Green infrastructure strategies are often cited as best practice for urban water management; however, limited research has been undertaken to compare intervention effectiveness during moderate to extreme intensity rainfall events which are typically responsible for surface water flooding. This research responds to this through applying a cellular automata-based rapid scenario screening framework to predict the flood management performance of green infrastructure strategies across an urban catchment in Melbourne City Centre (Australia). Key findings indicate an intensive application of green infrastructure could substantially reduce flood depth and velocity in the catchment but that residual risk remains, particularly during extreme flood events. The best performing intervention strategy in the study area was found to be catchment-wide decentralised rainwater capture. The research also evidences the utility of rapid scenario screening tools to complement existing flood modelling approaches through screening management strategies, exploring scenarios and engaging a wide range of multidisciplinary stakeholders.

The simulation speed of two-dimensional hydrodynamic flood models is a limiting factor when catchments are large, a considerable number of simulations is required (e.g., exploratory modeling, Monte-Carlo flood simulations, or predicting probabilistic flood maps), or when there is a need for real-time flood emergency management. Rapid Flood Models (RFMs) that rely only on topographic depressions and the water balance equation have been successfully implemented to predict maximum urban flood inundation depths within seconds to a few minutes. However, the preprocessing step (identification of depressions and their attributes) and the postprocessing step (marking up possible flow paths of flood water in between flooded depressions) of RFMs is time consuming. In this study, we developed a new fast flood inundation model based on the cellular automata (CA) approach. The new model does not require the preprocessing and postprocessing steps of RFMs and therefore can provide more simulation speed. The performance of our new model, referred to as Cellular Automata fast flood evaluation (CA-ffé), was compared to two well-known hydrodynamic flood models (HEC-RAS and TUFLOW) in 20 simulation experiments conducted in five different urban subcatchments. CA-ffé predicted maximum inundation depth with reasonable accuracy in a matter of seconds to a few minutes for a single rainfall event simulation. The CA-ffé model performed exceptionally well in areas with low-lying depressions. However, in areas where floodwaters had higher momentum and velocity, the model usually was not able to estimate inundation depths calculated by HEC-RAS or TUFLOW. CA-ffé's key drawback is also its inability to represent the temporal evolution of flooding and flow velocities. Nevertheless, its ability to provide spatial flood extents and depths in a fraction of the time compared to its hydrodynamic counterparts is a significant advancement toward exploratory approaches for water systems planning, model-based predictive control, and real-time flood management.

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