SPEI Drought Forecasting in Central Mexico

This study compares three Standardized Precipitation and Evapotranspiration Index (SPEI) prediction models at different time scales: (1) Kalman filter with exogenous variables (DKF-ARX-Pt, FK), (2) gated recurrent unit (GRU), and (3) autoregressive neural networks with external input (NARX). Using observed data from meteorological stations in the State of Mexico and Mexico City, considering performance metrics, such as mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), coefficient of determination (R2), Nash–Sutcliffe efficiency coefficient (NSE) and Kling–Gupta efficiency (KGE). The results indicate that the FK model with exogenous variables is the most accurate model for SPEI prediction at different time scales, standing out in terms of stability and low variance in prediction error. GRU networks showed acceptable performance on long time scales (SPEI12 and SPEI24), but with lower stability on short scales. In contrast, NARX presented the worst performance, with high errors and negative efficiency coefficients at several time scales. Models based on Kalman filters can be key tools to improve drought mitigation strategies in vulnerable regions, as it has an improved average predictive accuracy by reducing the MAE by up to 68% and achieving higher consistency in KGE values at longer time scales (SPEI12 and SPEI24).