Investigation of the projected impacts of climate change on the hydrology of Labrador's Churchill river basin using multi-model ensembles
Bibliographic record
Abstract
This manuscript thesis presents four stand-alone papers which all contribute to the investigation of projected impacts of climate change on the hydrology of Labrador’s Churchill River Basin. The overarching goal of this undertaking was to provide useful information to Nalcor Energy, a hydroelectric developer, regarding the change in the amount and timing of water in the Churchill River between a base period (1971-2000) and a future period (2041-2070). Three separate multi-model approaches used data from the North American Regional Climate Change Assessment Program to look at the impacts of climate change on the Churchill River: (i) Bias-corrected precipitation and temperature data forced a hydrologic model to investigate the changes in mean daily streamflow for the Pinus River, a subbasin of the Churchill River; (ii) A new approach (dubbed “fullstream analysis”) took advantage of the full range of simulated hydrological variables from each ensemble member and was used to study the expected changes in mean annual runoff of the entire basin, and; (iii) Weighted multi-model ensembles examined the simulated impacts of climate change on mean monthly runoff for the entire basin. Several results were common across the various approaches. Ensemble mean annual increases in runoff were found to be similar, between 8.9% and 14.6%. Further to this, an increase in cold-season runoff amounts, an earlier onset of the spring melt (though not necessarily a larger spring melt) and no discernable change in the late summer and early fall runoff were found. In an effort to further understand sources of error and uncertainty of the climate models used, water balances were investigated and the annual cycle of residuals quantified. Residual magnitudes varied widely between months and models and were dependent on whether one examined atmospheric or terrestrial balances. Water balance residuals remained relatively consistent between time periods implying they are systemic and not climate dependent.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".