Yukon River Basin Streamflow Forecasting System - Vector-Based MESH Model Setup for Yukon River Basin
Bibliographic record
Abstract
The Yukon River Basin the second largest river in the Arctic region of North America and is shared between Canada and the US. The Canadian part covers almost half of the Yukon Territory in addition to a small portion of the province of British Columbia, while the US part falls totally within the state of Alaska. This study is concerned with Canadian part of the Yukon River with its outlet at Eagle, Alaska - just downstream of the international boundary (288,000 km2). The southern part of the Yukon River basin is characterized by extensive icefields and snowfields at high elevations (up to 4700 m above sea level) with steep slopes, and thus generates considerable runoff. There are also mountain ranges on the eastern and northern boundaries of the basin, while the western areas are milder in slope and partially forested. Snow redistribution by wind, snowmelt, glacier melt and frozen soil processes in winter and spring along with summertime rainfall-runoff and evapotranspiration processes are thus key to the simulation of streamflow in the basin. This supplement shows the development of a vector-based MESH setup for the Canadian portion of the Yukon River Basin at Eagle. Without additional calibration, the vector-based model performance was compared to the previously generated grid-based MESH model whose development was documented in Centre for Hydrology Report #16. MESH was driven by the Environment and Climate Change Canada Global Multiscale Model (GEM) weather model forecasts with precipitation replaced with the Canadian Precipitation Analysis (CaPA) which assimilates local precipitation observations where they exist, collectively referred to as GEM-CaPA. Additionally, the models were run, without additional calibration using the newly developed Regional Deterministic Reforecast System v2 (RDRS v2) forcing. RDRS v2 forcing is being extended as a hindcast by ECCC to approx. 1980 and so will permit 40 year runs of MESH from which streamflow exceedance return periods can be calculated. Model performance was slightly inferior for the vector-based setup compared to the original grid-based one. This may be due to the full calibration applied to the grid-based model and parameter transfer to the vector-based model without recalibration. Model performance also deteriorated when the RDRS v2 was used as forcing data, as the model was originally calibrated to GEM-CaPA. It is expected that model performance will improve once it is fully calibrated using the RDRS v2 forcing data.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".