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

Thiago Masaharu Osawa (), Fabio Ferreira Nogueira, Brenda Chaves Coelho Leite and José Rodolfo Scarati Martins
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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

Sustainability, 2025, vol. 17, issue 13, 1-16

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.

Keywords: green roof hydrology; hydrological modeling; initial abstraction ratio; nature-based solutions; SCS-CN model; sustainable stormwater management (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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