Evaluating the cost–benefit of LID strategies for urban surface water flooding based on risk management

The changing climate and rapid urbanization have resulted in an increase in urban floods. It is crucial to develop and improve flood management measures in urban areas to minimize losses. The sponge city strategy, which is based on the concept of Low Impact Development (LID), has proven effective in mitigating urban surface water floods and improving water management. However, there is still a need to determine the scope of flood improvement based on flood risk assessment and consider the cost-effectiveness of improvement measures. This study addresses this gap by developing a rapid cost–benefit assessment method for LID measures using cellular automata. The study area was Waterloo, and potential LID measures such as green roofs, permeable pavements, and bio-retention facilities were chosen. LID mitigation scenarios were generated based on the risk distribution of buildings. The CADDIES model, which is based on cellular automata, was used to simulate urban surface floods. The study found that the submerged area of flood water depth greater than 0.3 m increased logarithmically with the rainfall return period, while areas with depths greater than 0.6 m and 0.9 m increased linearly. Implementation of all three LID scenarios had the best effect on flood control, with scenario S3 showing the highest economic benefit and scenario S12 demonstrating the best investment return. When only the green roofs measure was implemented, the recommended renovation area accounted for 30%-50% of the total building area. If the green roofs measure was combined with the other two measures, the renovation area should be as small as possible. The study has shown that this method efficiently selects an investment strategy with favorable cost–benefit outcomes, thereby assisting decision-making in the planning of sponge city construction. This practical and effective approach offers scientific support for the sustainable management of urban floods.