Spatiotemporal Assessment and Correction of Gridded Precipitation Products in North Western Morocco

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