Application of Satellite Rainfall Estimates in Quantitative Forecasting of Monthly Rainfall Using a Multi-Model Ensemble Approach, Kafue River Basin

Abstract

This study evaluates the reliability of North American Multi-Model Ensemble (NMME) models in forecasting monthly rainfall over the Kafue River Basin using a well-selected multi-model ensemble approach. Gridded monthly rainfall forecasts were derived from global NMME models and validated against satellite-based rainfall products (SRPs) over the basin. To establish a reliable gridded rainfall dataset, three SRPs—TAMSAT, CHIRPS, and ARC2—were assessed against observed station data. Historical data were divided into a calibration period (1983-2003) at the station level and a validation period (2004-2022) using gridded datasets. The NMME models—CMC2 CANSIPSv2, NASA-GEOSS2S, CANCM4i, GFDL-CM2p1, GFDL-CM2p5-FLOR-B01, GFDL-CM2p5, NCEP-CFSv2, and COLA-RSMAS-CCSM—were downscaled using the Canonical Correlation Analysis (CCA) algorithm and evaluated using Spearman’s correlation coefficient, mean bias, and root mean square error (RMSE). The Anomaly Correlation Coefficient (ACC) was used to assess forecast reliability. Results show that CHIRPS outperformed TAMSAT and ARC2 in representing observed rainfall and was used to generate a gridded time-series dataset. NMME model performance improved when validated against gridded datasets rather than station-based point data. The ensemble forecasting approach demonstrated reliable monthly rainfall predictions for December, January, and March (2004-2022). However, caution is advised when using NMME models for October and February, as these months exhibited negative ACC values (−1) over much of the basin. The study highlights spatial and temporal variability in the reliability of individual NMME models, emphasizing the importance of understanding model strengths and limitations for effective climate adaptation and water resource management.