Evaluation of an Intensive Data Collection System for Tennessee Surface Water Quality Assessment and Watershed Model Calibration

Water quality regulators, such as the Tennessee Department of Environment and Conservation, are challenged by data scarcity when identifying surface water quality impairment causes and pollutant sources. Surface water quality model users also seek to identify pollutant sources and design and place best management practices to efficiently improve water quality, but have insufficient data for model calibration. This research documents the design and evaluation of a novel, intensive water quality data collection system consisting of a automatic sampler, bi-weekly grab sampling, and a long term deployment sonde. System design characteristics that were emphasized included a focus on gathering data for common impairment causes (pathogens, siltation, nutrients, and dissolved oxygen-DO) and water quality criteria not currently being evaluated (pH and temperature rate of change and diurnal DO fluctuations). In addition, the system was designed to gather data for watershed model calibration in rural, un-gauged watersheds because agriculture is listed as the predominant source of water quality impairment in Tennessee. Thus, the system was unmanned to reduce labor input, self-powered because of limited access to the electrical grid, provided sample preservation (refrigeration at low pH), and included stage measurement. Two identical prototype systems were installed in adjacent ecoregion 67g watersheds in Greene County, Tennessee: Lick Creek, impaired for pathogens, nutrients, and low DO, and Little Chucky Creek, which is unimpaired and a former ecoregion reference stream.

The two primary objectives of this research were to evaluate the system power demand and determine whether a large water quality dataset improved impairment cause and source identification. A 270 watt solar panel power supply ultimately failed at Lick Creek during the summer when the refrigerated sampler cooling demand peaked, but was sufficient at Little Chucky Creek. System power supply design equations are provided, but with optimization the power supply used would likely be sufficient. The data collected did significantly improve insight into impairment cause identification. For example, total phosphorus rather than total nitrogen concentrations and low DO appeared to be a potential cause of impairment at Lick Creek. The system design was reliable and could be used to calibrate watershed models to improve source assessment.