Evaluation of GRACE and GRACE-FO derived-products for water storage assessment in Moroccan aquifers: analysis of drought and human-induced impacts
Bibliographic record
Abstract
Groundwater overexploitation in Morocco’s arid and semiarid regions poses a sustainability challenge. The Gravity Recovery and Climate Experiment (GRACE) offers valuable groundwater monitoring potential, despite its coarse resolution. This study evaluates GRACE and GRACE-Follow-On products against groundwater level data. The GRACE-Self-Data Assimilation product showed the best performance in Haouz-Mejjate (PCC = 0.97, RMSE = 0.21) and Bahira (PCC = 0.93, RMSE = 0.29), whereas the Goddard Space Flight Center product was more accurate for the Errachidia-Boudnib Cretaceous basin (PCC = 0.39, RMSE = 0.92) and Jurassic aquifers (PCC = 0.93, RMSE = 0.28). Meanwhile, the Jet Propulsion Laboratory’s Mass-concentration solution performed best in Fezna-Tafilalet (PCC = 0.83, RMSE = 0.76). The results show that the combined datasets (Mass-concentration solutions mean and the Combination Service for Time-variable Gravity Fields product) offer the best overall performance. Alarming declines in water storage occurred in Haouz-Mejjate (−0.37 ± 0.018 cm/month) and Bahira (−0.36 ± 0.021 cm/month) during 2015–2022, while southeastern aquifers remained stable until 2018, before declining. The findings emphasize GRACE’s utility in groundwater management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".