Water Quality Analysis of Black Creek Watershed: Identification of Point and Nonpoint Sources of Pollution and Loading Simulation Using the SWAT Model
Bibliographic record
Abstract
Nearshore Lake Ontario suffers from several beneficial use impairments due to water quality issues from the Genesee River and its contributing tributaries. Segments of Black Creek located in the Lower Genesee River basin are listed as impacted on the New York State 303(d) list because of excess sediment, nutrient, and bacteria losses. Sources of these pollutants from the Black Creek watershed include improperly managed cropland and pastures, dairy manure application, and effluent discharges from wastewater treatment plants. An assessment of the Black Creek watershed was undertaken to determine the nutrient and sediment contribution of Black Creek to the Genesee River and to determine sources of nutrients and sediment loss geospatially within the watershed. To accomplish this task, a multifaceted, integrated approach was taken by combining stream monitoring, segment analysis, and hydrologic modeling [Soil and Water Assessment Tool (SWAT)]. The annual losses (June 2010 through May 2011) of total phosphorus (TP), total nitrogen (TN), and total coliform bacteria from the Black Creek watershed were 16.5 MT/yr, 349.4 MT/yr, and 7.0E15 CFU/yr, respectively, where most of the losses occurred in the upper portion of the watershed. Impacted tributaries (Bigelow Creek and Spring Creek) had the highest areal loads of nutrients and bacteria and were a focus for remediation. More than 70% of the TP load was found to be due to anthropogenic sources including but not limited to manure applications from Confined Animal Feeding operations, the Bergen wastewater treatment plant, and nonpoint agricultural practices throughout the watershed. Sediment loss, on the other hand, was the highest in the downstream reaches of Black Creek where 73% of the total sediment load (8,360.6 MT/yr) occurs due to excessive flooding and stream bank erosion during events. These findings were used to calibrate a SWAT model for Black Creek that simulated the impact of implementing several Best Management Practices (BMPs) to reduce phosphorus and sediment loads. Individual BMPs reduced TP loads from Black Creek at Lower BC anywhere from 0 to 28% and sediment 0 to 84%. A holistic approach to watershed remediation using a combination of several effective BMPs focusing on major contributors of phosphorus and sediment reduced TP 28% and total suspended solids (TSS) 73%. This remedial action plan, if implemented, can reach a water quality target of 65 ?g P/L proposed by the Department of Environmental Conservation, which would reduce the annual TP concentration from 79.6 ?g P/L to 38.3 ?g P/L. This scenario can be used to determine an appropriate Total Maximum Daily Load for Black Creek that will help attain the ultimate goal of reducing the impairments of nearshore Lake Ontario.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".