Modeling the Potential Impacts of Climate Change on a Small Watershed in Labrador, Canada
Bibliographic record
Abstract
Hydroelectric power producers are strongly dependent on the climate to deliver the fuel necessary to generate electricity. This fuel source is watershed runoff, which is manifested as the balance between components of the climate system, primarily precipitation and evapotranspiration. Climate change threatens to alter global hydrological regimes and impacts need to be assessed to determine production vulnerabilities and opportunities. In this paper, dynamically downscaled regional climate models from the North American Regional Climate Change Assessment Program (NARCCAP) are combined with statistical bias correction techniques to generate 30-year time series of temperature and precipitation for a base period (1980s) and a future period (2050s). These time series are transformed into streamflow using the WATFLOOD hydrological model that has been calibrated for a sub-basin of the Churchill River. Results are consistent with IPCC results and show increasing mean annual streamflow of approximately 9% between the base and future periods with larger increases in winter runoff and little or no change during late summer and fall. Inter-model comparison and probabilistic methods are used to provide further insight into simulation results.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".