Quantifying snow water storage from aerial LiDAR surveys in eight Pacific coastal watersheds, British Columbia, Canada
Bibliographic record
Abstract
While airborne Light Detection and Ranging (LiDAR) surveys are routinely used to measure snow volume in many types of mountain watersheds, those that are heavily forested and lie within maritime environments have been largely ignored to date. Here, we summarise our findings from a four-year study (2020-2023) of eight watersheds within the coastal rainforests of southwest British Columbia, which collectively represent an area of >330 km2. Aerial LiDAR surveys were conducted 3 to 5 times per year between March and June in order to measure snow depth across each watershed. Spatiotemporally-distributed snow density was estimated using a random forest model incorporating weather station data, LiDAR-derived metrics and in-situ snow density observations. At peak snow volume, we find typical mean catchment-wide snow water equivalent values of ~600-1200 mm, verified by a widespread field campaign consisting of > 25,000 in-situ measurements of snow depth and density. We show that, typically, ~60-90 % of the snow water volume is stored at mid-elevations of between 800 and 1500 m, where air temperatures are close to melting point and forest cover is prevalent, leaving the snowpack vulnerable to early seasonal melt onset and impacts due to forest management. We find that while peak measured snow volume typically represents ~20-40 % of surface runoff, providing an important buffer towards droughts within the region, snowmelt volumes can be insufficient to safeguard downstream water supply during extreme seasonal drought events. Overall, the results of this work provide valuable insights into the vulnerability of the snowpack in coastal maritime regions and the potential knock-on effects of a changing snowpack on regional water security.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".