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Increasing Precision of Turbidity‐Based Suspended Sediment Concentration and Load Estimates
Author(s) -
Jastram John D.,
Zipper Carl E.,
Zelazny Lucian W.,
Hyer Kenneth E.
Publication year - 2010
Publication title -
journal of environmental quality
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.888
H-Index - 171
eISSN - 1537-2537
pISSN - 0047-2425
DOI - 10.2134/jeq2009.0280
Subject(s) - turbidity , sediment , environmental science , hydrology (agriculture) , turbidite , water quality , soil science , suspended solids , sampling (signal processing) , environmental engineering , geology , geotechnical engineering , ecology , geomorphology , oceanography , biology , filter (signal processing) , wastewater , computer science , computer vision
Turbidity is an effective tool for estimating and monitoring suspended sediments in aquatic systems. Turbidity can be measured in situ remotely and at fine temporal scales as a surrogate for suspended sediment concentration (SSC), providing opportunity for a more complete record of SSC than is possible with physical sampling approaches. However, there is variability in turbidity‐based SSC estimates and in sediment loadings calculated from those estimates. This study investigated the potential to improve turbidity‐based SSC, and by extension the resulting sediment loading estimates, by incorporating hydrologic variables that can be monitored remotely and continuously (typically 15‐min intervals) into the SSC estimation procedure. On the Roanoke River in southwestern Virginia, hydrologic stage, turbidity, and other water‐quality parameters were monitored with in situ instrumentation; suspended sediments were sampled manually during elevated turbidity events; samples were analyzed for SSC and physical properties including particle‐size distribution and organic C content; and 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 explained variability of those physical properties. Results indicated that the inclusion of any of the measured physical properties in turbidity‐based SSC estimation models reduces unexplained variance. Further, the use of hydrologic variables to represent these physical properties, along with turbidity, resulted in a model, relying solely on data collected remotely and continuously, that estimated SSC with less variance than a conventional turbidity‐based univariate model, allowing a more precise estimate of sediment loading. Modeling results are consistent with known mechanisms governing sediment transport in hydrologic systems.