An analysis of 100 years of post-fire streamflow responses of British Columbia watersheds
Bibliographic record
Abstract
Wildfires are becoming larger and more severe due to climate change. This trend affects the forest ecosystem and disrupts many eco-hydrologic processes in forested watersheds. Effects can include rapid runoff responses, increased surface runoff, and elevated erosion, leading to lower water quality and long-lasting effects on hydrologic ecosystem services (drinking water supply or flood regulation). However, post-fire hydrology studies often have variable and contrasting results, making cross-study comparisons difficult. Studies are typically short-term and focused on single wildfire events. Additionally, hydrologic ecosystem services are not always considered. This research has two objectives: to determine accurate indicators for post-fire flow responses; and to develop a flood risk map that considers wildfire history and the hydrologic ecosystem services. The study area includes 336 drainage basins (grouped into five ecozones) in British Columbia, Canada, known for its susceptibility to wildfires and floods. The study analyzes 110 years of wildfire data from 1910 to 2020. Of the 824 wildfires in the study period, over 400 fires were identified with five years of continuous streamflow and precipitation daily flow records. Percent changes in low, high, and peak flows were calculated using pre-fire and post-fire values. Using streamflow, precipitation, wildfire perimeters, land cover and topographic data, statistical analyses were done to determine the most influential watershed characteristic in post-fire streamflow responses. To develop the flood risk map, the same data will be combined with socio-economic and demographic data. Preliminary results suggest differing trends for low, high, and peak flows for the five ecozones in BC, demonstrating the importance of geophysical variables on streamflow response. Results will aid in understanding the effects of climate change over 110 years, specifically the wildfire effects on hydrology in forested watersheds and on the hydrologic ecosystem services provided to nearby communities. The determination of accurate post-fire streamflow indicators will also help water resource managers, urban planners, and other decision-makers allocate resources appropriately for long-term water management and reduce post-fire flood vulnerability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".