Abstract Event-based hydrological models are extensively adopted for the estimation of design floods and in operational flood forecasting frameworks. However, an accurate estimation of the initial hydrologic condition (IHC) is essential in enhancing the predictive capability of an event-based hydrological model. Hence, in this study, IHCs of an event-based conceptual model are estimated using two different methods: (1) assimilation of observed variables such as streamflow and soil moisture using an ensemble Kalman filter and (2) states obtained from the continuous model calibrated using four different calibration metrics. The observed flood events at the Jagdalpur catchment are simulated using a conceptual hydrologic model setup at two spatial resolutions (lumped and semi-distributed). The results of the study demonstrate that IHCs estimated by the continuous models perform better than those obtained through data assimilation. The performance of semi-distributed event-based models was found to be outperforming their lumped counterparts demonstrating the advantage of increased model resolution. The states obtained from the continuous models calibrated using Nash–Sutcliffe Efficiency (NSE) are performing well in initialising the event-based models. The median efficiency of the semi-distributed event-based model (based on states from the NSE calibrated continuous model) is 0.91 and 0.77 during calibration and validation periods, respectively.

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