Forest fire effects on stream water quality at continental scales: a meta-analysis
Bibliographic record
Abstract
Abstract Forested watersheds supply over two thirds of the world’s drinking water. The last decade has seen an increase in the frequency and intensity of wildfires that is threatening these source watersheds, and necessitating more expensive water treatment to address degrading water quality. Given increasing wildfire frequency in a changing climate, it is important to understand the magnitude of water quality impacts following fire. Here, we conducted a meta-analysis to explore post-fire changes in the concentrations of nitrogen (N) and phosphorus (P) species, dissolved organic carbon, and total suspended sediments in 121 sites around the world. Changes were documented over each study’s respective duration, which for 90% of sites was five years or fewer. We find concurrent increases in C, N and P species, highlighting a tight coupling between biogeochemical cycles in post-fire landscapes. We find that fire alters N and P speciation, with median increases of 40%–60% in the proportion of soluble inorganic N and P relative to total N and P. We also found that fire decreases C:N and C:P ratios, with median decreases ranging from 60% to 70%. Finally we observe a ‘hockey stick’-like response in changes to the concentration distribution, where increases in the highest concentration ranges are much greater than increases at lower concentrations. Our study documents strong heterogeneity in responses of water quality to wildfire that have been unreported so far in the literature.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.021 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".