Precipitation is a crucial source of data in hydrological applications for water resources management. However, several regions suffer from limited data from a ground measurement network. Remotely sensed data may provide a viable alternative for these regions. This study aimed to evaluate six satellite products (GPM-F, CHIRPS, PERSIANN-CCS-CDR, GPM-L, GPM-E and PDIR-Now), with high spatio-temporal resolution, in the sub-Saharan regions of Morocco. Precipitation observation data from 33 rain-gauge stations were collected and used over the period from September 2000 to August 2020. The assessment was performed on three temporal scales (daily, monthly and annually) and two spatial scales (pixel and basin scales), using different quantitative and qualitative statistical indices. The results showed that the GPM-F product performed the best, according to the different evaluation metrics, up to events with 40 mm/day, while the GPM near real-time products (GPM-E and GPM-L) were better at detecting more intense rainfall events. At the daily time scale, GPM-E and GPM-L and, on monthly and annual scales, CHIRPS and PERSIANN-CCS-CDR, provided satisfactory precipitation estimates. Moreover, the altitude-based analysis revealed a bias increasing from low to high altitudes. The continental and mountainous basins showed the lowest performance compared to the other locations closer to the Atlantic Ocean. The evaluation based on the latitudes of rain gauges showed a decrease of bias towards the most arid zones. These results provide valuable information in a scarcely gauged and arid region, showing that GPM-F could be a valuable alternative to rain gauges.

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.

Accurate flood simulation and forecasting are often hindered by the limited availability of high-resolution rainfall observations, particularly at sub-daily scales. This challenge is especially pronounced in developing countries such as Morocco, where dense rain gauge networks are lacking. Satellite-based precipitation products offer a valuable alternative for analyzing extreme hydrological events in poorly gauged regions. This study evaluates the applicability of three low-latency satellite precipitation products and, for the first time in Morocco, investigates the integration of two satellite products to improve flood modeling performance. The event-based MISDc hydrological model was applied using hourly streamflow observations from September 2000 to August 2015 across 20 sub-basins in southern Morocco. Flood events were identified using a 95th percentile discharge threshold, resulting in 558 events for model calibration and validation. Results demonstrate that integrated satellite products consistently outperform individual products, particularly during the validation phase. Positive hourly Kling–Gupta Efficiency (KGE) values were obtained at 14 out of 20 stations (70%), with a median KGE of 0.2. Model performance was influenced by basin physiographic characteristics, with larger basins showing improved simulations, while steeper slopes and higher elevations were associated with reduced accuracy. Overall, this study highlights the added value of integrated satellite products for sub-daily flood modeling in semi-arid and arid basins lacking hourly rainfall observations.

This study presents a multiscale evaluation of five gridded climate products, ERA5-Land, TerraClimate, CHIRPS, CHIRTS, and PERSIANN CCS-CDR, over the semiarid topographically complex High Atlas Mountains of Morocco. Using observations from nine ground stations, performance was assessed at daily, monthly, and annual scales through correlation coefficient, RMSE, bias, and MAE metrics. Results reveal substantial spatial and temporal variability in accuracy. ERA5-Land consistently achieved the highest agreement with observations (CC = 0.92–0.99 for temperature), attributed to its multisource data assimilation framework. TerraClimate also performed well, particularly for temperature and monthly precipitation, benefiting from hybrid downscaling and elevation correction. In contrast, satellite-based products exhibited pronounced systematic biases, including precipitation overestimation (+110% for CHIRPS), underestimation (−34% for PERSIANN CCS-CDR), and significant temperature biases for CHIRTS, especially at high-altitude stations. Spatial analysis confirmed a consistent decline in performance with increasing elevation, underscoring the limitations of both reanalysis and satellite-based estimation in mountainous terrain. These findings stress the importance of bias correction and careful dataset selection for hydrological, agricultural, and climate change applications. More broadly, this work offers a transferable evaluation framework for data-scarce, topographically complex regions, providing actionable guidance for researchers, water managers, and policymakers supporting climate adaptation strategies.