A model of rapid preferential hillslope runoff contributions to peak flow generation in a temperate rain forest watershed
Bibliographic record
Abstract
A model for the 10 km 2 Carnation Creek watershed on Vancouver Island, British Columbia, is used to assess preferential hillslope runoff contributions to peak flow generation. The model combines the matrix flow algorithm of the distributed hydrology soil vegetation model with a Green‐Ampt formulation for calculating matrix and by‐pass infiltration, preferential hillslope runoff initiation controlled by rainfall depth, and downslope subsurface flow rates prescribed based on at‐site tracer tests. Model evaluation using 1972–1990 hydrometeorological data reveals that this formulation is successful in simulating subannual and larger peak flows. Model results suggest that preferential flow contributions to streamflow generation become greater than matrix flow contributions for unit area discharge values in excess of 2.8 mm/hr, corresponding to a peak flow return period of 2–3 months. This transition from matrix flow dominated runoff to preferential flow dominated runoff is consistent with an observed upper limit of groundwater response to precipitation for return periods in excess of 2 months. A break in slope in peak flow frequency curves at a return period of about 20 months appears to correspond to a change in storm characteristics. Thus at least three physically distinct populations of peak flows may exist at Carnation Creek. The ability of the model to simulate peak flows and groundwater responses for small and large storms suggests that it may be useful for addressing runoff process considerations in the debate whether forest management effects for annual and larger peak flows are similar to those inferred from analyses dominated by subannual peak flows.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".