# Spatiotemporal Assessment and Correction of Gridded Precipitation Products in North Western Morocco
## 4 Conclusions and Perspectives  
The Bouregreg watershed serves as a representative example of an arid and semi-arid region characterized by high spatial and temporal variability in precipitation. Being situated in the Mediterranean region, this area is especially susceptible to climate change impacts. Accurate precipitation data are crucial for the region's development, considering its significant influence on various aspects. However, due to the limited number of measurement stations, reanalysis and global satellite data with continuous spatial coverage play a crucial role. Among the available products, IMERG and ERA5 represent modern gridded precipitation datasets that offer high-quality records, fine resolutions, and extensive spatiotemporal coverage, making them valuable for water resource management. Therefore, it is essential to thoroughly evaluate their performance in the Bouregreg watershed. In this regard, this study conducted a comprehensive assessment of three IMERG products (GPM-F, GPM-L, and GPM-E) and the ERA5 reanalysis product, utilizing ground-based daily observations from September 2000 to August 2020 in the Bouregreg watershed. The key findings of the study indicate that the GPM-F product exhibited the best performance across various temporal and spatial scales, followed by the GPM-L and GPM-E products. On the other hand, the ERA5 reanalysis product displayed the weakest performance at pixel evaluation. In terms of spatial analysis, the GPM models did not show significant variation with altitude, likely due to the relatively low altitude range of the rain gauges in the study area, which does not exceed 620 m. Conversely, the ERA5 product demonstrated lower performance at higher altitudes, characterized by increasing bias and decreasing correlation with increasing altitude. Additionally, the GPM products demonstrated good detection capability, precision, and accuracy at both temporal and spatial scales. All products, particularly GPM-F, showed favorable performance for precipitation intensities below 1 mm per day. In contrast, ERA5 exhibited better detection capability for higher intensities (\(\geq\)40 mm/day). Overall, all four datasets successfully reproduced seasonal cycles and spatial distributions, although GPM-F exhibited superior performance in capturing seasonal cycles, while ERA5 displayed high consistency in spatial distribution. Moreover, all products exhibited high biases during the rainy months and low biases during the summer months. In conclusion, the results suggest that precipitation products, especially the GPM-F product, are well-suited for arid and semi-arid environments and can be utilized for effective water resource quantification and management. Enhancing event detection capability and accuracy is crucial for improving gridded precipitation products. After applying the bias correction using the ANN model, the GPM-F product showed significant improvements in several daily precipitation indices, including accuracy, precision, F1_Score, and FDR. Additionally, the precision of the GPM-F in terms of bias, RMSE, and MAE also increased. However, although the accuracy of the GPM-F product has been improved,there is still potential for further enhancement, particularly in terms of recall, AUC value, and coefficient of determination. This would result in more accurate and reliable rainfall estimates for the Bouregreg watershed and similar regions. Finally, there is no doubt that it is interesting to assess the models' performance in other regions. On this subject, we point out the importance of conducting additional research and validation in order to generalize our findings beyond the studied basin and expand the use of gridded data to other hydrological applications such as surface water management, hydrological modeling, or the monitoring of drought and floods. Conceptualization, L.A.D., J.M., M.R., M.E.S. and A.H.; methodology, L.A.D., J.M., M.R., M.E.S. and A.H.; software, L.A.D. and M.R. ; validation, L.A.D., J.M., M.R., M.E.S.