THE CONTROLS AND DRIVERS OF DISSOLVED ORGANIC CARBON QUANTITY AND DISSOLVED ORGANIC MATTER QUALITY IN AN IMPACTED GREAT LAKES WATERSHED
Bibliographic record
Abstract
Intensely managed and modified catchments in the Great Lakes are linked to eutrophication and hypoxia of receiving water bodies downstream, resulting in water quality impairment, and adverse impacts on aquatic ecology. While much focus has been on the role of phosphorous and nitrogen, dissolved organic carbon (DOC) plays a complex and critical role in lake biogeochemical cycles, as it influences the interations between nutrients and contaminants in water and soil through processes of mobilization, transport, biological uptake, and deposition. Human-dominated landscapes have a range of consequences on DOC dynamics as catchment hydrology, plant cover, and nutrient inputs are altered in these environments. As such, the objectives of this study were to identify the controls and drivers of DOC quantity and DOM quality in the Spencer Creek watershed, which is the largest contributor of water to Cootes Paradise that ultimately drains into Lake Ontario. The 159 km2 study area of the catchment is complex, as the present landscape is composed of a mosaic of various land uses including agriculture, forest, wetland, urban, and industrial regions. Flow alterations contribute to the complexity of the watershed as there are managed reservoirs and alterations in water courses. From 2016- 2018, hydrometric data was collected across 9 monitoring sites, along with surface water samples that were analyzed for DOC concentration and optical properties. Results indicate differences in flow magnitudes and stream DOC between dry and wet conditions, where concentrations during wet conditions were significantly higher compared to dry. Additionally, there was substantial variation in DOC concentration and quality across the Spencer Creek watershed. DOC concentrations were found to be the lowest at groundwater influenced sites in the headwaters of the watershed, and the highest in the mid-catchment region where DOC quality was strongly influenced by wetland sources. The reservoir-influenced sites showed relatively intermediate concentrations of DOC, with quality that exhibited strong microbial signatures. At the outlet, DOC concentrations were attenuated and DOC quality was intermediate between allochthonous and autochthonous end members, reflecting upstream mixing processes. These processes were presented as a conceptual model of water and DOC movement through the Spencer Creek watershed. The implications of this research suggest that with anticipated wetter and warmer conditions DOC concentrations would increase in the watershed. The repercussions of increased DOC concentrations overall imply a decrease of terrestrial carbon storage, and greater input into more reactive and susceptible pools, which may result in further water quality degradation. Overall, the findings from this research provide insight into the fate and transport of water and DOC in a complex, managed catchment in the Great Lakes region, with the aims of providing key information for local stakeholders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".