Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting

The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological (RCMs), has provided the means to establish baselines for quantification, propagation, reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed improve operational real-time forecasts Upper Zambezi River Basin (UZRB), Africa, utilizing novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe discuss main steps required implement, calibrate, validate an hydrologic forecasting system (HFS) using VEF Hydrologic Processing Strategies (HPS). HFS was constructed monitor daily forecast them up eight days future. process called short- medium-range (SR2MR) implemented rainfall data from three SPPs (The TRMM Multisatellite Analysis TMPA-RT, NOAA CPC Morphing Technique CMORPH, Estimation Remotely Sensed Artificial Neural Networks, PERSIANN) Global System (GFS). preprocessing (HPR) strategy considered all raw bias corrected estimates calibrate distributed models (HYMOD_DS, HBV_DS, VIC 4.2.b). processing (HP) optimal parameter sets estimated during calibration increase number ensembles available forecasting. Finally, inference-based approaches were evaluated application a postprocessing (HPP) strategy. final evaluation multiple sources, i.e., precipitation products, models, sets, significantly achieved through fully implementation combined with several HPS. challenges opportunities associated SR2MR are discussed.