The grain size of sediments delivered to steep debris‐flow prone channels prior to and following wildfire
Bibliographic record
Abstract
Abstract Debris flows are powered by sediment supplied from steep hillslopes where soils are often patchy and interrupted by bare‐bedrock cliffs. The role of patchy soils and cliffs in supplying sediment to channels remains unclear, particularly surrounding wildfire disturbances that heighten debris‐flow hazards by increasing sediment supply to channels. Here, we examine how variation in soil cover on hillslopes affects sediment sizes in channels surrounding the 2020 El Dorado wildfire, which burned debris‐flow prone slopes in the San Bernardino Mountains, California. We focus on six headwater catchments (<0.1 km 2 ) where hillslope sources ranged from a continuous soil mantle to 95% bare‐bedrock cliffs. At each site, we measured sediment grain size distributions at the same channel locations before and immediately following the wildfire. We compared results to a mixing model that accounts for three distinct hillslope sediment sources distinguished by local slope thresholds. We find that channel sediment in fully soil‐mantled catchments reflects hillslope soils ( D 50 = 0.1–0.2 cm) both before and after the wildfire. In steeper catchments with cliffs, channel sediment is consistently coarse prior to fire ( D 50 = 6–32 cm) and reflects bedrock fracture spacing, despite cliffs representing anywhere from 5% to 95% of the sediment source area. Following the fire, channel sediment size reduces most (5‐ to 20‐fold) in catchments where hillslope sources are predominantly soil covered but with patches of cliffs. The abrupt fining of channel sediment is thought to facilitate postfire debris‐flow initiation, and our results imply that this effect is greatest where bare‐bedrock cliffs are present but not dominant. A patchwork of bare‐bedrock cliffs is common in steeplands where hillslopes respond to channel incision by landsliding. We show how local slope thresholds applied to such terrain aid in estimating sediment supply conditions before two destructive debris flows that eventually nucleated in these study catchments in 2022.
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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".