Performance analysis of a stormwater green infrastructure model for flow and water quality predictions

Harsha S. Fowdar, Teck Heng Neo, Say Leong Ong, Jiangyong Hu, David T. McCarthy

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)

Abstract

Nature-based solutions or Green infrastructure (GI) used for managing stormwater pollution are growing in popularity across the globe. Stormwater GI models are important tools to inform the planning of these systems (type, design, size), in the most efficient and cost-effective manner. MUSIC, an example of such a tool, uses regression and first order decay models. Studies validating MUSIC model performance are, however, scarce, hindering future model development and transferability of the model for systems operating under different design and climatic conditions. To close this gap, this paper evaluates MUSIC for a field scale bioretention system, stormwater wetland and vegetated swale operating under Singapore tropical climate. The treatment modules were able to simulate outflows and effluent pollutant concentrations reasonably well for cumulative event volumes (mostly within ±25%) and cumulative TP and TN loads (within ±30%). Outflow TSS loads were significantly under-estimated as a result of greater variability in measured TSS concentrations across events. The findings indicate that simple empirical models such as MUSIC can be transferred to different regions provided that management decisions are based on long-term modelling efforts. The modules generally simulated the outflow hydrographs and pollutographs of the different inflow and drying/wetting conditions relatively poorly.

Original languageEnglish
Article number115259
Number of pages12
JournalJournal of Environmental Management
Volume316
DOIs
Publication statusPublished - 15 Aug 2022

Keywords

  • Biofiltration
  • Hydrology
  • Model calibration
  • MUSIC
  • Stormwater
  • Water quality

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