Evaluation of GRACE and GRACE-FO derived-products for water storage assessment in Moroccan aquifers: analysis of drought and human-induced impacts
Why this work is in the frame
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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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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 | 0.001 | 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 it