Author(s): Kumudu Madhawa Kurugama; So Kazama; Yusuke Hiraga

Linked Author(s): So Kazama

Keywords: Data assimilation Hydrological modelling ENKF Uncertain observation

Abstract: Accurate flood predictions are essential to reduce socioeconomic losses, but hydrological models often face limitations due to sparse measurements, model imperfections, and uncertainties in input data. Recent studies suggest that incorporating independent flood observations can help reduce these uncertainties. This study explores the optimal configuration for discharge assimilation in a spatially distributed hydrological model using the Ensemble Kalman Filter (EnKF) to update state variables in the spatially distributed LISFLOOD model. A process-based flow routing model is used to more accurately represent time delays and flow attenuation. The study involves two synthetic twin experiments in the Fraser River Basin, Canada, and the Po River Basin, Italy. It assesses the effects of different configurations of spatially distributed discharge gauges and varying filtering frequencies on simulated discharge. The results show that assimilating interior gauge data at higher frequencies improves predictions at the catchment outlet and enhances peak discharge estimations, especially during flood events, when DA was performed at sub-daily frequencies

DOI:

Year: 2025