Real-time monitoring of urban flooding by ensemble Kalman filter

ABSTRACT

The graphical abstract of the SWMM-DA framework integrating the physics-based SWMM with real-time data assimilation (DA).

Real-time monitoring of urban flooding is challenging, as the required lead times are short and uncertainties in both hydrodynamic models and meteorological inputs substantially undermine monitoring accuracy. In particular, the complex networks of urban drainage systems (UDS) — governed by numerous physical parameters — make it difficult to maintain reliable monitoring performance during unprecedented storm events. In this study, we applied an Ensemble Kalman Filter (EnKF) to the widely used Storm Water Management Model (SWMM) to reduce these uncertainties. The proposed SWMM with data assimilation (SWMM-DA) system assimilates real-time sewer water level observations to sequentially update both model states and parameters. We evaluated the SWMM-DA system across, several severe storm events comparing its performance against a baseline SWMM using an existing manually calibrated parameter set. The key findings indicate that (1) jointly updating model states and parameters significantly improves urban flood monitoring accuracy without requiring prior parameter calibration from long-term historical observations, (2) shorter EnKF update windows are essential to maximize the potential of sequential data assimilation, demonstrating the feasibility of physics-based UDS models for real-time applications, and (3) the EnKF effectively compensates for model uncertainties by dynamically adjusting physical SWMM parameters.