Forest harvesting and hydrology in boreal Forests: Under an increased and cumulative disturbance context
Bibliographic record
Abstract
Boreal forests cover about one-third of the global forested area and are under rapid alteration due to increased natural and human-induced forest disturbance, which have important impacts on forest carbon cycling, hydrology, biodiversity, and many other ecological characteristics, processes, and functions. In this review, we focus on how forest harvesting affects hydrological processes in boreal forests within the context of increased and cumulative forest disturbance across various spatial scales. At the stand level, harvesting affects snow processes (i.e., snow interception, snow water equivalent, ablation, and snowmelt), decreases evapotranspiration (ET) and water use efficiency (WUE), and has negative impacts on soil dynamics (i.e., infiltration and soil moisture). These hydrological changes at the stand level can be counteractive or additive, cumulatively leading to more varied effects at larger spatial scales. In small watersheds, spring freshets (or high flows) are consistently increased following harvesting, while annual streamflow is often increased but some contradictory results are found in Siberia, Russia. These varied responses are likely dependent upon differences in energy budgets, climate, post-disturbance vegetation trajectories, and their dynamic interactions over space and time. For larger watersheds and regions, cumulative forest disturbance interacts with climate, leading to more complicated and varied hydrological responses. Forest management implications and future research topics are also suggested.
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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 | 0.001 |
| 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".