Comparison of downscaling methods for mean and extreme precipitation in Senegal
Bibliographic record
Abstract
Study region The study considers six precipitation stations located in Senegal, West Africa . Senegal is located in the Sahel, an area that is threatened by climate variability and change. Both droughts and extreme rainfall have been an issue in recent years. Study focus Two different statistical downscaling techniques were applied to the outputs of four regional climate models at six selected precipitation stations in Senegal. First, the delta-change method was applied to the mean annual precipitation as well as the 5, 10, 20, 50 and 100-year return period daily precipitation events. Second, a quantile–quantile transformation (QQ) was used to downscale the monthly distributions of precipitation simulated by regional climate models (RCMs). The 5, 10, 20, 50 and 100-year daily precipitation events were afterward calculated. All extreme events were calculated assuming that maximum annual daily precipitations follow the generalized extreme value (GEV) distribution. The two-sided Kolmogorov–Smirnov (KS) test was finally used to assess the performance of the quantile–quantile transformation as well as the GEV distribution fit for the annual maximum daily precipitation. New hydrological insights for the region Results show that the two downscaling techniques generally agree on the direction of the change when applied to the outputs of same RCM, but some cases lead to very different projections of the direction and magnitude of the change. Projected changes indicate a decline in mean precipitation except for one RCM over one region in Senegal. Projected changes in extreme precipitations are not consistent across stations and return periods. The choice of the downscaling technique has more effect on the estimation of extreme daily precipitations of return period equal or greater than ten years than the choice of the climate models.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".