Yukon River Basin Streamflow Forecasting System - Advancing, Calibrating, Demonstrating Snow Assimilation and Estimating Ungauged Basin Flow: The Vector-Based MESH Model of the 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 further development of a vector-based MESH setup for the Canadian portion of the Yukon River Basin down to Eagle, Alaska. For operational forecasting, MESH is 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 newly developed Regional Deterministic Reforecast System v2.1 (RDRS v2.1) forcing has been extended to span the period 1980-2018 enabling long-term assessments of hydrology. The revised vector-based model was calibrated for operational use based on the GEM-CaPA forcing dataset, and for performing historical simulations based on the RDRS v2.1 forcing dataset, using the period 2004-2011 in both cases. Performance was compared to the previously generated grid-based MESH model whose development was documented in Centre for Hydrology Report #16. A long-term historical simulation was then performed using RDRS v2.1 from which streamflow exceedance return periods for 15 important stations were calculated and presented in this supplement. Calibration has generally improved the performance of the vector-based setup compared to the previous simulations presented in supplement #1 of report #16. Parameter sets are slightly different when the model is calibrated to RDRS v2.1 compared to GEM-CaPA due to differences between the two datasets. A pilot study of the potential benefits of snow data assimilation into the existing MESH forecast system was conducted using historical data and the gridded MESH product that is used operationally by Yukon Environment. This test showed benefits to assimilating surface snowpack observations into MESH to correct winter precipitation. Outputs with assimilation showed improved snowpack simulations and improved streamflow forecasts.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".