Evaluation and improvement of PERSIANN-CCS precipitation data at multiple temporal scales in the Bouregreg–Chaouia Basin (Morocco)

ABSTRACT Satellite-based precipitation monitoring addresses challenges like the lack of gauging stations and limited access to data at small temporal scales. This study evaluates the performance of the PERSIANN-CCS product by comparing satellite data with rain gauge measurements in the Bouregreg and Chaouia watershed in Morocco. The database integrates hourly data from PERSIANN-CCS and 18 rain gauges. After filling data gaps, statistical parameters such as R, NSE, bias, RMSE, and MAE were used to assess accuracy. Results show that accuracy varies with time scales and station locations. Correlation (R) and NSE are moderate to high, with low bias and improved RMSE and MAE at longer temporal scales. Satellite products perform better during summer months but exhibit lower accuracy and higher bias during the rainy season. Trend analysis of annual precipitation reveals a general decline, although no statistically significant trends were found. Corrected satellite data show better alignment with observed trends, indicating improved accuracy. Various machine learning models were evaluated to improve estimates, and Random Forest showed the best results, increasing correlation and significantly reducing bias, RMSE, and MAE. This study highlights the potential of corrected satellite data for hydrological and climate applications, demonstrating improvements through machine learning.