Utility of satellite-based precipitation products for drought monitoring over Morocco
Bibliographic record
Abstract
Satellite precipitation products (SPPs) offer valuable data for large-scale drought analysis, although their accuracy varies with climate characteristics and data processing algorithms. This study aims to comprehensively analyze the utility of two satellite precipitation products (SPPs) for drought monitoring over the entire Moroccan territory: the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), using the Standardized Precipitation Evapotranspiration Index (SPEI) at multiple spatiotemporal scales. Ground-based precipitation data from 27 stations (1987–2017) served as reference. The comparison of SPEIs derived from satellite and reanalysis data with ground-based SPEIs revealed strong correlations, though spatial variability was notable, especially in high-altitude areas. Correlation coefficients ranged from 0.32 to 0.92 for CHIRPS and 0.45 to 0.92 for PERSIANN-CDR. Bias was 50–60% for CHIRPS and 37–141% for PERSIANN-CDR, with monthly RMSE values of 10–40 mm and 7–48 mm, respectively. Both products effectively captured drought occurrence and trends, with PERSIANN-CDR showing particularly high accuracy in simulating events. Overall, CHIRPS and PERSIANN-CDR provide valuable insights for drought monitoring and management across Morocco.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".