Are the effects of vegetation and soil changes as important as climatechange impacts on hydrological processes?
Bibliographic record
Abstract
Abstract. Hydrological processes are widely understood to be sensitive to changes in climate, but the effects of changes in vegetation and soils have seldom been considered. The response of mountain hydrology to future climate and vegetation/soil changes is modelled in three snowmelt dominated mountain basins in the North American Cordillera. A Cold Regions Hydrological Model developed for each basin was driven with perturbed observed meteorological time series. Monthly perturbations were developed from differences in eleven regional climate model outputs between the present and future scenarios. Future climate change in these basins results in decreased modelled peak snow water equivalent (SWE) but increased evapotranspiration in all basins. All three watersheds became more rainfall-dominated. In Wolf Creek in the Yukon Territory, an insignificant increasing effect of vegetation change on peak SWE was found to be important enough to offset the significant climate change effect on alpine snow. In Marmot Creek in the Canadian Rockies, a combined effect of soil and climate changes on increasing annual runoff becomes significant while their individual effects are not statistically significant. In the relatively warmer Reynolds Mountain East catchment in Idaho, USA, only vegetation change decreases annual runoff volume and changes in soil, climate, or combination of them do not affect runoff. At high elevations in Wolf and Marmot Creeks, modelled vegetation/soil changes moderated the impact of climate change on peak SWE, the timing of peak SWE, evapotranspiration, and annual runoff volume. At medium elevations, these changes intensified the impact of climate change, decreasing peak SWE, and sublimation. The modelled hydrological impacts of changes in climate, vegetation, and soil in mountain environments are similar in magnitude but not consistently in the direction in all biomes; in some combinations, this results in enhanced impacts at lower elevations and latitudes and offsetting effects at higher elevations and latitudes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".