Spatially Distributed Hydrological Modelling of a Western Africa Basin
Bibliographic record
Abstract
Distributed hydrological simulations aid to investigate the spatio-temporal behaviour of hydrological variables. However, data to feed hydrological models are not always available mainly due to lack of gauges or high retrieval fees. In this research, two 0.25- degree daily precipitation databases from the Tropical Rainfall Measuring Mission (TRMM) were tested to simulate daily runoff in the basin of the main Upper Niger River tributary. Precipitation data are TRMM and TRMM Real Time (RT) 3B42V7. For runoff simulation, the grid-based hydrological model CEQUEAU was chosen. To estimate the evaporation in the model, temperatures were retrieved from the third-generation reanalysis ERA-Interim. From gauges and both TRMM data, monthly basin precipitation was calculated and compared to analyse the performance of TRMM to estimate rainfall. Runoff was simulated with each of these three precipitation products. In each case, the daily ERA-Interim temperatures were used. By Nash-Sutcliffe model Efficiency (NSE) and coefficient of determination (R2), model performance was evaluated through comparison of daily discharges with simulations for both calibration and validation periods. Results show correlation of TRMM by 0.95 and TRMMRT by 0.91 with gauge data. Both TRMM products combined with ERA-Interim temperature were found suitable for daily runoff modelling with NSE >0.835 and R2 >0.872.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".