Changes in snow‐dominated streamflow quantity and timing following an extensive wildfire in British Columbia
Bibliographic record
Abstract
Abstract The length and frequency of extreme fire weather has increased across the globe in recent decades, with potential deleterious consequences to streamflow quantity, timing and quality. Changes in the hydrologic regime following wildfire can have substantial downstream consequences, affecting communities and ecosystems through flooding, erosion, loss of habitat and degraded water quality. While there are many studies that address post‐wildfire hydrology across the globe, there are few studies in the snow‐dominated regions. The 2017 Elephant Hill wildfire in south‐central BC burned across or adjacent to four watersheds with long‐term streamflow gauges providing a rare opportunity to evaluate hydrologic change. Several approaches were used to identify patterns of change following the wildfire, all of which suggest increased post‐fire flows. The before‐after‐control‐impact design showed significant increases in annual, spring and summer water yield from the small (49 km 2 ) Arrowstone Creek watershed (30%, 21% and 86%, respectively). Significant increases in spring water yield were observed in the larger (5318 km 2 ) Bonaparte River watershed (48%). Annual and summer water yield increased in the Bonaparte River (31% and 58%, respectively) but these changes were not statistically significant. In both the Bonaparte River and Arrowstone Creek, the onset of spring freshet (26 days earlier in both) was significantly advanced, however, the timing of maximum snowmelt discharge was significantly advanced (27 days earlier) only in Arrowstone Creek. Smaller changes were also observed in the reference watersheds; however, these were not statistically significant. The difference in results between the small and large watershed, as well as the effects of weather and watershed attributes, highlight the need for continued research into the relationships between wildfire and hydrologic regime across diverse landscapes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".