Accurate and spatially distributed precipitation data are fundamental to effective water resource management. In Morocco, as in other arid and semi-arid regions, precipitation exhibits significant spatial and temporal variability. Indeed, there is an intra- and inter-annual variability and the northwest is rainier than the rest of the country. In the Bouregreg watershed, this irregularity, along with a sparse gauge network, poses a major challenge for water resource management. In this context, remote sensing data could provide a viable alternative. This study aims precisely to evaluate the performance of four gridded daily precipitation products: three IMERG-V06 datasets (GPM-F, GPM-L, and GPM-E) and a reanalysis product (ERA5). The evaluation is conducted using 11 rain gauge stations over a 20-year period (2000–2020) on various temporal scales (daily, monthly, seasonal, and annual) using a pixel-to-point approach, employing different classification and regression metrics of machine learning. According to the findings, the GPM products showed high accuracy with a low margin of error in terms of bias, RMSE, and MAE. However, it was observed that ERA5 outperformed the GPM products in identifying spatial precipitation patterns and demonstrated a stronger correlation. The evaluation results also showed that the gridded precipitation products performed better during the summer months for seasonal assessment, with relatively lower accuracy and higher biases during rainy months. Furthermore, these gridded products showed excellent performance in capturing different precipitation intensities, with the highest accuracy observed for light rain. This is particularly important for arid and semi-arid regions where most precipitation falls under the low-intensity category. Although gridded precipitation estimates provide global coverage at high spatiotemporal resolutions, their accuracy is currently insufficient and would require improvement. To address this, we employed an artificial neural network (ANN) model for bias correction and enhancing raw precipitation estimates from the GPM-F product. The results indicated a slight increase in the correlation coefficient and a significant reduction in biases, RMSE, and MAE. Consequently, this research currently supports the applicability of GPM-F data in North Western Morocco.

Analysis of the temporal relationship between meteorological drought and hydrological drought is crucial in monitoring water resource availability. This study examined the linear and lagged relationships of the spread of meteorological drought to hydrological drought and their joint effects on low-flow drought variability in the Oum Er-Rbia (OER) watershed. To this end, random forest (RF) model and statistical methods were used to study the characteristics of the temporal relationships between meteorological and hydrological drought indices at monthly, seasonal, and annual scales. The various analyses revealed that the relationship between hydrological and meteorological drought is mainly a function of the time scale considered, the choice of indices to describe each type of drought and the season considered. The hydrological drought of surface water and snow cover is synchronized with the meteorological drought at the monthly, seasonal, and annual scales. In contrast, the transition from meteorological drought to groundwater drought has a lag time of 1 month and is statistically significant up to t − 5 and t + 5, i.e., 6 months. The linear correlation between the annual rainfall deficit and the monthly groundwater storage index was the lowest (0.15) in December and the highest (0.83) in March. This suggests a seasonal response of groundwater drought to the cumulative effects of precipitation deficits. The RF analysis highlighted the importance of the cumulative characteristics of meteorological drought regarding the severity of low-flow drought. The meteorological drought indices at longer time scales have a greater impact on the severity of low-flow drought, with a contribution of approximately 10% per index. However, the relative contributions of meteorological factors and hydrological indices rarely exceed 5%. Thus, by exploring for the first time the complex interactions among the severity of low-flow regimes, meteorological and hydrological drought indices and meteorological factors, this study provides a new perspective for understanding the characteristics of propagation from meteorological to severe hydrological drought.

Over the last century, significant climate changes, including more intense droughts and floods, have impacted agriculture and socio-economic development, particularly in rain-dependent regions like Marrakech–Safi (MS) in Morocco. Limited data availability complicates the accurate monitoring and assessment of these natural hazards. This study evaluates the role of satellite data in drought monitoring in the MS region using rain gauge observations from 18 stations, satellite-based precipitation estimates from Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), and temperatures from the fifth generation of the atmospheric global climate reanalyzed Era5-Land data. The Standardized Precipitation Evapotranspiration Index (SPEI) was calculated at various timescales to characterize droughts. Statistical analysis was then performed to assess the correlation between the SPEI and the cereal yields. The results show that CHIRPS effectively monitors droughts, demonstrating strong statistically significant correlations (r ~ 0.9) with the observed data in the plains, the plateaus, Essaouira–Chichaoua Basin, and the coastal zones, along with a good BIAS score and lower root mean square error (RMSE). However, discrepancies were observed in the High Atlas foothills and the mountainous regions. Correlation analysis indicates the significant impact of droughts on agricultural productivity, with strong correlations between the Standardized Yield Residual Series (SYRS) and SPEI-6 in April and SPEI-12 in June (r ~ 0.80). These findings underscore the importance of annual and late-season precipitation for cereal yields. Analysis provides valuable insights for decision-makers in designing adaptation strategies to enhance small-scale farmers’ resilience to current and projected droughts.