A coupled data-driven and three-dimensional (3D) process-based fluorescent dissolved organic matter (fDOM) prediction model was developed for a shallow, subtropical Australian reservoir. The extent to which reservoir water volume, inflow and wind conditions affect the fDOM transport dynamics during cyclonic weather events was assessed through scenario analysis and a data-driven Bayesian Network (BN) approach. The analysis shows that (a) inflow plumes are the main sources of fDOM during heavy rainfall; (b) the concentration of fDOM near the dam wall is related to rainfall intensity; (c) higher reservoir volumes reduce the rate of increase and peak of fDOM concentration during rainfall events; and (d) the degree of fDOM transport to the dam wall was strongly influenced by the prevailing wind direction. A naïve BN was developed for fDOM assessment and displayed a strong sensitivity of the peak fDOM value to rainfall-related characteristics, while the lag time between rainfall event and fDOM peak at the dam wall was highly sensitive to reservoir water volume and wind speed. The developed hybrid modelling approach provides both new information on three-dimensional fDOM transport in reservoirs during extreme weather events through the 3D fDOM model, and an easy to interpret, instantaneous modelling output for treatment operators through the BN modelling component. The latter is an essential addition for water treatment operators, to promptly predict the impacts of extreme weather events and proactively adjust treatment operations, without the computational time burden of a 3D process-based model.