Montane Seasonal and Elevational Precipitation Gradients in the Southern Rockies of Alberta, Canada
Bibliographic record
Abstract
ABSTRACT Modelling precipitation inputs in mountainous terrain is challenging for water resource managers given sparse monitoring sites and complex physical hydroclimatic processes. Government of Alberta weather station uncorrected and bias‐corrected precipitation datasets were used to examine elevational precipitation gradients (EPGs) and seasonality of EPGs for six South‐Saskatchewan River headwater sites (alpine, sub‐alpine, valley). January EPG from valley to alpine sites (730 m elevation difference) using uncorrected precipitation was 19 mm/100 m. Corrected EPG was approximately three times greater (61 mm/100 m). The valley received more precipitation than the alpine (inverse EPG) in late spring and summer. A seasonal signal was present whereby all sites demonstrated 50%–70% lower summertime precipitation relative to winter months, with the greatest seasonal variance at the alpine site. Winter watershed‐level spatialized precipitation volume was compared to modelled snow water equivalent (SWE) associated with two late‐winter airborne lidar surveys. Uncorrected volumes (2020: 64.0 × 106m3, 2021: 63.2 × 106m3) were slightly higher than modelled mean SWE (2020: 51.6 × 106m3, 2021: 44.2 × 106m3) whereas bias‐corrected (2020: 120.5 × 106m3, 2021: 119.7 × 106m3) almost doubled the estimate. Corrected precipitation is assumed closer to the true value. Cumulative sublimation, evaporation and snowmelt losses result in ground‐level snowpack yield that deviates from total atmospheric precipitation in an increasingly negative manner. The 2020/2021 simulations suggest wintertime atmospheric precipitation exceeds late‐winter snowpack accumulation by up to 57% and 63%, respectively. A loss of 16 × 106m3 (7%) watershed SWE from the alpine zone was partially attributed to redistribution downslope to the treeline‐ecotone. Physical snowpack losses from sublimation and melt, or modelling uncertainty due to precipitation correction and alpine snow‐density uncertainties can also contribute to observed discrepancies between in situ SWE and cumulative precipitation. Ignoring bias‐correction in headwater precipitation estimates can greatly impact headwater precipitation volume estimates and ignoring EPG seasonality is likely to result in under‐estimated winter and over‐estimated summer yields.
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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 | 0.003 |
| 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) | 0.002 | 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".