The role of wildfires and forest harvesting on geohazards and channel instability during the November 2021 atmospheric river in southwestern British Columbia, Canada
Bibliographic record
Abstract
Abstract Sediment mobilized to rivers during extreme flood events can influence channel stability and cause significant morphological changes. A prolonged and intense atmospheric river (AR) struck southwestern British Columbia, Canada in November 2021, leading to extreme flooding and landsliding over approximately 70 000 km2 of mountainous areas. Entire communities within the region were evacuated, and the transportation infrastructure connecting them was severely damaged. The locations of 1300+ geohazards (e.g., debris flows, debris flood, debris slides, shallow landslides and bank erosion) were mapped from helicopter, ground observations, orthoimagery, site photos and social media posts alongside rivers and large gravel‐bed streams that experienced lateral instability. Morphological changes in two of these gravel‐bed rivers were examined in more detail by comparing pre‐event and post‐event lidar data using three‐dimensional point‐based normal differencing. We found that geohazards occurred more frequently in burned areas and along forest harvesting resource roads, providing point sources of sediment that entered mainstem rivers. The geohazard mapping and lidar change detection revealed that bank erosion and lateral instability often occurred downstream of these mapped sediment sources. As the frequency of wildfires and extreme meteorological events is predicted to increase with continued climate change, future risk assessments in communities should consider sediment sources that can be mobilized by these events and the resulting downstream morphological impacts.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".