Climate change impacts on hydrology and phosphorus loads under projected global warming levels for the Lake of the Woods watershed
Bibliographic record
Abstract
Climate change and variability could directly impact inflows and nutrients from the watershed into Lake of the Woods (LoW), which has been experiencing seasonal algal blooms with a shift in community composition of bloom species mostly to toxin species over the past decades. The main contributing factor to these blooms is deemed to be external inflowing total phosphorus (TP) mostly from the Rainy River. Here, we advance our understanding of potential impacts of climate change on streamflow and non-point source (NPS) TP in the LoW watershed. To this end and for the first time, we developed LoW watershed CanSWAT (Soil and Water Assessment Tool) models forced with climate projections from seven downscaled Coupled Model Intercomparison Project Phase 6 Global Climate Models under two Shared Socioeconomic Pathways. We analyzed hydrological and water quality change at policy-relevant +1.5 to +3.0 °C Global Mean Temperature (GMT) above the pre-industrial period. Under 1.5–3.0 °C GMT increases, projected mean runoff (NPS TP) is 7.3–36.6 % (1.5–117.6 %) and 7.5–31.7 % (−1.6 to 81.4 %) higher than the 1980–2010 reference period in winter/spring in the Precambrian Shield and Agassiz zone, respectively; and lower in summer (−3.0 to −18.8 % (−0.01 to −20.1 %) for both regions). Changes in mean annual Rainy River NPS TP range from −1.8 to 3.1 %. Furthermore, there is a shift in seasonal delivery of NPS TP loads to LoW with winter/spring increases (3.5–26.8 %) and summer/autumn reductions (−0.01 to −14.8 %), which could potentially affect algal productivity and general water quality in LoW. These findings provide important insights to inform future water quality and nutrient management plans.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".