Abstract
Automated monitoring of turbidity using in-situ water quality sondes can be used to infer sediment loading and offers several advantages over manual, event-based sampling. However, because turbidity is an optical property and not a true measurement of gravimetric TSS concentration, a regression model between turbidity and TSS must be used prior to any loading estimations. The relationship between turbidity and TSS is dependent on a number of site-specific factors including dissolved organic material, watershed mineralogy and sedimentology, particle density, etc. Therefore, an important step for using turbidity data to estimate sediment loading is the development of a site-specific regression model between turbidity and TSS. Regression model development is conducted by analyzing several samples (n > 100) concurrently for turbidity and TSS across the expected range of turbidity levels.
This dataset was generated from a pre-restoration monitoring project funded through the National Fish and Wildlife Foundation - Gulf Environmental Benefit Fund (Project #67265). The dataset includes information on turbidity readings and TSS measurements for water samples collected at a site upstream of the confluence of Schoolhouse Branch and Magnolia River. Additionally, the R script for the data regressions is provided.
Purpose
The purpose of this project was to develop a site-specific TSS-turbidity regression model that can be used to infer TSS from turbidity data for the Week’s Bay watershed. Samples collected at the Schoolhouse Branch restoration site over the course of early- to mid-2024 by an automated sampler were analyzed to develop the regression model. Completion of this work allows for more robust pre- and post-restoration sediment monitoring and will help inform stream restoration and watershed management planning activities.
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