Development and Assessment of Seasonal Rainfall Forecasting Models for the Bani and the Senegal Basins by Identifying the Best Predictive Teleconnection

The high variability of rainfall in the Sahel region causes droughts and floods that affect millions people every year. Several forecasting models have been proposed, but results still need to be improved. In this study, linear, polynomial, exponential are developed forecast Bani Senegal River basins. All three use Atlantic sea surface temperature (SST). A fourth algorithm using stepwise regression was also for precipitation estimates over these two uses SST with covariates, mean level pressure (MSLP), relative humidity (RHUM), five El Niño indices. explanatory variables SST, RHUM, MSLP were selected based on principal component analysis (PCA) cluster find homogeneous greatest predictive ability. PERSIANN-CDR data used as dependent variable. Models each pixel 0.25° × spatial resolution. second-order polynomial model a lag about 11 months outperforms all other explains 87% variance watersheds. Nash–Sutcliffe efficiency (NSE) values between 0.751 0.926 basin from 0.175 0.915 basin, which lowest found driest area (Sahara). Results showed North shows more robust teleconnection dynamics both