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Australia’s rapid urbanization, compounded by its increasingly unpredictable climate, underscores the urgent need for robust methodologies to assess the performance of green roofs (GRs) in mitigating urban stormwater runoff. A major barrier to widespread GR adoption is the lack of calibrated hydrological models capable of accurately predicting GR performance under diverse climatic and design conditions in different regions. The complexity of GR systems, characterized by nonlinear water flow dynamics and multilayered substrate interactions, presents a significant challenge for effective performance modeling. This study conducts a comparative analysis of four established hydrological models: URBS, SWMM, HYDRUS-1D, and MIKE SHE to evaluate their applicability for simulating GR hydrological response in Australian urban environments. As a preliminary reconnaissance step, these models were tested using a synthetic unit hydrograph (100 mm peak rainfall over one hour), providing baseline performance metrics and establishing a methodological foundation for subsequent analysis of real field data collected over a three-year period. Key governing equations were implemented, including conceptual storage-routing (URBS), Green-Ampt infiltration formulations (SWMM), Richards’ equation for variably saturated flow (HYDRUS-1D and MIKE SHE), and Darcy’s law for porous media hydraulics. The results demonstrated consistent attenuation capacity across all models, with peak runoff reductions of 69.98–74.77 mm/h, lag times of 9–30 min, and volume reductions of 14.48–27.33 mm. However, the reliance on synthetic data, absence of antecedent moisture conditions, and lack of calibration against observed datasets limit the predictive reliability of these preliminary simulations. This analysis represents a critical first step, identifying model capabilities and limitations under controlled conditions, and informing the development of a multi-layered, calibrated modeling framework using natural datasets for enhanced accuracy and applicability in Australian contexts.

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Hydrological Models and Equations for Green Roof Performance in Australia: A Comprehensive Evaluation

  • 会议论文

  • 发表日期：

    2026年

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  • Mohammad A. Rahman,
  • David Stone,
  • Ataur Rahman,
  • Mohammad A. Alim

摘要

Australia’s rapid urbanization, compounded by its increasingly unpredictable climate, underscores the urgent need for robust methodologies to assess the performance of green roofs (GRs) in mitigating urban stormwater runoff. A major barrier to widespread GR adoption is the lack of calibrated hydrological models capable of accurately predicting GR performance under diverse climatic and design conditions in different regions. The complexity of GR systems, characterized by nonlinear water flow dynamics and multilayered substrate interactions, presents a significant challenge for effective performance modeling. This study conducts a comparative analysis of four established hydrological models: URBS, SWMM, HYDRUS-1D, and MIKE SHE to evaluate their applicability for simulating GR hydrological response in Australian urban environments. As a preliminary reconnaissance step, these models were tested using a synthetic unit hydrograph (100 mm peak rainfall over one hour), providing baseline performance metrics and establishing a methodological foundation for subsequent analysis of real field data collected over a three-year period. Key governing equations were implemented, including conceptual storage-routing (URBS), Green-Ampt infiltration formulations (SWMM), Richards’ equation for variably saturated flow (HYDRUS-1D and MIKE SHE), and Darcy’s law for porous media hydraulics. The results demonstrated consistent attenuation capacity across all models, with peak runoff reductions of 69.98–74.77 mm/h, lag times of 9–30 min, and volume reductions of 14.48–27.33 mm. However, the reliance on synthetic data, absence of antecedent moisture conditions, and lack of calibration against observed datasets limit the predictive reliability of these preliminary simulations. This analysis represents a critical first step, identifying model capabilities and limitations under controlled conditions, and informing the development of a multi-layered, calibrated modeling framework using natural datasets for enhanced accuracy and applicability in Australian contexts.