Statistical seasonal rainfall and streamflow forecasting for the Sirba watershed, West Africa, using sea-surface temperatures
Bibliographic record
Abstract
The ability of various statistical techniques to forecast the July-August-September (JAS) total rainfall and monthly streamflow in the Sirba watershed (West Africa) was tested. First, multiple linear regression was used to link predictors derived from the Atlantic and Pacific sea-surface temperatures (SST) to JAS rainfall in the watershed up to 18 months ahead; then, daily precipitation was generated using temporal disaggregation; and finally, a rainfall–runoff model was used to generate future hydrographs. Different combinations of lag times and time windows on which SSTs were averaged were considered. Model performance was assessed using the Nash-Sutcliffe coefficient (Ef), the coefficient of determination (R2) and a three-category hit score (H). The best results were achieved using the Pacific Ocean SST averaged over the March–June period of the year, before the rainy season, and led to a performance of R2 = 0.458, Ef = 0.387 and H = 66.67% for JAS total rainfall, and R2 = 0.552, Ef = 0.487 and H = 73.28% for monthly streamflow.Editor D. Koutsoyiannis; Associate editor Not assigned
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Research integrity | 0.000 | 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".