A New Workflow for Estimating Groundwater Recharge in Data-Scarce Environments.
Bibliographic record
Abstract
This study proposes a novel workflow for estimating groundwater recharge, using the Densu Basin in Ghana as a case study. The method combines the water table fluctuation method (WTFM) and the soil water balance (SWB) approach to address data limitations and enhance estimation accuracy. Recharge estimates from both methods revealed significant spatial and seasonal variability, with higher rates in the northern sectors characterized by favorable soil types (sandy clay loam and sandy clay) and extensive forest cover. The WTFM, applied from 2004 to 2009, yielded an average monthly recharge of 43.9 mm, while the SWB approach, covering 1960 to 2015, estimated a lower average of 33.7 mm. The JJA season (June-July-August) exhibited the highest recharge contribution. The study employed WTFM estimates to bias-correct SWB estimates, with linear scaling emerging as the most effective method. This correction resulted in mean monthly recharge rates ranging from 27 to 39 mm/month, increasing from the southern to the northern parts of the basin. The SCS method estimated an annual runoff of 439 mm (26\% of annual rainfall). The research highlights the significant impact of soil properties, land cover, and rainfall patterns on recharge dynamics and recommends the need for adaptive, localized water management strategies to ensure the long-term sustainability of groundwater resources in the Densu 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".