Hydrologic impact of aspen harvesting within the subhumid Boreal Plains of Alberta
Bibliographic record
Abstract
Abstract This study examined the hydrological impact of harvesting aspen ( Populus tremuloides ) within the subhumid Boreal Plains of Alberta, Canada, following clear‐cutting of two stands in successive winters within a small catchment situated in glacial moraine. Impacts were evaluated using a combination of observational data collected from within the harvested catchment and an adjacent reference catchment and numerical simulations conducted with HydroGeoSphere. Sensitivity simulations evaluated the influence of post‐harvest evapotranspiration rates and variability in atmospheric conditions on the range of hydrologic response. Study results indicate that aspen harvesting had limited impact on groundwater levels and streamflows within these hydrologic systems because of the subhumid climate with low frequency of large storms, large soil‐moisture storage capacity of heterogeneous glacial materials, and high evapotranspiration rates of regenerating aspen. Despite an estimated increase in hillslope groundwater levels of up to 3 m, pond and peatland water levels increased by less than 0.3 m and were accompanied by increased streamflows of less than 10 mm. However, predicted increases in groundwater levels and streamflows were sensitive to regenerating aspen evapotranspiration rates, which can be enhanced by appropriate harvesting techniques but may be reduced by climate change. These results are consistent with previous results from within the Boreal Plains but differ from aspen harvesting studies conducted in other settings where appreciable increases in streamflows have been reported. This disparity highlights the need to consider the integrated response of the hydrologic system when evaluating impacts from disturbance and making comparisons between settings.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".