Reservoir management under predictable climate variability and change
Bibliographic record
Abstract
The potential effects of climate change on future water budget components and streamflow in the Mississippi River (Ontario) are assessed. Analyses of historic hydrometric data indicate an increasing trend in winter streamflows due to the rising winter air temperatures across the region over the latter half of the 20th century. These temperatures have resulted in reduced snow accumulation and earlier spring snowmelt. Projected future climate data are developed using the second generation Coupled Global Climate Model and downscaled using the change factor method for the Mississippi River watershed (Ontario). The projected future climate data are then used as input to a calibrated hydrologic model for simulation of future water balance and streamflows in this river basin. These simulations predict a gradual annual rate of change of: 0.1% increase in total precipitation; 0.2% increase in rainfall; 0.7% decrease in snowfall; 0.2% increase in potential evapotranspiration; 0.1% decrease in soil moisture; 1.4% increase in water deficit; 0.5% increase in streamflow during winter months; and 0.3% decrease in summer streamflows. Cyclic pattern analysis of the historic streamflow records suggests the existence of pronounced 3-year and 12-year cycles, providing short-term streamflow forecasting opportunities for optimum reservoir management operations during the wet-year/dry-year cycles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".