Assessing Effects of Climate Variability and Forest Disturbance on Annual Streamflow of the Stellako Watershed, Canada
Bibliographic record
Abstract
Climate variability and vegetation disturbance as critical driving factors significantly affect the regional hydrology in forested watersheds. Yet, due to landscape heterogeneities such as topography, soil characteristics, climate conditions, and vegetation types, hydrological responses to climate and forest changes and associated mechanisms have not been fully understood. The forested Stellako watershed, a typical forest-disturbed area in Canada, has been studied to assess the annual streamflow affected by climate variability and forest disturbance. The study period was from 1951 to 2018. The methods include the modified double mass curve (MDMC), Autoregressive Integrated Moving Average (ARIMA) intervention, and multivariate ARIMA. In the Stellako watershed, 1980 was identified as a breakpoint in the yearly time series between 1951 and 2018. The 1951-1979 period was considered the reference. From 1980 to 2018, the streamflow decreased by 14.69 and 26.18 mm due to climate and forest changes, respectively. The annual runoff variation was mainly attributed to forest disturbances contributing 60.78% of the total variations. Thus, a timely assessment of the runoff variety at a watershed scale has been obtained. The developed methodology can be applied to quantify the disturbance effects at a watershed or regional scale and develop watershed and forest management strategies under future climate and forest changes.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".