River morphodynamic response in a watershed affected by megafires (Deadman River, British Columbia, Canada)
Bibliographic record
Abstract
Wildfires can have profound impacts on the movement of water and sediment along watersheds due primarily to changes in the mechanical and hydrologic properties of vegetation and soil. Previous studies have documented the damaging dynamics of local post-wildfire landslides, debris floods and flows, though little attention has been paid to the large watershed-scale fluvial responses. To address this knowledge gap, this study focuses on the Deadman River watershed, which was severely affected by the Elephant Hill and Sparks Lake fires of, respectively, 2017 and 2021 in the southern interior of British Columbia, Canada. We included results from field-data collection, multispectral remote sensing, and timelapse photogrammetry, to examine how fire-related changes in hydrology and sediment flux have influenced channel morphology and meander migration. The timelapse imagery analysis indicates a broad, watershed-wide increase in channel width and meander migration rate over the observed timespan (2002–2023), with a sharp increase in these metrics (up to 100% and 540%, respectively) in the aftermath of the 2021 fire. Integration of sub-catchment-scale burn severity analysis reveals that regions that burned more severely drained into reaches that migrated laterally at faster rates. We observed a dependency of channel migration rate on integration timescale that was primarily attributed to channel-migration reversals and chute cutoffs. We finally conducted a normalization of migration rate to demonstrate that the observed post-fire morphodynamic changes are not an artifact of integration timescale – a conclusion corroborated by the relationships with channel width, which is not sensitive to integration timescale. These results provide a quantification of the degree to which wildfires can alter the morphodynamics of meandering rivers and their catchments. We anticipate our study to inform natural hazard mitigation efforts as well as longer-term ecosystem restoration and recovery efforts in area affected by megafires.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".