FIRES AND FLOODS: A YEAR OF CLIMATE CHANGE DRIVEN DISASTERS IN BRITISH COLUMBIA, CANADA
Bibliographic record
Abstract
In June 2021, British Columbia (BC), Canada experienced historic heat waves that generated extreme fire hazard and fire activity across most of the province. The resultant fire season was the third largest on record in BC with 1,600 fires and nearly 9000 km2 burned. These fires conditioned the landscape for post-wildfire debris flows. One of the largest wildfires was the Lytton Creek Fire, which rapidly destroyed most of the community of Lytton (traditionally named Kumsheen). In August and September 2021, post-wildfire debris flows were triggered by convective rainfall in the Lytton Creek Fire and blocked highways and railways along the Thompson River valley. Following the hot and dry summer, BC transitioned into wetter-than-normal fall conditions attributable to La Niña and negative Pacific Decadal Oscillation phases. Between September and mid-November 2021, five atmospheric rivers (ARs) landed in Southern BC, resulting in high antecedent soil moisture conditions. Between November 14 to 16, 2021, a locally extreme AR struck Southern BC. It caused widespread flooding, landslides, and (post-wildfire) debris flows and debris floods. Railway and highway infrastructure was severed because of bank erosion and culvert washouts. These damages resulted in access loss to several communities, interrupted emergency response, and a completely disrupted all transportation corridors between Western Canada and Vancouver, BC for nearly a week. Since the early 2000s, BC has experienced larger and more intense wildfires and more frequent post-wildfire debris flows. Current post-wildfire debris flow assessments are labour-intensive and are not suited to provide relevant information quickly when numerous fires are burning in areas with important infrastructures. Assessments developed in other fire-prone areas (e.g., Southern California) systematically overestimate post-wildfire debris-flow volumes when applied to BC. We evaluate the use of ranked burn severity and watershed morphometry characteristics (Melton ratio, watershed length) to rapidly assess large fire areas. This rapid assessment procedure can be deployed to prioritize detailed assessments for communities and infrastructure.
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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 | 0.000 | 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 | 0.001 |
| 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".