{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/32514"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/32514","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Improving Turbidity-Based Estimates of Suspended Sediment Concentrations and Loads","abstract":"As 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.","abstract_html":"As 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.","abstract_has_math":false,"creators":["Jastram, John Dietrich"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Environmental Sciences and Engineering","degree_department":"Environmental Sciences and Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Zipper, Carl E."],"committee_members":["Zelazny, Lucian W.","Spitzner, Dan J.","Hyer, Ken"],"year":2007,"date_issued":"2007-05-04","date_published":"2007-05-04","updated_at":"2026-07-22T22:19:54Z","subjects":["continuous monitoring","sediment transport modeling","indicator variables","multiple linear regression"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-05102007-143910"],"render_values":[{"text":"etd-05102007-143910","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/32514","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Zipper, Carl E."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Zelazny, Lucian W.","Spitzner, Dan J.","Hyer, Ken"]},{"key":"dc:contributor.department","label":"Department","values":["Environmental Sciences and Engineering"]},{"key":"dc:creator","label":"Author","values":["Jastram, John Dietrich"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T20:36:04Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T20:36:04Z","2007-06-12"]},{"key":"dc:date.issued","label":"Date","values":["2007-05-04"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Environmental Sciences and Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["continuous monitoring","sediment transport modeling","indicator variables","multiple linear regression"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-05102007-143910"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/32514"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["As 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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:title","label":"Title","values":["Improving Turbidity-Based Estimates of Suspended Sediment Concentrations and Loads"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Zipper, Carl E."],"dc:contributor.committeemember":["Zelazny, Lucian W.","Spitzner, Dan J.","Hyer, Ken"],"dc:contributor.department":["Environmental Sciences and Engineering"],"dc:creator":["Jastram, John Dietrich"],"dc:date.accessioned":["2014-03-14T20:36:04Z"],"dc:date.available":["2014-03-14T20:36:04Z","2007-06-12"],"dc:date.issued":["2007-05-04"],"dc:description.abstract":["As 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."],"dc:description.degree":["Master of Science"],"dc:identifier.other":["etd-05102007-143910"],"dc:identifier.uri":["http://hdl.handle.net/10919/32514"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["continuous monitoring","sediment transport modeling","indicator variables","multiple linear regression"],"dc:title":["Improving Turbidity-Based Estimates of Suspended Sediment Concentrations and Loads"],"dc:type":["Thesis"],"thesis:degree_discipline":["Environmental Sciences and Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:54Z"}