A Thirty-Year Precipitation Record at Wolf Creek Research Basin, Yukon and the Importance of Bias-Corrected, Sub-Daily Measurements in a Changing Climate
Bibliographic record
Abstract
Precipitation (snow and rainfall) is an essential climate variable for hydrological modelling, flood forecasting, avalanche preparedness and assessing permafrost stability and ecological change. In data sparse regions, such as the Canadian Sub-Arctic, long-term sub-daily precipitation measurements are rare, yet imperative to understanding environmental feedback and the impact of extreme events. The Wolf Creek Research Basin (WCRB) in the southern Yukon, Canada, provides a unique long-term hydrological and climate record across forested, shrub and alpine ecozones. This study presents hourly precipitation recorded in WCRB since 1993 using a variety of instruments. The diversity in measurement techniques and range of monitoring elevations allows for thorough consideration of precipitation phase and lapse rate.We outline the challenges of maintaining and compiling in-situ, remote monitoring data spanning decades of change. This study facilitates discussion around best practices for cold-region precipitation data products by using transparent data filtering, correction and in-filling. We consider the efficacy and uncertainty of measurement techniques and bias correction methods for wind-induced losses at a site equipped with multiple concurrent instruments, shields and gauges. Our results explore spatiotemporal trends in the preliminary dataset and compare to available data in the southern Yukon. This work provides critical insights into the improvement and longevity of cold region, remote precipitation monitoring and the importance of long-term data sets in a changing climate.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 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".