Impacts of Regional Climate Model Spatial Resolution on Summer Flood Simulation
Bibliographic record
Abstract
This study aims to evaluate the impact of the Canadian Regional Climate Model’s (CRCM) spatial resolution on summer floods simulation. Four different climate simulations issued from the fourth version of the CRCM (two driven by the Canadian General Circulation Model (CGCM) and two driven by the ERA40c reanalysis) are employed. One simulation at 45 km resolution and another one at 15km resolution for each driver were compared on a daily time-step for the 1960-1990 period. These four simulations are used as inputs for two hydrological models of varying complexity (HSAMI and MOHYSE). Each model is calibrated using three different objective functions based on the Kling-Gupta Efficiency criterion (KGE) to target floods. Two seasonal indices are used to evaluate the CRCM outputs: bias (temperature) and relative bias (precipitation). For the streamflow simulations analysis, the seasonal values of KGE and relative bias are used. The results show an impact of spatial resolution on climate model outputs, on streamflow simulation and flood indicators in the hydrological models. However, other elements such as climate model driver and domain size can influence the results, highlighting the need for further research to assess the impact of spatial resolution on summer floods.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".