Goumi et al. (2025) Assessing the SM2RAIN-ASCAT dataset in Morocco: Accuracy evaluation and drought monitoring application

Identification

• Journal: Atmospheric Research
• Year: 2025
• Date: 2025-12-20
• Authors: Said El Goumi, Mustapha Namous, Abdenbi Elaloui, Samira Krimissa, Nasem Badreldin, Sakine Koohi, Nafia Elalaouy, ElHoussaine Bouras
• DOI: 10.1016/j.atmosres.2025.108729

Research Groups

• Data Science for Sustainable Earth Laboratory (Data4Earth), Sultan Moulay Slimane University, Beni Mellal, Morocco
• Faculte des Arts et des Sciences (FAFS), Universite de Saint-Boniface, Winnipeg, MB, Canada
• Department of Soil Science, University of Manitoba, Winnipeg, MB, Canada
• Water Engineering Dept., Imam Khomeini International University, Qazvin, Iran
• Geosciences Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakech, Morocco
• Center for Remote Sensing Application (CRSA), College of Agriculture and Environmental Sciences (CAES), Mohammed VI Polytechnic University (UM6P), Ben Guerir, Morocco

Short Summary

This study evaluates the accuracy of the SM2RAIN-ASCAT satellite-based precipitation product against ground observations across various climate zones in Morocco and assesses its suitability for drought monitoring using the Standardized Precipitation Index (SPI). It concludes that SM2RAIN-ASCAT is a reliable option for drought analysis, particularly in arid regions and for long-term hydrological drought monitoring.

Objective

• To assess the suitability and accuracy of the SM2RAIN-ASCAT satellite-based precipitation product for precipitation estimation and drought monitoring in Morocco by comparing it with observed data from 36 ground-based stations across different climate zones.

Study Configuration

• Spatial Scale: Morocco, covering various climate zones, validated against 36 ground-based stations.
• Temporal Scale: Daily, 10-day, and monthly aggregations for precipitation assessment; Standardized Precipitation Index (SPI) calculated at 1, 3, 6, and 12 month timescales.

Methodology and Data

• Models used: Standardized Precipitation Index (SPI).
• Data sources: SM2RAIN-ASCAT (bottom-up satellite-based precipitation product), observed precipitation data from 36 ground-based stations.

Main Results

• The mean correlation coefficient (CC) between SM2RAIN-ASCAT and ground observations improved from 0.45 (daily) to 0.67 (monthly), with monthly and 10-day aggregations showing the most consistent performance.
• The product demonstrated a high probability of detecting rain events, with a monthly Probability of Detection (POD) exceeding 0.75 for 89% of stations.
• SM2RAIN-ASCAT tended to underestimate intense rainfall, but the relative bias was low at nearly half of the stations, and the lowest Root Mean Square Error (RMSE) was observed at the monthly scale.
• The product exhibited climatic bias, underestimating precipitation in Mediterranean regions and overestimating it in arid regions of Morocco, yet its efficacy for drought monitoring was proven.
• Calculated SPI values for short to medium-term durations (1, 3, 6, and 12 months) aligned well with ground observations across several climate zones.
• The correlation for SPI strengthened markedly for 3 and 6 month periods, with CC values of approximately 0.70 and 0.80, respectively.
• Long-term hydrological drought monitoring (SPI-12) showed excellent agreement with ground observations across nearly all stations.
• SM2RAIN-ASCAT is a reliable option for drought analysis in arid regions like Morocco, excelling at detecting droughts in arid zones over humid ones, and identifying wet periods in hot arid climates more effectively than in wetter zones.

Contributions

• Provides a comprehensive accuracy evaluation of the SM2RAIN-ASCAT satellite-based precipitation product specifically for Morocco, stratified by climate zone.
• Demonstrates the suitability and reliability of SM2RAIN-ASCAT for drought monitoring across various timescales (1, 3, 6, 12 months) in a data-scarce region.
• Highlights the product's strengths and weaknesses (e.g., climatic bias, underestimation of intense rainfall) in different Moroccan climate zones, offering practical insights for its application.
• Recommends SM2RAIN-ASCAT for agricultural drought monitoring and water management, particularly in arid regions.

Funding

• Not explicitly mentioned in the provided text.

Citation

@article{Goumi2025Assessing,
  author = {Goumi, Said El and Namous, Mustapha and Elaloui, Abdenbi and Krimissa, Samira and Badreldin, Nasem and Koohi, Sakine and Elalaouy, Nafia and Bouras, ElHoussaine},
  title = {Assessing the SM2RAIN-ASCAT dataset in Morocco: Accuracy evaluation and drought monitoring application},
  journal = {Atmospheric Research},
  year = {2025},
  doi = {10.1016/j.atmosres.2025.108729},
  url = {https://doi.org/10.1016/j.atmosres.2025.108729}
}