Understanding Terrestrial Water Storage Changes Derived from the GRACE/GRACE-FO in the Inner Niger Delta in West Africa
Bibliographic record
Abstract
This study analyzed terrestrial water storage (TWS) changes across the Inner Niger Delta (IND) in Mali (West Africa) from April 2002 to September 2022 using Gravity Recovery and Climate Experiment (GRACE), GRACE-Follow-On (GRACE-FO), and Global Land Data Assimilation System (GLDAS) products. TWS changes exhibited strong seasonal patterns (−170 mm to 330 mm) with a high correlation between GRACE/GRACE-FO and GLDAS (r = 0.92, RMSE = 35 mm). The TWS trend was positive (7.3 to 9.5 mm/year). Maximum TWS changes occurred in September, while minimum values were observed between April and May. Wavelet analysis identified dominant seasonal cycles (8–16 months). Finally, we examined the climatic effects on TWS changes along the Niger River, from its source in the humid zones of Guinea to the semi-arid Sahelian zones of the IND in Mali. Precipitation (P) and potential evapotranspiration (PE) influence TWS changes only in the humid regions (r = 0.18–0.26, p-value < 10−2). Surface water bodies (SWB) exhibited a significant correlation with TWS in all regions, with r exceeding 0.50 in most cases. Groundwater changes, estimated from GRACE/GRACE-FO and GLDAS, showed strong agreement (r > 0.60, RMSE < 120 mm), with recharge rates increasing in semi-arid and Sahelian regions (r > 0.70, p-value < 10−3). This study highlights that precipitation, surface water bodies, and groundwater recharge appear as primary drivers of TWS in different regions: precipitation in the humid forest of Guinea, surface water bodies in the Southern and Northern Guinea Savanna along the Guinea–Mali border, and groundwater recharge in the semi-arid and IND Sahelian regions of central Mali.
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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 | 0.000 |
| 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 | 0.001 |
| 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 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".