Change detection of storm runoff and sediment yield using hydrologic models following wildfire in a coastal redwood forest, California
Bibliographic record
Abstract
This study attempted to detect changes in stormflow volumes, peakflows, and sediment loads using hydrologic models within the context of an uncertainty assessment following wildfire. In 2009, after 8 years of study, the Lockheed Fire burned the treatment and control watersheds of a paired watershed study in coastal California, USA, eliminating the ability to continue a paired watershed before–after control–intervention (BACI) study design. An alternative analysis was used to detect stormflow and sediment load changes due to the wildfire by comparing measured posttreatment stormflow and sediment load with simulated predisturbance responses predicted with the hydrologic models HBV-EC and DHSVM. High natural variability of stormflow and sediment measurements compounded with uncertainty associated with the hydrologic models and climate suggest that only large changes can be detected. The fire and subsequent salvage harvest created an approximately 9%–12% reduction in forest overstory canopy and a 70%–90% consumption of understory vegetation. No discernible changes in slopes of regression lines were detected between predisturbance and postfire stormflow volumes, peakflows, or sediment loads. No changes were detected in stormflow volume, peakflow, or sediment loads comparing pre- and post-fire vegetation inputs to the hydrologic model DHSVM. The lack of detected change in streamflow to accelerate stream channel erosion combined with low to moderate fire severity adjacent to stream channels most likely were the reasons for no detected postfire change to sediment loads.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".