir13_5086web.pdf"},{"id":272114,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2013/5086/downloads/"},{"id":272112,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5086/"}],"country":"United States","state":"Iowa","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -96.64,40.38 ], [ -96.64,43.5 ], [ -90.14,43.5 ], [ -90.14,40.38 ], [ -96.64,40.38 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518b65e6e4b0037667dbc7e7","contributors":{"authors":[{"text":"Eash, David A. 0000-0002-2749-8959 daeash@usgs.gov","orcid":"https://orcid.org/0000-0002-2749-8959","contributorId":1887,"corporation":false,"usgs":true,"family":"Eash","given":"David","email":"daeash@usgs.gov","middleInitial":"A.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnes, Kimberlee K.","contributorId":41476,"corporation":false,"usgs":true,"family":"Barnes","given":"Kimberlee K.","affiliations":[],"preferred":false,"id":478530,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Veilleux, Andrea G. aveilleux@usgs.gov","contributorId":4404,"corporation":false,"usgs":true,"family":"Veilleux","given":"Andrea","email":"aveilleux@usgs.gov","middleInitial":"G.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":478529,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70045873,"text":"ofr20131017 - 2013 - Water-quality, bed-sediment, and biological data (October 2010 through September 2011) and statistical summaries of data for streams in the Clark Fork basin, Montana","interactions":[],"lastModifiedDate":"2013-05-07T15:55:52","indexId":"ofr20131017","displayToPublicDate":"2013-05-07T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1017","title":"Water-quality, bed-sediment, and biological data (October 2010 through September 2011) and statistical summaries of data for streams in the Clark Fork basin, Montana","docAbstract":"Water, bed sediment, and biota were sampled in streams from Butte to near Missoula, Montana, as part of a monitoring program in the upper Clark Fork basin of western Montana; additional water samples were collected from near Galen to near Missoula at select sites as part of a supplemental sampling program. The sampling program was conducted by the U.S. Geological Survey in cooperation with the U.S. Environmental Protection Agency to characterize aquatic resources in the Clark Fork basin, with emphasis on trace elements associated with historic mining and smelting activities. Sampling sites were located on the Clark Fork and selected tributaries. Water samples were collected periodically at 20 sites from October 2010 through September 2011. Bed-sediment and biota samples were collected once at 14 sites during August 2011. This report presents the analytical results and quality-assurance data for water-quality, bed-sediment, and biota samples collected at sites from October 2010 through September 2011. Water-quality data include concentrations of selected major ions, trace elements, and suspended sediment. Turbidity was analyzed for water samples collected at the four sites where seasonal daily values of turbidity were being determined. Daily values of suspended-sediment concentration and suspended-sediment discharge were determined for four sites. Bed-sediment data include trace-element concentrations in the fine-grained fraction. Biological data include trace-element concentrations in whole-body tissue of aquatic benthic insects. Statistical summaries of water-quality, bed-sediment, and biological data for sites in the upper Clark Fork basin are provided for the period of record since 1985.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131017","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Dodge, K.A., Hornberger, M.I., and Dyke, J., 2013, Water-quality, bed-sediment, and biological data (October 2010 through September 2011) and statistical summaries of data for streams in the Clark Fork basin, Montana: U.S. Geological Survey Open-File Report 2013-1017, vi, 134 p., https://doi.org/10.3133/ofr20131017.","productDescription":"vi, 134 p.","numberOfPages":"142","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":400,"text":"Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":272046,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131017.gif"},{"id":272044,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1017/"},{"id":272045,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1017/OF13-1017_508.pdf"}],"country":"United States","state":"Montana","otherGeospatial":"Clark Fork Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -0.01638888888888889,0.0011111111111111111 ], [ -0.01638888888888889,0.0011111111111111111 ], [ -0.01638888888888889,0.0011111111111111111 ], [ -0.01638888888888889,0.0011111111111111111 ], [ -0.01638888888888889,0.0011111111111111111 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518a1460e4b061e1bd533363","contributors":{"authors":[{"text":"Dodge, Kent A. kdodge@usgs.gov","contributorId":1036,"corporation":false,"usgs":true,"family":"Dodge","given":"Kent","email":"kdodge@usgs.gov","middleInitial":"A.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478475,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hornberger, Michelle I. 0000-0002-7787-3446 mhornber@usgs.gov","orcid":"https://orcid.org/0000-0002-7787-3446","contributorId":1037,"corporation":false,"usgs":true,"family":"Hornberger","given":"Michelle","email":"mhornber@usgs.gov","middleInitial":"I.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":478476,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dyke, Jessica jldyke@usgs.gov","contributorId":1035,"corporation":false,"usgs":true,"family":"Dyke","given":"Jessica","email":"jldyke@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":false,"id":478474,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70042647,"text":"70042647 - 2013 - Practical guidance on characterizing availability in resource selection functions under a use-availability design","interactions":[],"lastModifiedDate":"2013-07-15T09:20:03","indexId":"70042647","displayToPublicDate":"2013-05-07T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Practical guidance on characterizing availability in resource selection functions under a use-availability design","docAbstract":"Habitat selection is a fundamental aspect of animal ecology, the understanding of which is critical to management and conservation. Global positioning system data from animals allow fine-scale assessments of habitat selection and typically are analyzed in a use-availability framework, whereby animal locations are contrasted with random locations (the availability sample). Although most use-availability methods are in fact spatial point process models, they often are fit using logistic regression. This framework offers numerous methodological challenges, for which the literature provides little guidance. Specifically, the size and spatial extent of the availability sample influences coefficient estimates potentially causing interpretational bias. We examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer GPS data. Bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability. Spatial autocorrelation in covariates, which is common for landscape characteristics, exacerbated the error in availability sampling leading to increased bias. These results have strong implications for habitat selection analyses using GPS data, which are increasingly prevalent in the literature. We recommend researchers assess the sensitivity of their results to their availability sample and, where bias is likely, take care with interpretations and use cross validation to assess robustness.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Ecological Society of America","doi":"10.1890/12-1688.1","usgsCitation":"Northrup, J.M., Hooten, M., Anderson, C.R., and Wittemyer, G., 2013, Practical guidance on characterizing availability in resource selection functions under a use-availability design: Ecology, v. 94, no. 7, p. 1456-1463, https://doi.org/10.1890/12-1688.1.","productDescription":"8 p.","startPage":"1456","endPage":"1463","ipdsId":"IP-040982","costCenters":[{"id":189,"text":"Colorado Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"links":[{"id":473839,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1890/12-1688.1","text":"Publisher Index Page"},{"id":271952,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":271946,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1890/12-1688.1"}],"volume":"94","issue":"7","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518a145de4b061e1bd53334b","contributors":{"authors":[{"text":"Northrup, Joseph M.","contributorId":101965,"corporation":false,"usgs":true,"family":"Northrup","given":"Joseph","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":471981,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false}],"preferred":true,"id":471978,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Charles R. Jr.","contributorId":75042,"corporation":false,"usgs":true,"family":"Anderson","given":"Charles","suffix":"Jr.","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":471980,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wittemyer, George","contributorId":25058,"corporation":false,"usgs":true,"family":"Wittemyer","given":"George","affiliations":[],"preferred":false,"id":471979,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70045859,"text":"70045859 - 2013 - Analyzing the water budget and hydrological characteristics and responses to land use in a monsoonal climate river basin in South China","interactions":[],"lastModifiedDate":"2013-06-17T09:24:06","indexId":"70045859","displayToPublicDate":"2013-05-07T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Analyzing the water budget and hydrological characteristics and responses to land use in a monsoonal climate river basin in South China","docAbstract":"Hydrological models have been increasingly used by hydrologists and water resource managers to understand natural processes and human activities that affect watersheds. In this study, we use the physically based model, Soil and Water Assessment Tool (SWAT), to investigate the hydrological processes in the East River Basin in South China, a coastal area dominated by monsoonal climate. The SWAT model was calibrated using 8-year (1973–1980) record of the daily streamflow at the basin outlet (Boluo station), and then validated using data collected during the subsequent 8 years (1981–1988). Statistical evaluation shows that SWAT can consistently simulate the streamflow of the East River with monthly Nash–Sutcliffe efficiencies of 0.93 for calibration and 0.90 for validation at the Boluo station. We analyzed the model simulations with calibrated parameters, presented the spatiotemporal distribution of the key hydrological components, and quantified their responses to different land uses. Watershed managers can use the results of this study to understand hydrological features and evaluate water resources of the East River in terms of sustainable development and effective management.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Environmental Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1007/s00267-013-0045-5","usgsCitation":"Wu, Y., and Chen, J., 2013, Analyzing the water budget and hydrological characteristics and responses to land use in a monsoonal climate river basin in South China: Environmental Management, v. 51, no. 6, p. 1174-1186, https://doi.org/10.1007/s00267-013-0045-5.","productDescription":"13 p.","startPage":"1174","endPage":"1186","ipdsId":"IP-042191","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":272031,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":272012,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s00267-013-0045-5"}],"country":"China","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 73.5,18.2 ], [ 73.5,53.6 ], [ 134.8,53.6 ], [ 134.8,18.2 ], [ 73.5,18.2 ] ] ] } } ] }","volume":"51","issue":"6","noUsgsAuthors":false,"publicationDate":"2013-04-23","publicationStatus":"PW","scienceBaseUri":"518a1451e4b061e1bd533337","contributors":{"authors":[{"text":"Wu, Yiping ywu@usgs.gov","contributorId":987,"corporation":false,"usgs":true,"family":"Wu","given":"Yiping","email":"ywu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":478445,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chen, Ji","contributorId":101960,"corporation":false,"usgs":true,"family":"Chen","given":"Ji","email":"","affiliations":[],"preferred":false,"id":478446,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70042106,"text":"70042106 - 2013 - Reconciling resource utilization and resource selection functions","interactions":[],"lastModifiedDate":"2013-10-30T10:08:14","indexId":"70042106","displayToPublicDate":"2013-05-07T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Reconciling resource utilization and resource selection functions","docAbstract":"Summary: 1. Analyses based on utilization distributions (UDs) have been ubiquitous in animal space use studies, largely because they are computationally straightforward and relatively easy to employ. Conventional applications of resource utilization functions (RUFs) suggest that estimates of UDs can be used as response variables in a regression involving spatial covariates of interest. 2. It has been claimed that contemporary implementations of RUFs can yield inference about resource selection, although to our knowledge, an explicit connection has not been described. 3. We explore the relationships between RUFs and resource selection functions from a hueristic and simulation perspective. We investigate several sources of potential bias in the estimation of resource selection coefficients using RUFs (e.g. the spatial covariance modelling that is often used in RUF analyses). 4. Our findings illustrate that RUFs can, in fact, serve as approximations to RSFs and are capable of providing inference about resource selection, but only with some modification and under specific circumstances. 5. Using real telemetry data as an example, we provide guidance on which methods for estimating resource selection may be more appropriate and in which situations. In general, if telemetry data are assumed to arise as a point process, then RSF methods may be preferable to RUFs; however, modified RUFs may provide less biased parameter estimates when the data are subject to location error.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Animal Ecology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1111/1365-2656.12080","usgsCitation":"Hooten, M., Hanks, E., Johnson, D., and Alldredge, M.W., 2013, Reconciling resource utilization and resource selection functions: Journal of Animal Ecology, v. 52, no. 6, p. 1146-1154, https://doi.org/10.1111/1365-2656.12080.","productDescription":"9 p.","startPage":"1146","endPage":"1154","numberOfPages":"9","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-038934","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":473837,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2656.12080","text":"Publisher Index Page"},{"id":271989,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":271986,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/1365-2656.12080"}],"volume":"52","issue":"6","noUsgsAuthors":false,"publicationDate":"2013-04-09","publicationStatus":"PW","scienceBaseUri":"518a145ee4b061e1bd533353","contributors":{"authors":[{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":470778,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hanks, Ephraim M.","contributorId":104630,"corporation":false,"usgs":true,"family":"Hanks","given":"Ephraim M.","affiliations":[],"preferred":false,"id":470781,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Devin S.","contributorId":47524,"corporation":false,"usgs":true,"family":"Johnson","given":"Devin S.","affiliations":[],"preferred":false,"id":470779,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Alldredge, Mat W.","contributorId":65361,"corporation":false,"usgs":true,"family":"Alldredge","given":"Mat","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":470780,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70045884,"text":"sir20125242 - 2013 - Simulations of groundwater flow, transport, and age in Albuquerque, New Mexico, for a study of transport of anthropogenic and natural contaminants (TANC) to public-supply wells","interactions":[],"lastModifiedDate":"2013-05-07T21:26:46","indexId":"sir20125242","displayToPublicDate":"2013-05-07T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-5242","title":"Simulations of groundwater flow, transport, and age in Albuquerque, New Mexico, for a study of transport of anthropogenic and natural contaminants (TANC) to public-supply wells","docAbstract":"Vulnerability to contamination from manmade and natural sources can be characterized by the groundwater-age distribution measured in a supply well and the associated implications for the source depths of the withdrawn water. Coupled groundwater flow and transport models were developed to simulate the transport of the geochemical age-tracers carbon-14, tritium, and three chlorofluorocarbon species to public-supply wells in Albuquerque, New Mexico. A separate, regional-scale simulation of transport of carbon-14 that used the flow-field computed by a previously documented regional groundwater flow model was calibrated and used to specify the initial concentrations of carbon-14 in the local-scale transport model. Observations of the concentrations of each of the five chemical species, in addition to water-level observations and measurements of intra-borehole flow within a public-supply well, were used to calibrate parameters of the local-scale groundwater flow and transport models.\n\nThe calibrated groundwater flow model simulates the mixing of “young” groundwater, which entered the groundwater flow system after 1950 as recharge at the water table, with older resident groundwater that is more likely associated with natural contaminants. Complexity of the aquifer system in the zone of transport between the water table and public-supply well screens was simulated with a geostatistically generated stratigraphic realization based upon observed lithologic transitions at borehole control locations. Because effective porosity was simulated as spatially uniform, the simulated age tracers are more efficiently transported through the portions of the simulated aquifer with relatively higher simulated hydraulic conductivity. Non-pumping groundwater wells with long screens that connect aquifer intervals having different hydraulic heads can provide alternate pathways for contaminant transport that are faster than the advective transport through the aquifer material. Simulation of flow and transport through these wells requires time discretization that adequately represents periods of pumping and non-pumping. The effects of intra-borehole flow are not fully represented in the simulation because it employs seasonal stress periods, which are longer than periods of pumping and non-pumping. Further simulations utilizing daily pumpage data and model stress periods may help quantify the relative effects of intra-borehole versus advective aquifer flow on the transport of contaminants near the public-supply wells. The fraction of young water withdrawn from the studied supply well varies with simulated pumping rates due to changes in the relative contributions to flow from different aquifer intervals.\n\nThe advective transport of dissolved solutes from a known contaminant source to the public-supply wells was simulated by using particle-tracking. Because of the transient groundwater flow field, scenarios with alternative contaminant release times result in different simulated-particle fates, most of which are withdrawn from the aquifer at wells that are between the source and the studied supply well. The relatively small effective porosity required to simulate advective transport from the simulated contaminant source to the studied supply well is representative of a preferential pathway and not the predominant aquifer effective porosity that was estimated by the calibration of the model to observed chemical-tracer concentrations.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125242","collaboration":"National Water-Quality Assessment Program","usgsCitation":"Heywood, C.E., 2013, Simulations of groundwater flow, transport, and age in Albuquerque, New Mexico, for a study of transport of anthropogenic and natural contaminants (TANC) to public-supply wells: U.S. Geological Survey Scientific Investigations Report 2012-5242, ix, 51 p., https://doi.org/10.3133/sir20125242.","productDescription":"ix, 51 p.","numberOfPages":"65","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":272049,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20125242.gif"},{"id":272047,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5242/"},{"id":272048,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5242/pdf/sir2012-5242.pdf"}],"country":"United States","state":"New Mexico","city":"Albuquerque","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -106.088,34.95 ], [ -106.088,35.22 ], [ -106.47,35.22 ], [ -106.47,34.95 ], [ -106.088,34.95 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518a145fe4b061e1bd533357","contributors":{"authors":[{"text":"Heywood, Charles E. cheywood@usgs.gov","contributorId":2043,"corporation":false,"usgs":true,"family":"Heywood","given":"Charles","email":"cheywood@usgs.gov","middleInitial":"E.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478477,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70045851,"text":"sir20135071 - 2013 - Continuous real-time water-quality monitoring and regression analysis to compute constituent concentrations and loads in the North Fork Ninnescah River upstream from Cheney Reservoir, south-central Kansas, 1999–2012","interactions":[],"lastModifiedDate":"2013-05-07T13:25:37","indexId":"sir20135071","displayToPublicDate":"2013-05-07T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-5071","title":"Continuous real-time water-quality monitoring and regression analysis to compute constituent concentrations and loads in the North Fork Ninnescah River upstream from Cheney Reservoir, south-central Kansas, 1999–2012","docAbstract":"Cheney Reservoir, located in south-central Kansas, is the primary water supply for the city of Wichita. The U.S. Geological Survey has operated a continuous real-time water-quality monitoring station since 1998 on the North Fork Ninnescah River, the main source of inflow to Cheney Reservoir. Continuously measured water-quality physical properties include streamflow, specific conductance, pH, water temperature, dissolved oxygen, and turbidity. Discrete water-quality samples were collected during 1999 through 2009 and analyzed for sediment, nutrients, bacteria, and other water-quality constituents. Regression models were developed to establish relations between discretely sampled constituent concentrations and continuously measured physical properties to compute concentrations of those constituents of interest that are not easily measured in real time because of limitations in sensor technology and fiscal constraints. Regression models were published in 2006 that were based on data collected during 1997 through 2003. This report updates those models using discrete and continuous data collected during January 1999 through December 2009. Models also were developed for four new constituents, including additional nutrient species and indicator bacteria. In addition, a conversion factor of 0.68 was established to convert the Yellow Springs Instruments (YSI) model 6026 turbidity sensor measurements to the newer YSI model 6136 sensor at the North Ninnescah River upstream from Cheney Reservoir site. Newly developed models and 14 years of hourly continuously measured data were used to calculate selected constituent concentrations and loads during January 1999 through December 2012. The water-quality information in this report is important to the city of Wichita because it allows the concentrations of many potential pollutants of interest to Cheney Reservoir, including nutrients and sediment, to be estimated in real time and characterized over conditions and time scales that would not be possible otherwise. In general, model forms and the amount of variance explained by the models was similar between the original and updated models. The amount of variance explained by the updated models changed by 10 percent or less relative to the original models. Total nitrogen, nitrate, organic nitrogen, E. coli bacteria, and total organic carbon models were newly developed for this report. Additional data collection over a wider range of hydrological conditions facilitated the development of these models. The nitrate model is particularly important because it allows for comparison to Cheney Reservoir Task Force goals. Mean hourly computed total suspended solids concentration during 1999 through 2012 was 54 milligrams per liter (mg/L). The total suspended solids load during 1999 through 2012 was 174,031 tons. On an average annual basis, the Cheney Reservoir Task Force runoff (550 mg/L) and long-term (100 mg/L) total suspended solids goals were never exceeded, but the base flow goal was exceeded every year during 1999 through 2012. Mean hourly computed nitrate concentration was 1.08 mg/L during 1999 through 2012. The total nitrate load during 1999 through 2012 was 1,361 tons. On an annual average basis, the Cheney Reservoir Task Force runoff (6.60 mg/L) nitrate goal was never exceeded, the long-term goal (1.20 mg/L) was exceeded only in 2012, and the base flow goal of 0.25 mg/L was exceeded every year. Mean nitrate concentrations that were higher during base flow, rather than during runoff conditions, suggest that groundwater sources are the main contributors of nitrate to the North Fork Ninnescah River above Cheney Reservoir. Mean hourly computed phosphorus concentration was 0.14 mg/L during 1999 through 2012. The total phosphorus load during 1999 through 2012 was 328 tons. On an average annual basis, the Cheney Reservoir Task Force runoff goal of 0.40 mg/L for total phosphorus was exceeded in 2002, the year with the largest yearly mean turbidity, and the long-term goal (0.10 mg/L) was exceeded in every year except 2011 and 2012, the years with the smallest mean streamflows. The total phosphorus base flow goal of 0.05 mg/L was exceeded every year. Given that base flow goals for total suspended solids, nitrate, and total phosphorus were exceeded every year despite hydrologic conditions, the established base flow goals are either unattainable or substantially more best management practices will need to be implemented to attain them. On an annual average basis, no discernible patterns were evident in total suspended sediment, nitrate, and total phosphorus concentrations or loads over time, in large part because of hydrologic variability. However, more rigorous statistical analyses are required to evaluate temporal trends. A more rigorous analysis of temporal trends will allow evaluation of watershed investments in best management practices.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135071","collaboration":"Prepared in cooperation with the city of Wichita, Kansas","usgsCitation":"Stone, M.L., Graham, J.L., and Gatotho, J.W., 2013, Continuous real-time water-quality monitoring and regression analysis to compute constituent concentrations and loads in the North Fork Ninnescah River upstream from Cheney Reservoir, south-central Kansas, 1999–2012: U.S. Geological Survey Scientific Investigations Report 2013-5071, viii, 46 p., https://doi.org/10.3133/sir20135071.","productDescription":"viii, 46 p.","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":272007,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135071.gif"},{"id":272005,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5071/"},{"id":272006,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5071/sir13-5071.pdf"}],"country":"United States","state":"Kansas","otherGeospatial":"Cheney Reservoir;North Fork Ninnescah River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -99.25,37.5 ], [ -99.25,38.16 ], [ -97.75,38.16 ], [ -97.75,37.5 ], [ -99.25,37.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518a145be4b061e1bd53333b","contributors":{"authors":[{"text":"Stone, Mandy L. 0000-0002-6711-1536 mstone@usgs.gov","orcid":"https://orcid.org/0000-0002-6711-1536","contributorId":4409,"corporation":false,"usgs":true,"family":"Stone","given":"Mandy","email":"mstone@usgs.gov","middleInitial":"L.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":478424,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":1769,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer","email":"jlgraham@usgs.gov","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478423,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gatotho, Jackline W.","contributorId":76616,"corporation":false,"usgs":true,"family":"Gatotho","given":"Jackline","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":478425,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70045819,"text":"ds709T - 2013 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Herat mineral district in Afghanistan: Chapter T in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-05-06T21:08:57","indexId":"ds709T","displayToPublicDate":"2013-05-06T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"709","chapter":"T","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Herat mineral district in Afghanistan: Chapter T in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Herat mineral district, which has barium and limestone deposits. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420–500 nanometer, nm), green (520–600 nm), red (610–690 nm), and near-infrared (760–890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520–770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency (©JAXA,2007,2008,2009), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band's picture element based on the digital values of all picture elements within a 1,000-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (41 for Herat) and the WGS84 datum. The final image mosaics were subdivided into eight overlapping tiles or quadrants because of the large size of the target area. The eight image tiles (or quadrants) for the Herat area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image. Within the Herat study area, one subarea was designated for detailed field investigations (that is, the Barium-Limestone subarea); this subarea was extracted from the area's image mosaic and is provided as separate embedded geotiff images.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709T","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey. This report is Chapter T in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. For more information, see: Data Series 709.","usgsCitation":"Davis, P.A., Arko, S.A., and Harbin, M., 2013, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Herat mineral district in Afghanistan: Chapter T in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, Readme; 2 Maps: 11 x 8.5 inches and 97.39 x 69.63 inches; 18 Image Files; 18 Metadata Files; 1 Shapefile; DS 709, https://doi.org/10.3133/ds709T.","productDescription":"Readme; 2 Maps: 11 x 8.5 inches and 97.39 x 69.63 inches; 18 Image Files; 18 Metadata Files; 1 Shapefile; DS 709","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2006-01-24","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":271905,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":271896,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/t/"},{"id":271898,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/t/index_maps/Herat_Area-of-Interest_Index_Map.pdf"},{"id":271899,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/t/index_maps/Herat_Image_Index_Map.pdf"},{"id":271897,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/t/1_readme.txt"},{"id":271900,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/t/index_maps/index_maps.html"},{"id":271901,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/t/image_files/image_files.html"},{"id":271902,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/t/metadata/metadata.html"},{"id":271903,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/ds/709/t/shapefiles/shapefiles.html"},{"id":271904,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/709/index.html"}],"country":"Afghanistan","state":"Herat","otherGeospatial":"Herat Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 60.9,34.25 ], [ 60.9,35.5 ], [ 63.1,35.5 ], [ 63.1,34.25 ], [ 60.9,34.25 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5188d465e4b023d2d75b9a38","contributors":{"authors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":478391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arko, Scott A.","contributorId":101929,"corporation":false,"usgs":true,"family":"Arko","given":"Scott","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":478393,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harbin, Michelle L.","contributorId":20590,"corporation":false,"usgs":true,"family":"Harbin","given":"Michelle L.","affiliations":[],"preferred":false,"id":478392,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70045803,"text":"ofr20131097 - 2013 - Mercury and water-quality data from Rink Creek, Salmon River, and Good River, Glacier Bay National Park and Preserve, Alaska, November 2009-October 2011","interactions":[],"lastModifiedDate":"2013-05-06T12:39:33","indexId":"ofr20131097","displayToPublicDate":"2013-05-06T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1097","title":"Mercury and water-quality data from Rink Creek, Salmon River, and Good River, Glacier Bay National Park and Preserve, Alaska, November 2009-October 2011","docAbstract":"Glacier Bay National Park and Preserve (GBNPP), Alaska, like many pristine high latitude areas, is exposed to atmospherically deposited contaminants such as mercury (Hg). Although the harmful effects of Hg are well established, information on this contaminant in southeast Alaska is scarce. Here, we assess the level of this contaminant in several aquatic components (water, sediments, and biological tissue) in three adjacent, small streams in GBNPP that drain contrasting landscapes but receive similar atmospheric inputs: Rink Creek, Salmon River, and Good River.\n\nTwenty water samples were collected from 2009 to 2011 and processed and analyzed for total mercury and methylmercury (filtered and particulate), and dissolved organic carbon quantity and quality. Ancillary stream water parameters (discharge, pH, dissolved oxygen, specific conductance, and temperature) were measured at the time of sampling. Major cations, anions, and nutrients were measured four times. In addition, total mercury was analyzed in streambed sediment in 2010 and in juvenile coho salmon and several taxa of benthic macroinvertebrates in the early summer of 2010 and 2011.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131097","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Nagorski, S.A., Neal, E., and Brabets, T.P., 2013, Mercury and water-quality data from Rink Creek, Salmon River, and Good River, Glacier Bay National Park and Preserve, Alaska, November 2009-October 2011: U.S. Geological Survey Open-File Report 2013-1097, vi, 20 p., https://doi.org/10.3133/ofr20131097.","productDescription":"vi, 20 p.","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2009-11-01","temporalEnd":"2011-10-31","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":271881,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131097.jpg"},{"id":271879,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1097/"},{"id":271880,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1097/pdf/ofr20131097.pdf"}],"country":"United States","state":"Alaska","otherGeospatial":"Glacier Bay National Park","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -138.22,58.43 ], [ -138.22,59.24 ], [ -135.78,59.24 ], [ -135.78,58.43 ], [ -138.22,58.43 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5188d46ce4b023d2d75b9a3c","contributors":{"authors":[{"text":"Nagorski, Sonia A.","contributorId":32940,"corporation":false,"usgs":true,"family":"Nagorski","given":"Sonia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":478374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Neal, Edward G.","contributorId":68775,"corporation":false,"usgs":true,"family":"Neal","given":"Edward G.","affiliations":[],"preferred":false,"id":478375,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brabets, Timothy P. tbrabets@usgs.gov","contributorId":2087,"corporation":false,"usgs":true,"family":"Brabets","given":"Timothy","email":"tbrabets@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":true,"id":478373,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70173425,"text":"70173425 - 2013 - Microhabitat use of the diamond darter","interactions":[],"lastModifiedDate":"2016-06-16T15:44:18","indexId":"70173425","displayToPublicDate":"2013-05-06T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1471,"text":"Ecology of Freshwater Fish","active":true,"publicationSubtype":{"id":10}},"title":"Microhabitat use of the diamond darter","docAbstract":"The only known extant population of the diamond darter (Crystallaria cincotta) exists in the lower 37 km of Elk River, WV, USA. Our understanding of diamond darter habitat use was previously limited, because few individuals have been observed during sampling with conventional gears. We quantified microhabitat use of diamond darters based on measurements of water depth, water velocity and per cent substrate composition. Using spotlights at night-time, we sampled 16 sites within the lower 133 km of Elk River and observed a total of 82 diamond darters at 10 of 11 sampling sites within the lower 37 km. Glides, located immediately upstream of riffles, were the primary habitats sampled for diamond darters, which included relatively shallow depths (<1 m), moderate-to-low water velocities (often < 0.5 m·s−1) and a smooth water surface. Microhabitat use (mean ± SE) of diamond darters was estimated for depth (0.47 ± 0.02 m), average velocity (0.27 ± 0.01 m·s−1) and bottom velocity (0.15 ± 0.01 m·s−1). Substrate used (mean ± SE) by diamond darters was predominantly sand intermixed with lesser amounts of gravel and cobble: % sand (52.1 ± 1.6), % small gravel (12.2 ± 0.78), % large gravel (14.2 ± 0.83), % cobble (19.8 ± 0.96) and % boulder (1.6 ± 0.36). Based on our microhabitat use data, conservation and management efforts for this species should consider preserving glide habitats within Elk River. Spotlighting, a successful sampling method for diamond darters, should be considered for study designs of population estimation and long-term monitoring.
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During the low-flow summer period, sufficient nutrients and a long residence time typically combine with ample sunshine and warm water to fuel blooms of cryptophyte algae, diatoms, green and blue-green algae in the low-gradient, slow-moving reservoir reach of the lower river. Algae in the Tualatin River generally drift with the water rather than attach to the river bottom as a result of moderate water depths, slightly elevated turbidity caused by suspended colloidal material, and dominance of silty substrates. Growth of algae occurs as if on a “conveyor belt” of streamflow, a dynamic system that is continually refreshed with inflowing water. Transit through the system can take as long as 2 weeks during the summer low-flow period. Photosynthetic production of DO during algal blooms is important in offsetting oxygen consumption at the sediment-water interface caused by the decomposition of organic matter from primarily terrestrial sources, and the absence of photosynthesis can lead to low DO concentrations that can harm aquatic life. \n\nThe periods with the lowest DO concentrations in recent years (since 2003) typically occur in August following a decline in algal abundance and activity, when DO concentrations often decrease to less than State standards for extended periods (nearly 80 days). Since 2003, algal populations have tended to be smaller and algal blooms have terminated earlier compared to conditions in the 1990s, leading to more frequent declines in DO to levels that do not meet State standards. This study was developed to document the current abundance and species composition of phytoplankton in the Tualatin River, identify the possible causes of the general decline in algae, and evaluate hypotheses to explain why algal blooms diminish in midsummer. \n\nPlankton and water-quality sample data from 2006 to 2008 were combined with parts of a larger discrete-sample and continuous water-quality monitoring dataset and examined to identify patterns in water-quality and algal conditions since 1991, with a particular emphasis on 2003–08. Longitudinal plankton surveys were conducted in 2006–08 at six sites between river miles (RM) 24.5 and 3.4 at 2- to 3-week intervals, or 5–6 per season, and in-situ bioassay experiments were conducted in 2008 to examine the potential effects of wastewater treatment facility (WWTF) effluent and phosphorus additions on phytoplankton biomass and algal photosynthesis. Phytoplankton and zooplankton community composition, streamflow, and water-quality data were analyzed using multivariate statistical techniques to gain insights into plankton dynamics to determine what factors might be most tied to the abundance and characteristics of the phytoplankton assemblages, and identify possible causes of their declines.\n\nThe connection between low-DO events and algal declines was clearly evident, as bloom crashes were nearly always followed by periods of low DO. Algal blooms occurred each year during 2006–08, producing maximum chlorophyll-a (Chl-a) values in June or July generally in the range of 50–80 micrograms per liter (µg/L). Bloom crashes and absence of sufficient algal photosynthesis in mid- to late-summer contributed to minimum DO concentrations that were less than the State standard of 6.5 milligrams per liter (mg/L) based on the 30-day mean daily concentration, for 62–74 days each year. At times, the absolute minimum State standard (4 mg/L DO) also was not met. To learn more about why low-DO events occurred, specific algal declines during 2003–08 were scrutinized to determine their likely causal factors. From this information, a series of hypotheses were formulated and evaluated in terms of their ability to explain recent declines in algal populations in the river in late summer.\n\nMeteorological, streamflow, turbidity, water temperature, and conductance conditions in the Tualatin River during the 2006–08 summer seasons were not atypical. Natural flow comprised the majority (70–80 percent) of flow each year during spring, but then reduced to 38–40 percent during midsummer when WWTF effluent—which contributed as much as 36 percent—and flow augmentation releases comprised a greater fraction of the flow. Summer 2008 was unusual, however, in the prolonged influence from the Wapato Lake agricultural area near Gaston in the upper part of the basin. The previous winter flooding and levee breach at Wapato Lake caused a much greater area of inundation. As a result, drainage from this area continued into July, much later than normal. A subsequent algal bloom in Wapato Lake then seeded the upper Tualatin River, and this drainage had a profound effect on the downstream plankton community. A large blue-green algae bloom developed—the largest in recent memory—prompting a public health advisory for recreational contact for about two weeks.\n\nAlgal growths and surface blooms are a common feature of the Tualatin River. Most of the dominant algae have growth forms and morphologies that are well suited for planktonic life, employing spines and gas vacuoles to resist settling, forming colonies, and producing mucilage (or toxins) to resist zooplankton grazing. In 2006–08, 143 algal taxa were identified in 117 main-stem samples; diatoms and green algae were more diverse than blue-green, golden, and cryptophyte algae, although these later groups sometimes dominated the overall volumetric abundance (biovolume). The most frequently occurring taxa, occurring in 97–99 percent of samples, were flagellated cryptophytes Cryptomonas erosa and Rhodomonas minuta. Other important algal taxa included centric diatoms Stephanodiscus, Cyclotella, and Melosira species and colonial green algae Scenedesmus and Actinastrum. These taxa comprised the majority of the algal biovolume during much of the growing season. A general seasonal trend in the phytoplankton assemblages was observed, with dominance by filamentous centric diatoms Stephanodiscus and Melosira in spring and early summer, and flagellated cryptophytes and green algae, particularly Chlamydomonas sp., in late-summer; or, in 2008, dominance by blue-green algae Anabaena flos-aquae and Aphanizomenon flos-aquae during the Wapato Lake bloom event.\n\nThere were 99 zooplankton taxa identified from the Tualatin River in 2006–08, composed primarily of cladocerans, copepods, and rotifers. A seasonal increase in zooplankton abundance was observed in early summer just as or shortly after the phytoplankton population began to increase, with populations growing to 15,000−120,000 organisms per cubic meter in the lower river. Zooplankton abundance showed a predictable and distinct longitudinal downstream increase, particularly downstream of Highway 99W (RM 11.6). Although grazing rates were not measured, the data suggest that, at times, zooplankton grazing may affect algal abundance and species composition in the Tualatin River, with diatoms becoming relatively less abundant and flagellated cryptophytes and green algae relatively more abundant during periods when zooplankton densities were highest.\n\nMultivariate statistical analyses identified soluble reactive phosphorus (SRP), natural flow, flow augmentation, and WWTF effluent as important factors influencing Tualatin River phytoplankton populations, with zooplankton density (particularly rotifers and copepods), specific conductance, chloride, and water temperature also having an important influence. Although SRP was highly correlated with the plankton communities, that correlation was likely the result of high or low algal activity (uptake) as SRP concentrations were often reduced to low levels during blooms. While previous studies have already established that phosphorus, among other factors such as flow, places a theoretical cap on the size of the phytoplankton population in the river, sometimes algal declines occur when SRP concentrations are apparently sufficient. To identify alternative causal factors, additional analyses were performed without SRP to focus on other water-quality parameters, zooplankton density, and flow factors. Considering data for all 3 years and including just those samples from the lower Tualatin River not affected by the 2008 Wapato Lake drainage event, three factors (percentage of reservoir flow augmentation, total natural flow, and rotifer density) best explained variations in the phytoplankton assemblages.\n\nAnalyses focusing on the possible causes of algal declines included the above multivariate analyses, scrutiny of 10 specific instances of declines in algal populations during 2003–08 including several bloom–crash sequences, and analyses of historic routine watershed monitoring data from Clean Water Services. Six factors were hypothesized to be important in causing bloom crashes or impeding blooms from rebounding in August: (1) light limitation from cloudy weather, (2) a reduction in the plankton inocula or “seed” entering the lower river from upstream sources, (3) increased summer streamflows, (4) changes in the dominant sources of flow as the percentage of flow augmentation and WWTF discharges have increased, (5) zooplankton grazing, and (6) low concentrations of bioavailable phosphorus (<0.015 milligram per liter). All of these hypotheses are supported in some fashion by the available data and statistical analyses. Zooplankton grazing, short-term declines in photosynthesis from cloudy weather, total flow as it affects residence time, and the dominant source of flow are primary factors responsible for the low-DO events caused by declines in algae in the lower Tualatin River during late summer.\n\nCloudy weather and increased turbidity are known to inhibit algal growth in the Tualatin River, and slight increases in turbidity in recent years may be a problem. Upstream sources of algae are critical in determining the characteristics and size of downstream populations, as illustrated by the Wapato Lake bloom in 2008, but more data are needed from upstream to fully define the importance of this connection. The sources of flow, through their differential contribution of plankton inocula (quality and amount), were, at times, important factors affecting phytoplankton populations. While SRP concentrations were often most highly correlated with phytoplankton species community, the bioavailability of phosphorus is still somewhat unknown and there are several sources to consider. Preliminary bioassay tests suggested that while treated wastewater effluent may stimulate algae at 30 percent concentrations, negative effects (or decreased stimulation) on Chl-a and DO production may occur at concentrations of 50 percent. Targeted data collection and future research will be needed to further understand the importance of these factors on Tualatin River phytoplankton.\n\nWhile the data and analysis completed for this report provide insights into future research and monitoring that would be useful to continue, additional monitoring of turbidity, Chl-a, and plankton abundance and species composition in the upper part of the basin would enhance our understanding of plankton dynamics and factors affecting phytoplankton abundance in the lower river. Assessment of the key upstream sources of algal inocula via surveys of the major flow sources as well as tributaries and wetlands would provide useful information for the management of river water quality. Other studies that could prove useful for developing management strategies include targeted experiments to evaluate the bioavailability of phosphorus from a variety of sources. New research on phytoplankton–zooplankton interactions, and studies of planktivorous fish, might also provide insight about food web dynamics and potential “top-down” effects of fish predation on the plankton communities. In addition, further development of neural-network or other water-quality models would help to evaluate management strategies and provide forecasts of water-quality conditions. Finally, periodic future reassessments of the available data with the multivariate statistical tools used in this study would be helpful to assess whether and how plankton communities are changing, and to continue to shed light on the importance of factors shaping the plankton. Although certain types and sizes of algal blooms are undesirable, minimum phytoplankton populations are an important part of aquatic food webs and are needed to maintain healthy levels of DO in the river. By understanding the sources, characteristics, causal factors, and responses of the plankton communities, management strategies can be developed to improve DO conditions in the lower Tualatin River during the important summer low-flow period.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135037","collaboration":"Prepared in cooperation with Clean Water Services","usgsCitation":"Carpenter, K., and Rounds, S.A., 2013, Plankton communities and summertime declines in algal abundance associated with low dissolved oxygen in the Tualatin River, Oregon: U.S. Geological Survey Scientific Investigations Report 2013-5037, x, 78 p.; Appendixes A-C; Table 10, https://doi.org/10.3133/sir20135037.","productDescription":"x, 78 p.; Appendixes A-C; Table 10","additionalOnlineFiles":"Y","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":271825,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135037.jpg"},{"id":271821,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5037/appendixes/sir20135037_appendixA.xlsx"},{"id":271822,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5037/appendixes/sir20135037_appendixB.xlsx"},{"id":271823,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5037/appendixes/sir20135037_appendixC.xlsx"},{"id":271824,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2013/5037/appendixes/sir20135037_table10.pdf"},{"id":271819,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5037/"},{"id":271820,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5037/pdf/sir20135037.pdf"}],"country":"United States","state":"Oregon","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.6,42.0 ], [ -124.6,46.3 ], [ -116.5,46.3 ], [ -116.5,42.0 ], [ -124.6,42.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5187716ce4b078fc9c244b63","contributors":{"authors":[{"text":"Carpenter, Kurt D. kdcar@usgs.gov","contributorId":1372,"corporation":false,"usgs":true,"family":"Carpenter","given":"Kurt D.","email":"kdcar@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":478342,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rounds, Stewart A. 0000-0002-8540-2206 sarounds@usgs.gov","orcid":"https://orcid.org/0000-0002-8540-2206","contributorId":905,"corporation":false,"usgs":true,"family":"Rounds","given":"Stewart","email":"sarounds@usgs.gov","middleInitial":"A.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478341,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}