Characterizing Recharge in Southern Mali Using a Combination of Modeling and Stable Isotopes
Bibliographic record
Abstract
Groundwater recharge in southern Mali is investigated using interpretive recharge models and stable isotopes to identify the dominant recharge mechanism and explore how local variations in geological materials influence the recharge characteristics. At a regional scale, the groundwater level hydrographs from across southern Mali (1998–2002) are relatively consistent, showing seasonal variations, suggesting diffuse recharge is the dominant mechanism. Groundwater samples plot within the range of the weighted mean monthly δ 18 O and δ 2 H concentrations for July-August-September rainfall, and below the weighted mean annual δ 18 O and δ 2 H concentrations for rainfall, suggesting a dominantly rainy season source of recharge. Recharge is simulated for four representative unsaturated zone environments, each with varying soil, laterite and sedimentary bedrock layers, and three ranges of water table depths, for a total of 12 combinations. The simulated recharge response starts in July, 1 month after the arrival of the rainy season, and recharge is greatly accelerated through August to its peak in September. On an annual basis, ~72% of annual rainfall occurs between July and September, and nearly 60% of simulated recharge occurs between August and October. The simulated regional average annual recharge is 519 mm/year (479–560 mm/year range among models). By comparison, recharge estimated from the observed storage anomaly hydrographs using the water table fluctuation method is 384 mm/year (189–619 mm/year) using a specific yield of 0.05, although the range could be as high as 83–772 mm/year given the uncertainty in specific yield values (0.02–0.07). The simulated recharge also agrees with the timing of regional observed storage anomalies for all observation wells, but somewhat less so for the regional GRACE storage anomaly (2002–2008), which has a slower rate of rise in storage and a faster rate of recession compared to the observed storage anomalies and simulated recharge response.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".