How does wildfire and climate variability affect streamflow in forested catchments? A regional study in eastern Australia
Bibliographic record
Abstract
The impact of wildfire on catchment water yield remains uncertain, with case studies reporting a range of observed response paths. Additionally, the impacts of fire and climate variability are often intertwined, making them difficult to evaluate separately. This study assesses how wildfire and climate influence streamflow in forested catchments. We focused on selected forested catchments in eastern Australia that have experienced both sustained drought and large fire events, but otherwise have experienced little hydrological modification. We compared the relationship between streamflow and rainfall before and after the severe fire events in the 2019/2020 season and over a longer multi-year period. We obtained historical data for streamflow, rainfall, and wildfire extent, timing, and severity for each catchment. The study found a consistent increase in streamflow with given rainfall after the 2019/2020 fire event. However, the timing of the fire aligned with the end of a prolonged major drought that affected the region. Dry conditions decrease runoff while fire increases it, which makes it difficult to attribute the flow increase to fire alone. We also assessed the relative importance of multiple potential drivers (climatic and fire-related) of changes in streamflow over a longer, multi-year historical period. We found that the impacts of historical wildfires on streamflow are generally smaller than the impacts of hydro-climatic factors such as catchment storage, which has a relative importance for the streamflow over 3 times greater than that of the fire-related factors. Our results imply that historical changes in flow in the study region are more heavily affected by climate variability than by fires at the catchment scale. They emphasize the importance for water resources management of considering regional drivers such as changing climatic conditions over wildfire, which often affects only parts of individual catchments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".