Assessment of Satellite and Reanalysis Precipitation Data Using Statistical Metrics, Classification Metrics, and Wavelet Analysis in a Semi-Arid Region, Morocco

Climate change, marked by decreasing rainfall and increasing extreme events, represents a major challenge for water resources, particularly in semi-arid regions. To estimate aquifer recharge, it is essential to assess the fraction of precipitation contributing to groundwater recharge and to implement a water balance model. However, the limited number of rainfall stations has led researchers to rely on satellite and reanalysis rainfall products. The accuracy of these datasets is essential for reliable hydrological modeling. In this study, we evaluated five rainfall products—CHIRPS, ERA5_Ag, CFSR, GPM, and PERSIANN-CDR—by comparing them to ground measurements from gauging stations in the central Haouz region of Marrakech at three temporal scales: daily, monthly, and annual. Statistical and classification measures were applied, as well as wavelet analysis, to improve the accuracy of the evaluation. The results identified ERA5_Ag and GPM as the most accurate products in capturing rainfall events. Nevertheless, ERA5_Ag showed a high bias. After applying the quantile mapping method to correct the bias, the product exhibited greater accuracy. The corrected datasets from these two products will be used to estimate recharge over the last 30 years, contributing to the development of a hydrogeological model for groundwater dynamics.