Improving Turbidity-Based Estimates of Suspended Sediment Concentrations and Loads

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 ca...

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Main Author: Jastram, John Dietrich
Other Authors: Environmental Sciences and Engineering
Format: Others
Published: Virginia Tech 2014
Subjects:
Online Access:http://hdl.handle.net/10919/32514
http://scholar.lib.vt.edu/theses/available/etd-05102007-143910/
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spelling ndltd-VTETD-oai-vtechworks.lib.vt.edu-10919-325142020-09-26T05:37:42Z Improving Turbidity-Based Estimates of Suspended Sediment Concentrations and Loads Jastram, John Dietrich Environmental Sciences and Engineering Zipper, Carl E. Zelazny, Lucian W. Spitzner, Dan J. Hyer, Ken continuous monitoring sediment transport modeling indicator variables multiple linear regression 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. Master of Science 2014-03-14T20:36:04Z 2014-03-14T20:36:04Z 2007-05-04 2007-05-10 2007-06-12 2007-06-12 Thesis etd-05102007-143910 http://hdl.handle.net/10919/32514 http://scholar.lib.vt.edu/theses/available/etd-05102007-143910/ Jastram_Thesis_Final.pdf In Copyright http://rightsstatements.org/vocab/InC/1.0/ application/pdf Virginia Tech
collection NDLTD
format Others
sources NDLTD
topic continuous monitoring
sediment transport modeling
indicator variables
multiple linear regression
spellingShingle continuous monitoring
sediment transport modeling
indicator variables
multiple linear regression
Jastram, John Dietrich
Improving Turbidity-Based Estimates of Suspended Sediment Concentrations and Loads
description 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. === Master of Science
author2 Environmental Sciences and Engineering
author_facet Environmental Sciences and Engineering
Jastram, John Dietrich
author Jastram, John Dietrich
author_sort Jastram, John Dietrich
title Improving Turbidity-Based Estimates of Suspended Sediment Concentrations and Loads
title_short Improving Turbidity-Based Estimates of Suspended Sediment Concentrations and Loads
title_full Improving Turbidity-Based Estimates of Suspended Sediment Concentrations and Loads
title_fullStr Improving Turbidity-Based Estimates of Suspended Sediment Concentrations and Loads
title_full_unstemmed Improving Turbidity-Based Estimates of Suspended Sediment Concentrations and Loads
title_sort improving turbidity-based estimates of suspended sediment concentrations and loads
publisher Virginia Tech
publishDate 2014
url http://hdl.handle.net/10919/32514
http://scholar.lib.vt.edu/theses/available/etd-05102007-143910/
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