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dc.contributor.authorJastram, John Dietrichen_US
dc.date.accessioned2014-03-14T20:36:04Z
dc.date.available2014-03-14T20:36:04Z
dc.date.issued2007-05-04en_US
dc.identifier.otheretd-05102007-143910en_US
dc.identifier.urihttp://hdl.handle.net/10919/32514
dc.description.abstractAs the impacts of human activities increase sediment transport by aquatic systems the need to accurately quantify this transport becomes paramount. Turbidity is recognized as an effective tool for monitoring suspended sediments in aquatic systems, and with recent technological advances turbidity can be measured in-situ remotely, continuously, and at much finer temporal scales than was previously possible. Although turbidity provides an improved method for estimation of suspended-sediment concentration (SSC), compared to traditional discharge-based methods, there is still significant variability in turbidity-based SSC estimates and in sediment loadings calculated from those estimates. The purpose of this study was to improve the turbidity-based estimation of SSC. Working at two monitoring sites on the Roanoke River in southwestern Virginia, stage, turbidity, and other water-quality parameters and were monitored with in-situ instrumentation, suspended sediments were sampled manually during elevated turbidity events; those samples were analyzed for SSC and for physical properties; rainfall was quantified by geologic source area. The study identified physical properties of the suspended-sediment samples that contribute to SSC-estimation variance and hydrologic variables that contribute to variance in those physical properties. Results indicated that the inclusion of any of the measured physical properties, which included grain-size distributions, specific surface-area, and organic carbon, in turbidity-based SSC estimation models reduces unexplained variance. Further, the use of hydrologic variables, which were measured remotely and on the same temporal scale as turbidity, to represent these physical properties, resulted in a model which was equally as capable of predicting SSC. A square-root transformed turbidity-based SSC estimation model developed for the Roanoke River at Route 117 monitoring station, which included a water level variable, provided 63% less unexplained variance in SSC estimations and 50% narrower 95% prediction intervals for an annual loading estimate, when compared to a simple linear regression using a logarithmic transformation of the response and regressor (turbidity). Unexplained variance and prediction interval width were also reduced using this approach at a second monitoring site, Roanoke River at Thirteenth Street Bridge; the log-based transformation of SSC and regressors was found to be most appropriate at this monitoring station. Furthermore, this study demonstrated the potential for a single model, generated from a pooled set of data from the two monitoring sites, to estimate SSC with less variance than a model generated only from data collected at this single site. When applied at suitable locations, the use of this pooled model approach could provide many benefits to monitoring programs, such as developing SSC-estimation models for multiple sites which individually do not have enough data to generate a robust model or extending the model to monitoring sites between those for which the model was developed and significantly reducing sampling costs for intensive monitoring programs.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartJastram_Thesis_Final.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectcontinuous monitoringen_US
dc.subjectsediment transport modelingen_US
dc.subjectindicator variablesen_US
dc.subjectmultiple linear regressionen_US
dc.titleImproving Turbidity-Based Estimates of Suspended Sediment Concentrations and Loadsen_US
dc.typeThesisen_US
dc.contributor.departmentEnvironmental Sciences and Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineEnvironmental Sciences and Engineeringen_US
dc.contributor.committeechairZipper, Carl E.en_US
dc.contributor.committeememberZelazny, Lucian W.en_US
dc.contributor.committeememberSpitzner, Dan J.en_US
dc.contributor.committeememberHyer, Kenen_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-05102007-143910/en_US
dc.date.sdate2007-05-10en_US
dc.date.rdate2007-06-12
dc.date.adate2007-06-12en_US


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