On the Use of Multi Site Generated Meteorological Input Data for Realistic Hydrological Modeling in the Context of Climate Change Impact Studies
Bibliographic record
Abstract
Weather generators have been used successfully for a wide array of applications such as hydrology, agriculture, environmental studies and recently climate change studies. Unfortunately, most weather models ignored spatial dependence exhibited by weather series at multiple sites because of climatic phenomena, which extend over a region rather than a station location and constrain the observations in a given place to be correlated to those in the surrounding area. The multi-site generation approach was then developed and has been successfully applied to precipitation occurrences and amounts. In this paper, the proposed multi-site generation approach will be used to simulate minimum and maximum temperature data. It analyzes patterns in space and investigates the dependence of weather data at multiple locations. It aims at reproducing daily spatial autocorrelations in the synthetic time series that are identical to those observed. The Peribonca River Basin in the Canadian province of Quebec was used and the results are generally satisfactory. Moreover, this multi-site approach has an important repercussion on the hydrological model compared to the uni-site approach. In order to evaluate the effects of climate changes on the Peribonca river basin hydrology, the parameters of the weather generator will be modified.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".