The Use of Decision Analysis and Watershed Modeling to Investigate E.coli Potential Sources and Solutions in Lake Tuscaloosa Watershed, Alabama
Bibliographic record
Abstract
Lake Tuscaloosa, an artificial impoundment that serves as a public water supply, is located in Tuscaloosa and Fayette counties in the State of Alabama, in the Southeastern United States.Recent studies and monitoring of the lake show high levels of E.coli bacteria in the upper parts of the lake (near the main stream entrances) during periods of high stream flow.These high levels of E.coli are a concern for many different interested parties in the area.The city is under pressure to strengthen its management, monitoring, and control of existing and future pollutant sources (mostly land development) around the lake that is in its jurisdiction.Additionally, the city has to consider other sources of bacteria in the watershed outside of its jurisdiction as potential causes of these elevated bacteria levels.The decision analysis framework and modeling schemes developed as part of this research examine flow, E.coli sources and transport issues, along with potential solutions.The decision analysis framework assisted at different stages of the project during the collection and management of the information that helped in the analysis of the problems and solutions.The flow and E.coli watershed models assisted in the analysis of the available data for the watershed to identify locations, seasons, and flow ranges associated with the E.coli discharges.Developing a strategy to maintain the E.coli levels below the permissible limits in the watershed was challenging because many factors and information were needed for consideration during the data analysis and decision making parts of the research. IntroductionThe Lake Tuscaloosa watershed is located in Tuscaloosa and Fayette counties in the State of Alabama, in the Southeastern United States.The Lake, which was constructed on North River in 1970, serves as the major public water supply for the surrounding communities and is an important recreational water body in an area lacking in natural lakes.The watershed covers an area of approximately 1100 km 2 .The lake covers an area of approximately 24 km 2 and it holds about 150 million m 3 of water.The watershed and the lake are presented in Figure 1.1.This area normally has high rainfall (long term average of about 1400 mm/y), but is currently undergoing a severe drought, with about half of the normal rainfall having fallen during the last rain year.Even with this shortage, Lake Tuscaloosa has proven to be a reliable and sustainable water supply for the area.The reliability of this water supply has been an important component for the economy in the area.Recent studies and monitoring of the lake have shown high levels of E.coli bacteria, especially in the northern parts of the lake (near the main river entrances) during periods of high stream flow (O'Neil, 2005).These high levels of E.coli have been identified as a concern for many stakeholders in the area.E.coli is a type of fecal coliform bacteria that is usually found in the intestines of warm blooded animals such as humans, cattle, birds, and different wild animals and is commonly used as an indicator of domestic sewage contamination and the presence of possible pathogens.E.coli are mostly harmless bacteria commonly found in fecal discharges from warm blooded animals.Most strains are harmless, but some, most notably O157:H7, can cause serious illness in humans.In 1986, the EPA developed criteria for E.coli and Enterococci using currently accepted illness rates.These bacteria are assumed to be more specifically related to poorly treated human sewage than fecal coliforms.Many states and agencies therefore monitor E.coli as part of their surveillance monitoring activities.The typical analytical methods used are non-specific to the many E.coli stains; therefore, if E.coli are detected, it should not be assumed
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".