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Improving Green Roof Runoff Modeling for Sustainable Cities: The Role of Site-Specific Calibration in SCS-CN Parameters

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  • Thiago Masaharu Osawa

    (Department of Hydraulic and Environmental Engineering, University of Sao Paulo, Professor Almeida Prado Ave., 83 Jardim Universidade, Sao Paulo 05508-070, SP, Brazil)

  • Fabio Ferreira Nogueira

    (Department of Hydraulic and Environmental Engineering, University of Sao Paulo, Professor Almeida Prado Ave., 83 Jardim Universidade, Sao Paulo 05508-070, SP, Brazil)

  • Brenda Chaves Coelho Leite

    (Department of Civil Construction Engineering, University of Sao Paulo, Professor Almeida Prado Ave., 83 Jardim Universidade, Sao Paulo 05508-070, SP, Brazil)

  • José Rodolfo Scarati Martins

    (Department of Hydraulic and Environmental Engineering, University of Sao Paulo, Professor Almeida Prado Ave., 83 Jardim Universidade, Sao Paulo 05508-070, SP, Brazil)

Abstract

Green roofs are increasingly recognized as effective Nature-Based Solutions (NBS) for urban stormwater management, contributing to sustainable and climate-resilient cities. The Soil Conservation Service Curve Number (SCS-CN) model is commonly used to simulate their hydrological performance due to its simplicity and low data requirements. However, the standard assumption of a fixed initial abstraction ratio (Ia/S = 0.2), long debated in hydrology, has been largely overlooked in green roof applications. This study investigates the variability of Ia/S and its impact on runoff simulation accuracy for a green roof under a humid subtropical climate. Event-based analysis across multiple storms revealed Ia/S values ranging from 0.01 to 0.62, with a calibrated optimal value of 0.17. This variability is primarily driven by the physical and biological characteristics of the green roof rather than short-term rainfall conditions. Using the fixed ratio introduced consistent biases in runoff estimation, while intermediate ratios (0.17–0.22) provided higher accuracy, with the optimal ratio yielding a median Curve Number (CN) of 89 and high model performance (NSE = 0.95). Additionally, CN values followed a positively skewed Weibull distribution, highlighting the value of probabilistic modeling. Though limited to one green roof design, the findings underscore the importance of site-specific parameter calibration to improve predictive reliability. By enhancing model accuracy, this research supports better design, evaluation, and management of green roofs, reinforcing their contribution to integrated urban water systems and global sustainability goals.

Suggested Citation

  • Thiago Masaharu Osawa & Fabio Ferreira Nogueira & Brenda Chaves Coelho Leite & José Rodolfo Scarati Martins, 2025. "Improving Green Roof Runoff Modeling for Sustainable Cities: The Role of Site-Specific Calibration in SCS-CN Parameters," Sustainability, MDPI, vol. 17(13), pages 1-16, June.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:13:p:5976-:d:1690357
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    References listed on IDEAS

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    1. Manso, Maria & Teotónio, Inês & Silva, Cristina Matos & Cruz, Carlos Oliveira, 2021. "Green roof and green wall benefits and costs: A review of the quantitative evidence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    2. P. Singh & B. Yaduvanshi & Swati Patel & Saswati Ray, 2013. "SCS-CN Based Quantification of Potential of Rooftop Catchments and Computation of ASRC for Rainwater Harvesting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2001-2012, May.
    3. Stefano Cascone, 2019. "Green Roof Design: State of the Art on Technology and Materials," Sustainability, MDPI, vol. 11(11), pages 1-27, May.
    4. Vijay P. Santikari & Lawrence C. Murdoch, 2019. "Accounting for Spatiotemporal Variations of Curve Number Using Variable Initial Abstraction and Antecedent Moisture," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 641-656, January.
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