Estimating Climate Change’s Impacts on the Recharge of an Ungauged Tropical Aquifer (Togolese Coastal Sedimentary Basin)
Bibliographic record
Abstract
The aquifers of the Togolese coastal sedimentary basin are the principal sources of water for almost half of the country’s population. These aquifers’ features have not been adequately monitored and studied. The resource is threatened by human activities, notably agriculture, industry, and withdrawals for drinking water supplies. This situation is exacerbated by the potential effects of climate change. For this research, a basin-scale study was conducted to estimate current groundwater recharge and its future evolution in response to climate change. A recharge model based on Thornthwaite–Mather balance equations using runoff coefficients characterizing land use was fed with current and future climate data from an optimistic scenario (RCP 4.5) and a pessimistic scenario (RCP 8.4). Despite the associated uncertainties, the soil–water balance model at monthly time steps predicts a recharge of 3 to 455 mm per year from 2020 to 2039, and 40 to 420 mm per year from 2040 to 2059 under the optimistic RCP 4.5 scenario. According to the pessimistic RCP 8.5 scenario, the recharge will range between 16 and 515 mm per year from 2020 to 2049 and from 1 to 467 mm per year between 2040 and 2059. As a result, the basin’s groundwater recharge range, which is currently 47 to 225 mm, will significantly increase. This study provides a scientific basis for the sustainable management of groundwater in the Togolese coastal sedimentary basin. The recharge of the groundwater in the basin will increase regardless of the climate scenario and will support future development in the Togolese coastal sedimentary basin.
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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 | 0.001 |
| 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 | 0.001 | 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".