An Application of the Statistical DownScaling Model (SDSM) to Simulate Climatic Data for Streamflow Modelling in Québec
Why this work is in the frame
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Bibliographic record
Abstract
General Circulation Models (GCMs) are widely used tools to assess potential impacts of global climate warming. However, their outputs are difficult to use in regional impact studies with regard to water resources because of their coarse spatial resolution. Downscaling techniques have emerged as useful tools to reduce the problem of discordant scales by deriving regional climate information from global climate data. The objective of this study is to test the capability of one of these techniques, the Statistical DownScaling Model (SDSM), to derive local scale temperature and precipitation data series that can be used as inputs to a hydrologic model for streamflow modelling. Three river basins located in the province of Québec are analyzed. Results show that the SDSM provides reasonable downscaling data when using predictors representing the observed current climate. However, the performance is less reliable when using GCM predictors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it