Increased urbanisation is reducing the porous green spaces of cities worldwide. Furthermore, population growth and changes in rainfall due to climate change are placing significant stress on existing sewer systems that drain storm and wastewater from cities. Subsequently, surface flooding has increased, triggering global concerns due to the threats that flooding poses not only to human wellbeing and property safety, but also to economic and social development. Green infrastructures (GIs) were one of the first strategies proposed to mitigate flooding risks sustainably. This approach aims to maximise the opportunities and benefits of surface water management by mimicking the natural drainage processes of an area. However, this maximisation is not easy to achieve due to various technical and administrative barriers that prevent the adoption of GI retrofit schemes.
This dissertation addresses some of these technical and institutional barriers by discussing the full-scale performance of GI technologies retrofitted in London. Furthermore, the thesis analyses rain garden performance and the decision-making system necessary to promote GI uptake in long-term planning. A micro-monitoring system capable of recording the performance of individual GI components was also developed and implemented for this study.
First, (GRR) was computed for the catchment under investigation, and then this parameter was used to assign a target performance value (Qgrr/Qin). The lined rain garden system considered in this study showed good performance in terms of peak flow reduction; it reduced runoff rate by 83–86% in the reported events. However, the performance was poorer in terms of runoff volume reduction; the rain garden reduced the runoff volume by an average of 55% for 50% of the considered events. This reduction was below the 68% target corresponding to the 30-year storm. In terms of the lag-time ratio, the rain garden performance was around 2, which was less than the anticipated value of 3.
Second, the GI decision-making process was analysed using a systems approach by applying soft system methodology and the analytic network process. Third, the thesis presents a unique set of performance indicators, which were derived by coupling the concept of GRR and probability plots. These indicators can be adopted as a benchmarking system to define an acceptable level of performance for any similar GI design.
Fourth, multi-scale modelling was applied. The modelling process started first by a conceptual model for the GI unit and then a street-scale model, which was validated before using its output in a catchment scale analysis. The catchment scale model was used to assess different GI uptake scenarios. An allocation algorithm was coupled with the GI module in InfoWorks ICM to aggregate the effect of wide GI implementation in the catchment. The findings of this thesis were communicated to water utility management companies to help them better utilise GI in their long-term plans.
Finally, the thesis establishes a course for further research in increasing GI uptake from a bottom-up approach. This research would build upon the work done in this thesis to achieve higher objectives.