Wildfire increased summer low flows in snow-dominated watersheds: A combined approach of hydrometric monitoring and geochemical tracing
Bibliographic record
Abstract
Forests are experiencing more frequent and intense wildfires in Canada, which pose considerable threats to water quantity and quality, particularly during the summer low-flow period when water demand is high. While the impacts of wildfire on hydrology have been widely assessed at the watershed scale, the underlying mechanisms of the responses of summer low flows remain poorly understood. In this study, we employed an integrated research framework that combines hydrometric monitoring with geochemical tracing to evaluate how the 2021 White Rock Lake Wildfire affected summer low flows, and to identify the underlying mechanisms governing these responses in the Okanagan Valley, British Columbia (BC), Canada. We found that (1) summer low flows, represented by Q 90 (flows exceeded at 90% of the time in summer) significantly increased following the wildfire ( p < 0.05); (2) summer low flows were primarily regulated by snow water in early summer (July), while dominated by groundwater in late summer (August and September); and (3) enhanced snow water contribution and reduced evapotranspiration (ET) were two primary contributors to the increased summer low flows. Our results provide insights for developing sustainable water management strategies for the region in the context of climate change and increasing forest disturbance. This study also demonstrates that the combination of hydrometric monitoring and geochemical tracing is an effective approach towards uncovering mechanisms that drive low-flow responses. • Hydrometric monitoring and geochemical tracing was used to investigate summer low-flow responses and the mechanisms. • Wildfire significantly increased summer low flows (Q 90 ). • Snow water primarily supplied early-summer low flows, while groundwater dominated the late summer low flows. • Enhanced snow water contribution and reduced evapotranspiration were two drivers of enhanced post-fire summer low flows.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".