Application of the HBV model for the future projections of water levels using dynamically downscaled global climate model data
Bibliographic record
Abstract
Abstract The Hydrologiska Byråns Vattenbalansavdelning (HBV) model was used to project the future water levels of the Mackenzie River at selected stations. The Weather Research and Forecasting (WRF) model was utilized to dynamically downscale the Global Climate Model data. The calibrated and validated HBV model was run with the WRF downscaled CanESM2 data and with the PCIC data for the historical (1979–2005) period, and then compared with the observed flow data at the Fort Simpson station and the Arctic Red River station. The simulated streamflow showed a good correlation with the observed streamflow (R2 value was around 0.85). The HBV model was then forced with the bias-corrected WRF downscaled daily rainfall and temperature data driven by the CanESM2 RCP 4.5 and RCP 8.5 climate scenarios to simulate the future streamflow for the 2041–2070 period. Rating curves were used to convert streamflow to water levels. At the Fort Simpson station, mean flow was projected to decrease by about 5% under both RCP 4.5 and RCP 8.5 scenarios, whereas the peak flow was likely to reduce by about 12 and 9% for RCP 4.5 and RCP 8.5 scenarios, respectively, in the 2050s. The projected lower water levels could affect the navigability and the northern ferry operations of the Mackenzie River.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".