SENSITIVITY OF HYDROLOGICAL VARIABLES IN THE ARCTIC WATERSHED, COPPERMINE RIVER, NWT, CANADA DUE TO HYPOTHETICAL CLIMATE CHANGE
Bibliographic record
Abstract
The hydrological sensitivities to long-term climate change of the Coppermine River watershed in the Arctic region of Canada were analyzed using a watershed runoff model. This model describes an interdependent tank - cascade model that uses a mass balance coupled with linear reservoir concepts. It is physically based and uses climatological considerations not possible for watersheds. Mean annual and seasonal runoff resulting from a range of hypothetical climate changes were compared and evaluated. Water balance modelling techniques, modified for assessing climate effects, were developed and tested for a watershed using climate change Scenarios from state of the art general circulation models and a series of hypothetical Scenarios. In general, changes in precipitation had a larger effect on changes in runoff than changes in temperature. Changes in precipitation had significant effects on runoff during all seasons. Changes in temperature primarily affected the temporal distribution of runoff throughout the year. The changes in temperature affected the timing of snowmelt and the ratio of rain to snow. The effects of temperature were particularly significant during the spring and summer seasons. On an annual basis, increases in temperature led only to slight decreases in runoff. The effects of an increase in mean annual temperature of 1 o C on annual runoff could be offset by an increase in annual precipitation of 10%. The magnitude of natural climatic variability was large and might mask the effects of long-term climate changes. These results raise the possibility of major environmental and socioeconomic difficulties, and have significant implications for future water resource planning and management.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".