Performance Evaluation of Large-Scale Green Roofs Based on Qualitative and Quantitative Runoff Modeling Using MUSICX

Green roofs (GR) are known as one of the most effective water-sensitive urban design (WSUD) strategies to deal with numerous environmental and social issues that urbanized cities face today. The overall quality of research on GRs has significantly improved and an increasing trend is observed in the amount of research over the last decade. Among several approaches, the application of modeling tools is observed to be an effective method to simulate and evaluate the performance of GRs. Given that studies on GRs at a catchment scale are limited, this paper aims to provide a simple but effective framework for estimating the catchment-scale impacts of GR on runoff quantity and quality. MUSICX, an Australian-developed software that possesses the advantages of a conceptual model, is chosen as the modeling tool in this study. While MUSICX has built-in meteorological templates for Australian regions, this tool also supports several climate input file formats for application by modelers in other parts of the world. This paper presents two different modeling approaches using the Land Use node and Bioretention node in MUSICX. The steps used for model calibration are also provided in this paper. The modeling results present the annual reductions in runoff volume, total suspended solid (TSS), total phosphate (TP), and total nitrogen (TN) load. The largest reductions of roughly 30% per year were observed in runoff volume and TN load. The annual runoff reduction rate reported in this study is close to that of other published results. Similar research outcomes quantifying the benefits of GRs play a major role in facilitating the widespread implementation of GRs due to the awareness of both positive and negative impacts of GRs. Future studies are recommended to concentrate on modeling the impacts of implementing GRs at a large scale (i.e., scales exceeding the single-building scale) to fill the research gaps and enhance the modeling accuracy.