The sensitivity of North American mountain basin snow hydrology to changes in air temperature and precipitation
Bibliographic record
Abstract
The hydrological sensitivity of snowmelt-dominated, high mountain headwaters to climate change was investigated using a physically based model to diagnose snow processes and headwater basin runoff response to perturbations of the current climate in three well-instrumented mountain research basins spanning the northern North American Cordillera. High-resolution hourly meteorological observations were perturbed using air temperature increases and precipitation changes and then used to force comprehensive, mountain hydrological models created using the modular, process-based Cold Regions Hydrological Modelling Platform (CRHM) for each basin. Simulations using multiple elevations show that both peak snowpack and annual runoff respond to warming and precipitation changes and these responses vary with latitude. In all three basins, the timing and magnitude of peak snowpack were sensitive to changes in temperature and precipitation, but timing was most sensitive to temperature. Annual runoff was far less sensitive to temperature than the snow regime. The impacts of the range of warming expected from North American climate model simulations on annual runoff, but not peak snowpack, can be offset by the size of precipitation increases projected for the future period 2041-2070. To offset the impact of 2°C warming on annual runoff, precipitation would need to increase by less than 5% in all three basins. To offset the impact of 2°C warming on peak snowpack, however, precipitation would need to increase by 12% in Wolf Creek-Yukon Territory, 18% in Marmot Creek-Canadian Rockies and an amount greater than the maximum projected at Reynolds Mountain-Idaho. The role of increased precipitation as a compensator for the impact of warming on mountain snow hydrology is more effective at the high elevations and high latitudes. Increased precipitation leads to resilient and strongly coupled snow and hydrological regimes in cold regions and sensitive and weakly coupled regimes in the low elevations and temperate climate zones.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".