A data-parsimonious workflow for rapid pluvial flood modelling in data-scarce cities: Benchmarking a 2D weighted cellular automata model against a physically-based coupled 1D-2D model

Reliable and computationally efficient urban flood modelling approaches are essential for evaluating adaptation and response options under climate change, rapid urbanisation, and infrastructure deterioration. However, conventional physically based hydrodynamic models require extensive hydro-meteorological and hydraulic data for calibration and validation, which are often limited in data-scarce environments. This study presents and evaluates a practical workflow for rapid pluvial flood modelling that integrates simplified event-scale rainfall-loss estimation, calibration using post-event flood depth observations and a computationally efficient weighted two-dimensional Cellular Automata (2DCA) model. Effective rainfall derived from synthetic design storms and the 26 March 2025 extreme rainfall event in Kampala city, Uganda were used as forcing inputs to the 2DCA model and the resulting flood simulations were benchmarked against a calibrated and validated coupled 1D–2D PCSWMM model. The proposed workflow reproduces 90–98% of the flooded area, with simulated flood depths showing strong agreement with the coupled model (NSE = 0.958–0.995; RMSE = 0.103–0.177 m). The proposed workflow enables rapid and physically plausible city-scale flood assessments using minimal data and provides a practical pathway for flood hazard mapping and future development of flood early warning systems in data-scarce cities.