Bibliographic record
Abstract
Abstract. Uncertainty in estimating precipitation in mountain headwaters can be transmitted to estimates of river discharge far downstream. Quantifying and reducing this uncertainty is needed to better constrain the uncertainty of hydrological predictions in rivers with mountain headwaters. Spatial estimation of precipitation fields can be accomplished through interpolation of snowfall and rainfall observations, these are often sparse in mountains and so gauge density strongly affects precipitation uncertainty. Elevational lapse rates also influence uncertainty as they can vary widely between events and observations are rarely at multiple proximal elevations. Therefore, the spatial, temporal, and elevational domains need to be considered to quantify precipitation gauge network uncertainty. This study aims to quantify the spatiotemporal and elevational uncertainty in the spatial precipitation interpolated from gauged networks in the snowfall-dominated, triple continental divide, Canadian Rockies headwaters of the Mackenzie, Nelson, Columbia, Fraser and Mississippi rivers of British Columbia and Alberta, Canada and Montana, USA. A 30-year (1991–2020) daily precipitation database was created in the region and utilized to generate spatial precipitation and uncertainty fields utilizing kriging interpolation and lapse rates. Results indicate that gauge network coverage improved after the drought of 2001–2002, but it was still insufficient to decrease domain-scale uncertainty, because most gauges were deployed in valley bottoms. It was identified that deploying gauges above 2000 m will have the greatest cost-effective benefits for decreasing uncertainty in the region. High-elevation gauge deployments associated with university research and other programs after 2005 had a widespread impact on reducing uncertainty. The greatest uncertainty in the recent period remains in the Nelson headwaters, whilst the least is in the Mississippi headwaters. These findings show that both spatiotemporal and elevational components of precipitation uncertainty need to be quantified to estimate uncertainty for use in precipitation network design in mountain headwaters. Understanding and then reducing these uncertainties through additional precipitation gauges is crucial for more reliable prediction of river discharge.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.026 | 0.024 |
| Insufficient payload (model declined to judge) | 0.127 | 0.098 |
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".