Negligible soil erosion in a burned mountain watershed, Canadian Rockies: field and modelling investigations considering the role of duff
Bibliographic record
Abstract
ABSTRACT Increased soil erosion in immediate post‐wildfire years has been well documented in the literature, but many unanswered questions remain about the factors controlling erosional responses in different regional settings. The field site for the present study was located in a closed canopy, subalpine forest in Kootenay National Park, British Columbia that was subjected to a high‐intensity crown fire in the summer of 2003. Low soil erosion values were documented at the study site in the years immediately following the 2003 wildfire, with estimates ranging from approximately 10 ‐1 up to 10 0 t ha ‐1 . Following the wildfire, notable duff coverage (the duff layer is the combined fermentation and humus soil organic layers) remained above the mineral soil. This finding supports earlier studies documenting only partial duff consumption by high‐intensity wildfires in the boreal forest of Canada. It is postulated that remnant duff coverage after many high‐intensity wildfires impacts the hydrological and soil erosional response to rainstorm events in post‐wildfire years. In particular, duff provides detention storage for infiltrating rainfall and, therefore, may inhibit the generation of overland flow. Furthermore, duff also provides a physical barrier to soil erosion. The Green–Ampt model of rainfall infiltration is employed to better assess how interactions between rainfall duration/intensity and soil/duff properties affect hydrological response and the generation of overland flow. Model results show that duff provides an effective zone for detention storage and that duff accommodates all rainfall intensities to which it was subjected without the occurrence of surface ponding. In addition, the penetration of the wetting front is relatively slow in duff due to its high porosity and water storage potential. Copyright © 2011 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".