Abstract
This study thoroughly assesses four well-developed SPPs (GPM, TRMM, CHIRPS, and PERISIANN-CDR) within four basins in northern Morocco, in the Tangier–Tetouan–Al Hoceima region. Their performance is assessed by directly comparing the SPPs against data collected from 49 rain gauge stations distributed across 4 basins with different climates (humid, semi-humid, and semi-arid) and topographical conditions within the region. The evaluation is conducted at three time scales (daily, monthly, and annually) and three spatial scales (point-to-pixel, basin, and regional) from January 2000 to December 2022. Various statistical measures assess their performance, including continuous (RSR, PBIAS, R2, and RMSE) and categorical (POD, FAR, FBI, and ACC) metrics. Results indicate a poor performance at a daily time scale at all three spatial scales (RSR > 0.95, R2 < 0.39, and PBIAS > ± 50%). However, GPM and TRMM outperformed the other SPPs on monthly and annual scales. The four SPPs estimate precipitation better in humid and semi-humid regions than in semi-arid regions. TRMM and GPM perform better in humid regions, while CHIRPS outperforms the other SPPs in semi-humid regions. Meanwhile, in semi-arid regions, TRMM and CHIRPS outperform other SPPs. The SPPs tend to overestimate precipitation, except in humid climates where CHIRPS and TRMM underestimate precipitation. Lower altitudes (< 500 m) give better results for SPPs, while higher (> 500 m) altitudes pose challenges for accurate precipitation estimation. In addition, these results lay the foundations for future algorithm development within the SPPs mission, underlining the ongoing need for research and improvement in this field.
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Abbreviations
- SPPs:
-
Satellite-based precipitation products
- GPM:
-
Global precipitation measurement mission
- TRMM:
-
Tropical rainfall measuring mission
- CHIRPS:
-
Climate hazards group infrared precipitation with station data
- PERISIANN-CDR (P-CDR):
-
Precipitation estimation from remotely sensed information using artificial neural networks (P-CDR)
- RSR:
-
Root mean standard deviation ratio
- PBIAS:
-
Percent bias coefficient
- R 2 :
-
Coefficient of determination
- RMSE:
-
Root mean square error
- POD:
-
Probability of detection
- FAR:
-
False alarm ratio
- FBI:
-
Frequency bias index
- ACC:
-
The accuracy
- TTA:
-
The Tangier–Tetouan–Al Hoceima region
- TN:
-
Tangier Basin
- LK:
-
Loukkos Basin
- MC:
-
Mediterranean Coast Basin
- GN:
-
Ghiss-Nekor Basin
- GPCP:
-
Global Precipitation Climatology Project
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Acknowledgements
We gratefully acknowledge the Loukkos Water Basin Agency for providing the rainfall data used in this study. We would also like to thank the NASA, JAXA, and USGS teams for their efforts in creating and freely sharing the precipitation products used in the research. Finally, we would like to express our gratitude to the editor and the reviewers for dedicating their time to assess our work.
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Et-Takaouy, C., Aqnouy, M., Boukholla, A. et al. Exploring the spatio-temporal variability of four satellite-based precipitation products (SPPs) in northern Morocco: a comparative study of complex climatic and topographic conditions. Med. Geosc. Rev. (2024). https://doi.org/10.1007/s42990-024-00119-5
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DOI: https://doi.org/10.1007/s42990-024-00119-5