1,1,- 1Data Science for Sustainable Earth Laboratory (Data4Earth), Sultan Moulay Slimane University, Beni Mellal 23000, Morocco
- 2Faculté des Arts et des Sciences (FAFS), Université de Saint-Boniface, Winnipeg, MB, Canada
- 3Geosciences Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakech, 40000, Morocco
- 4Center for Remote Sensing Application (CRSA),College of Agriculture and Environmental Sciences (CAES), Mohammed VI Polytechnic University (UM6P), Ben Guerir, 43150, Morocco
SM2RAIN-ASCAT is a satellite-based precipitation product that derives rainfall estimates from soil moisture observations using a bottom-up approach. This study evaluates its performance for precipitation estimation and drought monitoring across Morocco by comparing it with in-situ data from 36 ground-based stations covering multiple climate zones. In this context, a range of quantitative and qualitative metrics was used to validate SM2RAIN-ASCAT data against observed precipitation. The Standardized Precipitation Index (SPI) was calculated at 1, 3, 6, and 12-month timescales to assess drought monitoring effectiveness, with performance stratified by climate zone.
Results reveal that correlation coefficients with ground observations increased from 0.45 at the daily time scale to 0.67 at the monthly time scale, with 10-day and monthly aggregations offering the best agreement. The dataset revealed a strong ability to detect rain, attaining monthly Probability of Detection values exceeding 0.75 at 89% of stations. Although the product exhibited a tendency to underestimate intense rainfall events, relative bias remained low at nearly half of the stations, with minimum RMSE values occurring at the monthly scale. Regional performance showed consistent variability, with underestimation in Mediterranean zones and overestimation in arid regions, though drought monitoring capability remained robust.
SPI values for short- to medium-term durations aligned well with ground observations across Morocco's climate zones. Agreement was weak to moderate for 1-month SPI but improved substantially for 3-month and 6-month periods, with correlation coefficients of approximately 0.70 and 0.80, respectively. Long-term drought monitoring using SPI-12 showed particularly strong performance, with excellent agreement at nearly all stations. The product showed superior accuracy in detecting droughts in arid zones against humid zones and wet conditions in hot arid climates compared to wetter climates. These findings suggest that integrating bottom-up SM2RAIN-ASCAT and top-down approaches can enhance precipitation and drought monitoring by addressing the limitations of each method. SM2RAIN-ASCAT is particularly recommended for agricultural drought monitoring and water resource management in arid regions.
Keywords: Standardized Precipitation Index, SM2RAIN-ASCAT, Rainfall, Bottom-up approach, Drought
How to cite: El Goumi, S., Namous, M., Elaloui, A., Krimissa, S., Nait-Taleb, O., Chouidda, H., Elalaouy, N., and Bouras, E.: Assessing SM2RAIN-ASCAT rainfall products for drought monitoring across Morocco, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-695, https://doi.org/10.5194/egusphere-egu26-695, 2026.
