Uncertainty in the impacts of projected climate change on the hydrology of a subarctic environment: Liard River Basin
Bibliographic record
Abstract
Abstract. Like many high latitude areas, the mountainous region of subarctic Canada has experienced recent warming and is an area of large inter-annual temperature variations, most notably during the winter. Quantifying how climate tendencies affect streamflow, especially in the spring melt season, is critical not only to regional water resource management, but to understanding the influence of freshwater on the Arctic sea-ice cover and global climate system. The impact of projected atmospheric warming on the discharge of the Liard River is unclear. Here, uncertainty in climate projections associated with GCM structure (2 °C prescribed warming) and magnitude of increases in global mean air temperature (1 to 6 °C) on the river discharge are assessed using a well-tested, semi-distributed hydrological model. Analyses have shown that the hydrological impacts are highly dependant on the GCM scenario. Uncertainties between the GCM scenarios are driven by the inconsistencies in projected spatial variability and magnitude of precipitation, rather than warming temperatures. Despite these uncertainties, the entire scenario simulations project that the subarctic nival regime will be preserved in the future, but the magnitude of change in river discharge is highly uncertain. Generally, spring freshet will arrive earlier, autumn to spring discharge will increase whereas summer flow will decrease, leading to an overall increase in annual discharge.
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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 | 0.002 | 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 | 0.000 |
| 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".