Leveraging a time-series event separation method to disentangle time-varying hydrologic controls on streamflow – application to wildfire-affected catchments
Bibliographic record
Abstract
Increasing watershed disturbance regimes, such as from wildfire, are a growing concern for natural resource managers. However, the influence of watershed disturbances on event-scale rainfall–runoff patterns has proved challenging to disentangle from other hydrologic controls. To better isolate watershed disturbance effects, this study evaluates the influence of several time-varying hydrologic controls on event-scale rainfall–runoff patterns, including water year type, seasonality, and antecedent precipitation. To accomplish this, we developed the Rainfall–Runoff Event Detection and Identification (RREDI) toolkit, an automated time-series event separation and attribution algorithm that overcomes several limitations of existing techniques. The RREDI toolkit was used to generate a dataset of 5042 rainfall–runoff events from nine western US watersheds. By analyzing this large dataset, water year type and season were identified as significant controls on rainfall–runoff patterns, whereas antecedent moisture was pinpointed as a limited control. Specific effects of wildfire disturbance on runoff response were then demonstrated for two burned watersheds by first grouping rainfall–runoff events based on identified hydrologic controls, such as wet versus dry water year types. The role of water year type and season should be considered in future hydrologic analysis to better isolate the increasing and changing effects of wildfires on streamflow. The RREDI toolkit could be readily applied to investigate the influence of other hydrologic controls and watershed disturbances on rainfall–runoff patterns.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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 | 0.000 |
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".