Leveraging a time-series event separation method to untangle time-varying hydrologic controls influence on wildfire disturbed streamflow
Bibliographic record
Abstract
Abstract. Watershed disturbances can have broad, long-lasting impacts that result in a range of streamflow response. Increasing disturbance regimes, particularly from wildfire, is a growing concern for watershed management. The influence of watershed disturbances on rainfall-runoff patterns has proved challenging to isolate from undisturbed streamflow variability due to the role of hydrologic controls that vary through time, including water year type, seasonality, and antecedent precipitation. To better assess the influence of watershed disturbance on rainfall-runoff event patterns we developed the Rainfall-Runoff Event Detection and Identification (RREDI) toolkit. The RREDI toolkit is a novel time-series event separation method that automates the pairing and attribution of precipitation and streamflow events, leveraging and building on existing event separation methods. A rainfall-runoff event dataset of 5042 events was generated by the RREDI toolkit from a collection of nine western US study watersheds spanning a range of streamflow regimes, watershed properties, and burn characteristics. Through analyzing the rainfall-runoff event dataset, we found that water year type and season were significant controls on rainfall-runoff metrics. The significance of antecedent precipitation was variable between watersheds, indicating a more complex relationship for this control. The watershed-specific permutations of significant controls resulted in unique significant condition group trends in the rainfall storm depth and peak runoff relationship in two contrasting watersheds. In general, for each of the significant condition groups post-fire peak runoff was higher than undisturbed peak runoff except during winter in snow-dominated watersheds. Consideration of the time-varying hydrologic controls, particularly water year type and season, were identified as important when untangling the influence of wildfire on the rainfall-runoff patterns. The RREDI toolkit can be further applied to investigate the influence of other watershed disturbances and controls to increase understanding of rainfall-runoff patterns across the landscape.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".