Assessing the Potential Impacts of Climate Change on Mountain Snowpack in the St. Mary River Watershed, Montana
Bibliographic record
Abstract
Abstract The St. Mary River watershed is an important international watershed that supplies irrigation water to large portions of southern Alberta, Canada, and northern Montana. The St. Mary River is fully allocated and users on both sides of the border are concerned regarding declining water supplies and increasing water demands under climate warming. Water supply in the St. Mary River is largely from snowpack in the mountainous portion of the watershed. This work assesses potential future changes in snowpack for the St. Mary River watershed under a range of general circulation model (GCM) derived future climate scenarios. The Generate Earth Systems Science (GENESYS) input spatial hydrometeorological model is used to simulate potential changes in spring snowpack, the onset of melt, and changes in snow extent for three 30-yr periods centered around 2025, 2055, and 2085. Results suggest an earlier spring and associated earlier onset of snowmelt and probable declines in maximum annual snow water equivalent (SWE) over the St. Mary River watershed are likely under most future climate scenarios used in this study. However, results are responsive to future climate scenarios, where a scenario with substantial global greenhouse gas (GHG) emission controls shows a much lower decline in total accumulated SWE over the St. Mary River watershed. Without substantial GHG emission reductions, the study does show that there could be significant changes in snowpack over the St. Mary River watershed in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".