Effects of forest harvesting on snowmelt during rainfall in coastal British Columbia
Bibliographic record
Abstract
Rain-on-snow has been recognized as an event with potential for increasing flood and debris torrent hazards. However the effects of deforestation on increasing this hazard are not well understood. To better understand this phenomena a study was conducted in the Jamieson Creek experimental watershed near Vancouver B.C. Its primary objective was to determine the effects of forest harvesting on snow melt rates and subsequent runoff during rain-on-snow events. The energy balance of a snowcover and the theory of snowmelt are reviewed to better understand the processes involved during rain-on-snow. Techniques and instrumentation required to compute the energy budget are discussed, with the aerodynamic technique receiving greater attention. Also reviewed are the U.S. Army Corps of Engineers (1956) snowmelt equations and their applicability for rain-on-snow situations. The experimental set-up consisted of two study plots, one located in a recent cutover and a second in an adjacent Coastal Western Hemlock old-growth forest. Each plot was equipped with a large (22m[sup 2]) plastic sheet lysimeter recording through a tipping bucket arrangement, allowing comparison of snowmelt and runoff rates between sites. Direct measurement of snowmelt was also achieved using snow survey techniques. The USACE(1956) snowmelt equations were verified by comparing the computed melt with the lysimeter and snow survey results. Wind speed, relative humidity and air temperatures were measured at 0.6 and 1.5 meters above the snowpack to evaluate latent and sensible heat fluxes. Snowpack and ground heat exchanges were measured with a profile of five thermistors, and radiation was monitored with net all-wave radiometers. Three winters of data were collected (1981-82, 1982-83, 1983-84), with several storms being analysed from each of the last two years. Peak runoff intensities and total runoff amounts, during rain-on-snow were found to be greater at the forest site when there was presence of snow in the canopy. When there was no snow in the canopy runoff was always greater at the open site. The reasons for the greater runoff at either of the sites are discussed. The use of the USACE (1956) snowmelt equations generally compared favorably with the snow survey and lysimeter data, at the open site. However under certain specific conditions these snowmelt equations were shown to be inadequate for use at the forest site. The role of the forest canopy on snow and rainfall interception played a major role in explaining the differences in snowmelt and runoff rates encountered between the two sites.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".