# Spatiotemporal Assessment and Correction of Gridded Precipitation Products in North Western Morocco
###### Abstract  
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 (GFM-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. Morocco; gridded precipitation products; GPM; ERA5; sparse gauge network; artificial neural network +
Footnote †: journal: _arxiv.org_  
0000-0002-0001-9999-2237]L. [PERSON]  
1 Geo-Resources, Geo-Environment and Civil Engineering Laboratory, Cadi Ayyad University, Marrakesh 40000, Morocco; 2 [EMAIL_ADDRESS] {M.J.}; [EMAIL_ADDRESS] {M.R. }; 1  
[PERSON]  
1 Geo-Resources, Geo-Environment and Civil Engineering Laboratory, Cadi Ayyad University, Marrakesh 40000, Morocco; 2 [EMAIL_ADDRESS] {M.J.}; [EMAIL_ADDRESS] {M.R. }; 1
Mediterranean region, which is recognized as one of the most sensitive areas to climate change [1]. Climate models predict a decreasing trend in Mediterranean precipitation over the next few decades [2; 3]. In this context, ensuring access to water poses a significant challenge for sustainable development, with a clear environmental dimension that involves preserving a threatened resource in terms of both quantity and quality [4; 5]. Water plays a crucial role in socio-economic development, and accurately assessing its availability is a primary concern for governments, particularly in countries heavily reliant on agricultural revenue [5; 6]. Conventional methods for measuring precipitation primarily rely on rain gauges and weather radars. However, these traditional approaches have limitations in terms of data quality, maintenance costs, and potential data quality issues [7; 8]. With advancements in numerical simulations and Earth observation techniques, satellite-based and model-based methods have emerged as effective approaches for collecting accurate precipitation data with consistent spatiotemporal coverage.