Exploring the Complex Effects of Wildfire on Stream Water Chemistry: Insights From Concentration‐Discharge Relationships
Bibliographic record
Abstract
Abstract Wildfires are a worldwide disturbance with unclear implications for stream water quality. We examined stream water chemistry responses immediately (<1 month) following a wildfire by measuring over 40 constituents in four gauged coastal watersheds that burned at low to moderate severity. Three of the four watersheds also had pre‐fire concentration‐discharge data for 14 constituents: suspended sediment (SSfine), dissolved organic and inorganic carbon (DOC, DIC), specific UV absorbance (SUVA), major ions (Ca2+, K+, Mg2+, Na+, Cl−, , , F−), and select trace elements (total dissolved Mn, Fe). In all watersheds, post‐fire stream water concentrations of SSfine, DOC, Ca2+, Cl−, and changed when compared to pre‐fire data. Post‐fire changes in , K+, Na+, Mg2+, DIC, SUVA, and total dissolved Fe were also found for at least two of the three streams. For constituents with detectable responses to wildfire, post‐fire changes in the slopes of concentration‐discharge relationships commonly resulted in stronger enrichment trends or weaker dilution trends, suggesting that new contributing sources were surficial or near the surface. However, a few geogenic solutes, Ca2+, Mg2+, and DIC, displayed stronger dilution trends at nearly all sites post‐fire. Moreover, fire‐induced constituent concentration changes were highly discharge and site‐dependent. These similarities and differences in across‐site stream water chemistry responses to wildfire emphasize the need for a deeper understanding of landscape‐scale changes to solute sources and pathways. Our findings also highlight the importance of being explicit about reference points for both stream discharge and pre‐fire stream water chemistry in post‐fire assessment of concentration changes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".