Hydrological response to dynamical downscaling of climate model outputs: A case study for western and eastern snowmelt-dominated Canada catchments
Bibliographic record
Abstract
An analysis of hydrological response to a dynamically downscaled multi-member multi-model global climate model (GCM) ensemble of simulations based on the Canadian Regional Climate Model (CRCM) is presented for three snowmelt-dominated basins in Canada. The basins are situated in the western mountainous (British Columbia) and eastern level (Quebec) regions in Canada, providing comprehensive experiments to validate the CRCM over various topographic features. The evaluation of the CRCM as a tool to improve GCM simulations of catchment scale hydrology is investigated within the bounds of uncertainty associated with RCM simulations. Daily climate variables were extracted from a 30-year CRCM and GCM ensemble simulations. The hydrological response was assessed through the comparison of catchment water components simulated by SWAT. Results show that the CRCM captures the primary features of observed climate, but there are significant biases. Most noteworthy are a positive bias in precipitation and a negative bias in temperature over the BC basin. When looking at the hydrological modeling results, the benefit of using the RCM versus GCMs emerged distinctly for the mountainous BC basin where the RCM is preferred over the GCMs. The sensitivity experiments show that uncertainty in the GCM/RCM’s internal variability must be assessed to provide suitable regional hydrological responses to climate change.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".