The Kissimmee River Restoration Project was authorized by Congress in 1992 to restore more than 64 km2 (square kilometers) of river/floodplain ecosystem including 69 km of meandering river channel and 10,900 hectares (ha) of wetlands. Although biologic monitoring is an integral and active part of the Kissimmee River restoration, by 2007 geomorphic monitoring that included sediment transport was lacking. In 2007, the U.S. Geological Survey (USGS) entered into a cooperative agreement with the South Florida Water Management District (SFWMD) to determine sediment transport characteristics of the restored section of the Kissimmee River in Pool C. Sediment transport characteristics that are monitored include suspended-sediment concentrations and loads, bedload, and bed material. In addition, the organic content of suspended sediment and bedload was determined. This chapter describes methods and results of the sediment transport monitoring from July 2007 through September 2008 in the Kissimmee River in Pool C.
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Jeffrey C. Lewis of Washington Department of Fish and Wildlife, Patti J. Happe of Olympic National Park, and Kurt J. Jenkins of U. S. Geological Survey are the principal investigators of the monitoring and research program associated with the reintroduction. David J. Manson of Olympic National Park is the lead biological
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Rock samples were collected by the Pennsylvania Department of Environmental Protection (PaDEP) from five different sites to assess the draft standardized leaching column method (ADTI-WP2) for the prediction of weathering rates and water quality at coal mines. Samples were sent to USGS laboratories for mineralogical characterization and to ActLabs for chemical analysis. The samples represent a variety of rock types (shales, sandstones, and coal refuse) that are typical of coal overburden in the eastern United States. These particular samples were chosen for testing the weathering protocols because they represent a range of geochemical and lithologic characteristics, sulfur contents, and acid-base accounting characteristics (Hornberger et al., 2003). The rocks contain variable amounts of pyrite and carbonate minerals and vary in texture.
This chapter includes bulk rock chemical data and detailed mineralogical and textural data for unweathered starting materials used in the interlaboratory validation study, and for two samples used in the early phases of leaching column tests (Wadesville Sandstone, Leechburg Coal Refuse). We also characterize some of the post-weathering rock samples, report trace-element content in leachate, and discuss mineralogical controls on leachate quality based on data from one of the participating laboratories. Table 5.1 lists the samples described in this chapter, the sample numbers, and comments on the characteristics of each lithology. Sample locations are plotted in Figure 5.1. Chapters 2 and 3 describe the sample locations, sample preparation protocols, ABA characteristics, and rationale for selection of rock samples for testing. Microprobe data for pyrite and carbonate minerals are tabulated in Appendix 5.1. Leachate data, along with a series of graphs showing concentration and cumulative transport trends, for the laboratory data discussed in this chapter are included as Excel spreadsheets in Appendices 5.2 and 5.3. Leach column data for the interlaboratory study are evaluated and interpreted in Chapters 7 -11.
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III 0000-0003-3116-4684 cravotta@usgs.gov","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":196993,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","email":"cravotta@usgs.gov","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":false,"id":717439,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Galeone, Daniel G. 0000-0002-8007-9278 dgaleone@usgs.gov","orcid":"https://orcid.org/0000-0002-8007-9278","contributorId":2301,"corporation":false,"usgs":true,"family":"Galeone","given":"Daniel","email":"dgaleone@usgs.gov","middleInitial":"G.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":717440,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jackson, John C. jjackson@usgs.gov","contributorId":2652,"corporation":false,"usgs":true,"family":"Jackson","given":"John","email":"jjackson@usgs.gov","middleInitial":"C.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":717443,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dulong, Frank T. 0000-0001-7388-647X fdulong@usgs.gov","orcid":"https://orcid.org/0000-0001-7388-647X","contributorId":650,"corporation":false,"usgs":true,"family":"Dulong","given":"Frank","email":"fdulong@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":717441,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70157388,"text":"70157388 - 2009 - Estimating phosphorus concentrations following alum treatment using apparent settling velocity","interactions":[],"lastModifiedDate":"2018-02-06T12:36:14","indexId":"70157388","displayToPublicDate":"2015-06-01T05:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2592,"text":"Lake and Reservoir Management","active":true,"publicationSubtype":{"id":10}},"title":"Estimating phosphorus concentrations following alum treatment using apparent settling velocity","docAbstract":"he apparent settling velocity (Vs) is a term used in empirical, steady-state, mass-balance lake models to represent the net phosphorus flux from the water column. The Vollenweider (1969) mixed-reactor lake model was rearranged and used to calculate Vs values for total phosphorus (TP) for three lakes treated with alum to reduce the internal flux of P to the water column (Delavan Lake, Wisconsin; Lake Morey, Vermont; and West Twin Lake, Ohio). An analysis of Vs values was conducted using data from these three lakes for both the pre- and post-alum treated conditions. Analysis of Vs values for both the pre- and post-alum conditions in Lake Morey and West Twin Lake resulted in a post-treatment mean Vs value of 7 ± 2.0 m·yr−1. The effect of the alum treatment, although short-lived in Delavan Lake, resulted in a mean post-treatment Vs value of 3.4 ± 0.3 m·yr−1. The consistency in the post-treatment Vs values in Lake Morey and West Twin Lake is used to demonstrate a predictive analysis method for water column TP concentrations in lakes following a successful treatment of the anoxic sediment area with alum. Additional pre- and post-alum in-lake and watershed loading data are needed to advance this concept into a management model.
","language":"English","publisher":"North American Lake Management Society","doi":"10.1080/07438149909353949","usgsCitation":"Panuska, J., and Robertson, D.M., 2009, Estimating phosphorus concentrations following alum treatment using apparent settling velocity: Lake and Reservoir Management, v. 15, no. 1, p. 28-38, https://doi.org/10.1080/07438149909353949.","productDescription":"11 p.","startPage":"28","endPage":"38","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[],"links":[{"id":475965,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/07438149909353949","text":"Publisher Index Page"},{"id":308376,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio, Vermont, Wisconsin","otherGeospatial":"Lake Delevan, Lake Morey, West Twin 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The plan was threefold: (1) reduce external phosphorus (P) loading by applying Best Management Practices in the watershed, enhance an existing wetland, and short-circuit the inflows through the lake, (2) reduce internal P loading by treating the sediments with alum and removing carp, and (3) rehabilitate the fishery by removing carp and bigmouth buffalo and adding piscivores (biomanipulation). The first and second parts of the plan met with only limited success. With only minor reductions in internal and external P loading, P concentrations in the lake returned to near pre-treatment concentrations. The intensive biomanipulation and resulting trophic cascade (increased piscivores, decreased planktivores, increased large zooplankton populations, and reduced phytoplankton populations) eliminated most of the original problems in the lake (blue-green algal blooms and limited water clarity). However, now there is extensive macrophyte growth and abundant filamentous algae. Without significantly reducing the sources of the problems (high P loading) in Delavan Lake, the increased water clarity may not last. With an improved understanding of the individual components of this rehabilitation program, better future management plans can be developed for Delavan Lake and other lakes and reservoirs with similar eutrophication problems.
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Program","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":578595,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buso, Donald C.","contributorId":33212,"corporation":false,"usgs":true,"family":"Buso","given":"Donald","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":512919,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Likens, Gene E.","contributorId":56363,"corporation":false,"usgs":true,"family":"Likens","given":"Gene","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":512920,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70041614,"text":"70041614 - 2009 - Diffusion-equation representations of landform evolution in the simplest circumstances: Appendix C","interactions":[],"lastModifiedDate":"2015-10-29T10:03:39","indexId":"70041614","displayToPublicDate":"2014-12-08T08:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":4,"text":"Book"},"publicationSubtype":{"id":13,"text":"Handbook"},"title":"Diffusion-equation representations of landform evolution in the simplest circumstances: Appendix C","docAbstract":"The diffusion equation is one of the three great partial differential equations of classical physics. It describes the flow or diffusion of heat in the presence of temperature gradients, fluid flow in porous media in the presence of pressure gradients, and the diffusion of molecules in the presence of chemical gradients. [The other two equations are the wave equation, which describes the propagation of electromagnetic waves (including light), acoustic (sound) waves, and elastic (seismic) waves radiated from earthquakes; and LaPlace’s equation, which describes the behavior of electric, gravitational, and fluid potentials, all part of potential field theory. The diffusion equation reduces to LaPlace’s equation at steady state, when the field of interest does not depend on t. Poisson’s equation is LaPlace’s equation with a source term.]
\nJoseph Fourier developed the diffusion equation for heat conduction in 1807, and it has significant associations with probability theory (Narasimhan, 2009), as we will see shortly. In a novel and fascinating application, Gene Humphreys has employed solutions of the diffusion equation to describe the density of desert tortoises in the presence of population gradients caused by new dirt roads cut in the Mojave Desert. These new dirt roads induce an immediate line sink for unsuspecting tortoises. As of this writing in early September, I am not sure whether Gene has published this work.
\nMost of us here know that the diffusion equation has also been used to describe the evolution through time of scarp-like landforms, including fault scarps, shoreline scarps, or a set of marine terraces. The methods, models, and data employed in such studies have been described in the literature many times over the past 25 years. For most situations, everything you will ever need (or want) to know can be found in Hanks et al. (1984) and Hanks (2000), the latter being a review of numerous studies of the 1980s and 1990s and a summary of available estimates of the mass diffusivity κ. The geometric parameterization of scarp-like landforms is shown in Figure 1.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Friends of the Pleistocene 2009 Pacific Cell Field Trip: Paleoseismic, geomorphic, and geodetic studies across the Central Great Basin: Exploring active deformation along the eastern edge of the Pacific/North American plate boundary.","largerWorkSubtype":{"id":13,"text":"Handbook"},"language":"English","usgsCitation":"Hanks, T.C., 2009, Diffusion-equation representations of landform evolution in the simplest circumstances: Appendix C, 6 p.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-016448","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":310751,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56334338e4b048076347eebf","contributors":{"authors":[{"text":"Hanks, Thomas C. 0000-0003-0928-0056 thanks@usgs.gov","orcid":"https://orcid.org/0000-0003-0928-0056","contributorId":3065,"corporation":false,"usgs":true,"family":"Hanks","given":"Thomas","email":"thanks@usgs.gov","middleInitial":"C.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":578665,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70120865,"text":"70120865 - 2009 - Online interactive U.S. Reservoir Sedimentation Survey Database","interactions":[],"lastModifiedDate":"2014-08-18T10:27:35","indexId":"70120865","displayToPublicDate":"2013-08-18T10:22:00","publicationYear":"2009","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1578,"text":"Eos, Transactions, American Geophysical Union","onlineIssn":"2324-9250","printIssn":"0096-394","active":true,"publicationSubtype":{"id":10}},"title":"Online interactive U.S. Reservoir Sedimentation Survey Database","docAbstract":"In April 2009, the U.S. Geological Survey and the Natural Resources Conservation Service (prior to 1994, the Soil Conservation Service) created the Reservoir Sedimentation Survey Database (RESSED) and Web site, the most comprehensive compilation of data from reservoir bathymetric and dry basin surveys in the United States. RESSED data can be useful for a number of purposes, including calculating changes in reservoir storage characteristics, quantifying rates of sediment delivery to reservoirs, and estimating erosion rates in a reservoir's watershed.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Eos, Transactions American Geophysical Union","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1029/2009EO230003","usgsCitation":"Gray, J., Bernard, J., Schwarz, G., Stewart, D.W., and Ray, K., 2009, Online interactive U.S. Reservoir Sedimentation Survey Database: Eos, Transactions, American Geophysical Union, v. 90, no. 23, https://doi.org/10.1029/2009EO230003.","productDescription":"1 p.","startPage":"199","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":475967,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2009eo230003","text":"Publisher Index Page"},{"id":292389,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":292387,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1029/2009EO230003"}],"country":"Puerto Rico;United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 144.616667,12.233333 ], [ 144.616667,71.833333 ], [ -64.566667,71.833333 ], [ -64.566667,12.233333 ], [ 144.616667,12.233333 ] ] ] } } ] }","volume":"90","issue":"23","noUsgsAuthors":false,"publicationDate":"2011-06-03","publicationStatus":"PW","scienceBaseUri":"53f25fe9e4b033341871893b","contributors":{"authors":[{"text":"Gray, J.B.","contributorId":73119,"corporation":false,"usgs":true,"family":"Gray","given":"J.B.","email":"","affiliations":[],"preferred":false,"id":498503,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bernard, J.M.","contributorId":43999,"corporation":false,"usgs":true,"family":"Bernard","given":"J.M.","email":"","affiliations":[],"preferred":false,"id":498502,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwarz, G. E. 0000-0002-9239-4566","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":14852,"corporation":false,"usgs":true,"family":"Schwarz","given":"G. E.","affiliations":[],"preferred":false,"id":498501,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stewart, D. W.","contributorId":86194,"corporation":false,"usgs":true,"family":"Stewart","given":"D.","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":498505,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ray, K.T.","contributorId":77758,"corporation":false,"usgs":true,"family":"Ray","given":"K.T.","email":"","affiliations":[],"preferred":false,"id":498504,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70047288,"text":"70047288 - 2009 - Converting nonstandard fish sampling data to standardized data","interactions":[],"lastModifiedDate":"2021-06-04T16:44:48.968639","indexId":"70047288","displayToPublicDate":"2013-01-01T21:25:04","publicationYear":"2009","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"12","title":"Converting nonstandard fish sampling data to standardized data","docAbstract":"Fishery biologists spend considerable effort over multiple years collecting data on fish population and community status using a particular sampling method or set of methods. However, new (and often more effective) sampling methods and technologies are continuously being developed. To incorporate these new sampling techniques, fishery biologists need a means for converting sample data collected using old methods so they can be compared with data collected using new methods. Similarly, fishery biologists often need a means to compare fish sample data collected using the same method over time (e.g., from year to year) and space (e.g., between sample sites). If fish abundance, species presence, or richness are estimated using an unbiased statistical estimator, the estimates can be validly compared, even if the fish sample data were collected with different methods. However, if unbiased statistical estimators were not used, biologists need methods for adjusting fish sampling data collected using different methods or using the same method collected under different sampling conditions. In this chapter, we describe and provide examples of statistical techniques for converting nonstandard fish sampling data to standardized data and for making comparisons of fish sampling data collected at different times or at different locations. We define standard fish sampling data as data collected using the standardized fish sampling methods described throughout this book. Any other sampling methods and associated data are thus defined as nonstandard. Before delving into the details of the statistical modeling techniques, we describe the nature of fish sample data, their uses, and their limitations.
Catch-effort measures, such as relative abundance and catch per unit effort (CPUE), are more formally described as indices. Here, we define an index as any measure or count of a species or community (e.g., species richness) based on direct observation without an estimate of the ability to count individuals or species. Indices have some very desirable characteristics for use in fisheries research and management. In general (but not always), indices require less effort to collect and are usually more precise than unbiased population estimators (e.g., CPUE versus capture–recapture estimates of abundance). The proper use of indices for assessment of fish populations or communities, however, requires that the relationship between an index and the true value (e.g., fish density, species richness) is relatively constant (1) across the observable range of true values, (2) through time when evaluating trends at a single location, and (3) across space when making comparisons among locations.
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It is commonly used in flame retardants, agriculture and drilling.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Earth","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"AGI","usgsCitation":"Water Resources Division, U.S. Geological Survey, 2009, Mineral resource of the month: bromine: Earth, v. 54, no. 4, p. 29-29.","productDescription":"1 p.","startPage":"29","endPage":"29","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":272108,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518b73e8e4b0037667dbc819","contributors":{"authors":[{"text":"Water Resources Division, U.S. Geological Survey","contributorId":128075,"corporation":true,"usgs":false,"organization":"Water Resources Division, U.S. Geological Survey","id":535513,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70045918,"text":"70045918 - 2009 - Mineral resource of the month: diamond","interactions":[],"lastModifiedDate":"2013-05-08T19:57:18","indexId":"70045918","displayToPublicDate":"2013-01-01T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1419,"text":"Earth","active":true,"publicationSubtype":{"id":10}},"title":"Mineral resource of the month: diamond","docAbstract":"The article presents information on diamond, which is regarded as the world's most popular gemstone. It states that there is strength in the covalent bonding between its carbon atoms, resulting to the strength of its physical properties. The presence of colors in diamonds may be attributed to the impurities that settle in the crystal lattice. 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It states that early civilizations valued gold because of its scarcity, durability and characteristics yellow color. By the late 20th century, gold was used as an industrial metal because of its unique physicochemical properties. The U.S. has several productive deposits of gold, including placer, gold-quartz lode, epithermal and Carlin-type gold deposits.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Earth","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"AGI","usgsCitation":"George, M.W., 2009, Mineral resource of the month: gold: Earth, v. 54, no. 1, p. 29-29.","productDescription":"1 p.","startPage":"29","endPage":"29","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":272093,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518b73ece4b0037667dbc854","contributors":{"authors":[{"text":"George, Micheal W. mgeorge@usgs.gov","contributorId":3128,"corporation":false,"usgs":true,"family":"George","given":"Micheal","email":"mgeorge@usgs.gov","middleInitial":"W.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":478525,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}