Modelling snowpacks in topographically complex terrain during the winter accumulation season, Columbia River Basin, British Columbia, Canada
Bibliographic record
Abstract
This dissertation investigates winter accumulation and snow cover change in the Columbia Mountains of British Columbia. In chapter 1, I start with an introduction that describes the study area, and then outlines the objectives and structure of this dissertation. In chapter 2, I examine the performance of two snow evolution models with different complexities (SnowModel and Alpine3D) at simulating winter glacier mass balance on four individual glaciers using two different forcing datasets, the Weather Research and Forecasting model (WRF) outputs and the North American Land Data Assimilation System (NLDAS). My results show that both models can simulate winter accumulation with less than 20% bias for each glacier, with SnowModel forced by WRF yielding the least overall bias. In chapter 3, I study the effect of wind on snow patterns to determine the impact of snow redistribution by wind in terms of erosion, deposition, and sublimation on winter mass balance estimation. The results demonstrate that modelled redistribution of snow by wind produces a visually realistic pattern of snow accumulation when compared to observed snow depth, but its impact on the glacier-averaged winter mass balance estimation is negligible (< 4%). The results also suggest that drifting snow sublimation is highly time and space dependent. Considering the model performance from previous chapters, in chapter 4, I analyzed the future snow cover change over the upper Columbia Basin under the Representative Concentration Pathway (RCP8.5) climate scenario by the end of the 21st century. I used downscaled climate projections of the Community Earth System Model (CESM1) by WRF, along with statistically downscaled data provided from the Pacific Climate Impacts Consortium (PCIC) to force SnowModel. The simulated snow maps represent a higher dynamically downscaled mean snow water equivalent (SWE) reduction – reaching up to 30% by the end of the century - than the statistically downscaled SWE reduction. While SWE reduction of more than 60% happens at lower and mid-elevations, altitudes higher than 2000 m are less vulnerable to climate change. I conclude this dissertation (Chapter 5) with a summary of the progress gained, study limitations, suggestions for future research, and research implications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".