Sensitivity of snowmelt hydrology in Marmot Creek, Alberta, to forest cover disturbance
Bibliographic record
Abstract
Abstract A model including slope effects on snow redistribution, interception and energetics was developed using the Cold Regions Hydrological Model platform, parameterized with minimal calibration and manipulated to simulate the impacts of forest disturbance on mountain hydrology. A total of 40 forest disturbance scenarios were compared with the current land cover for four simulation years. Disturbance scenarios ranged from the impact of pine beetle kill of lodgepole pine to clear‐cutting of north‐ or south‐facing slopes, forest fire and salvage logging. Pine beetle impacts were small in all cases with increases in snowmelt volume of less than 10% and streamflow volume of less than 2%. This small impact is attributed to the low and relatively dry elevations of lodgepole pine forests in the basin. Forest disturbances due to fire and clear‐cutting affected much larger areas and higher elevations of the basin and were generally more than twice as effective as pine beetle in increasing snowmelt or streamflow. For complete forest cover removal by burning and salvage logging, a 45% increase in snowmelt volume was simulated; however, this only translated into a 5% increase in spring and summer streamflow volume. Forest burning with the retention of standing burned trunks was the most effective forest cover treatment for increasing streamflow (up to 8%) because of its minimizing of winter snow sublimation losses from interception and blowing snow. However, increases in streamflow volumes were almost entirely due to reductions in intercepted snow sublimation with decreasing canopy coverage. Peak daily streamflow discharges responded more strongly to forest cover disturbance than did seasonal streamflow volumes, with increases of almost 25% in peak streamflow from the removal of forest canopy by fire and the retention of standing burned trunks. Peak flow was most effectively increased by forest removal on south‐facing slopes and level sites. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".