Hydrometeorological validation of a Canadian Regional Climate Model simulation within the Chaudière and Châteauguay watersheds (Québec, Canada)
Bibliographic record
Abstract
This study involved regional validation of a recently developed Canadian Regional Climate Model (CRCM) simulation (version 4.1.1). Four hydrometeorological variables, minimum and maximum daily temperatures, total precipitation, and total runoff, were examined within the Châteauguay and Chaudière watersheds, Québec, Canada. These watersheds, located in southern Québec, are smaller in area (2530 and 6682 km2, respectively) than the size of watersheds usually used to validate this type of model (104–106 km2). The objective of the study was to evaluate if the model could reproduce data similar to field observations within these watersheds. A successful model could be used to produce reliable predictions regarding future climate change effects on watershed hydrology within any given watershed demonstrating similar climatological variables. Results show that even though the CRCM can produce reliable results, there remains a significant bias for each variable at least during one season. Analyses show that the bias for maximum temperature is not very strong (<1 °C) within either of the studied watersheds. However, minimum temperature is clearly underestimated (≈2 °C) in winter and in spring within both watersheds. Total precipitation is significantly overestimated in winter, spring and summer within the Châteauguay watershed (11%, 35%, and 30%, respectively), but for the Chaudière watershed overestimation is less than 5%. Total runoff is strongly overestimated in both watersheds for most of the annual cycle (>30%) and is highly variable in winter and spring. Ideally, the results of this study will be used to guide future studies on the causes of CRCM bias and ultimately lead to model improvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".