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<p>The roof runoff coefficient (RC) is a key design parameter for roof rainwater harvesting systems (RRHS), traditionally estimated based on standard norms, yet often with limited accuracy. This study systematically investigates the variability of RC under different roof conditions and rainfall scenarios through simulated rainfall experiments and analysis of 116 observed rainfall events. Pearson correlation analysis, response surface methodology (RSM), and linear regression modeling were employed to evaluate the effects of rainfall intensity, roof slope, material, and area. Results indicate that rainfall intensity is the primary controlling factor of RC, with roof slope exerting a secondary but significant influence. A linear regression model based on rainfall depth (<i>H</i>), RC = 0.86 − 0.48 × 0.94<sup><i>H</i></sup>, was established, achieving a high predictive reliability with a Nash-Sutcliffe efficiency coefficient of 0.927. Application of this model in RRHS design optimized the required storage volume by 50.79% compared to conventional methods, while maintaining high operational efficiency and water supply performance. This study provides a scalable, data-driven approach for determining roof runoff coefficients, offering a robust technical basis for the efficient utilization and management of urban rainwater resources.</p>

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Methodology for Determining Runoff Coefficients Based on Rainfall Depth and Sensitivity Analysis of Influencing Factors

  • 期刊论文

  • 发表日期：

    2026年5月28日

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  • Hao Yu,
  • Fei Han,
  • Wei Zhang

摘要

The roof runoff coefficient (RC) is a key design parameter for roof rainwater harvesting systems (RRHS), traditionally estimated based on standard norms, yet often with limited accuracy. This study systematically investigates the variability of RC under different roof conditions and rainfall scenarios through simulated rainfall experiments and analysis of 116 observed rainfall events. Pearson correlation analysis, response surface methodology (RSM), and linear regression modeling were employed to evaluate the effects of rainfall intensity, roof slope, material, and area. Results indicate that rainfall intensity is the primary controlling factor of RC, with roof slope exerting a secondary but significant influence. A linear regression model based on rainfall depth (H), RC = 0.86 − 0.48 × 0.94H, was established, achieving a high predictive reliability with a Nash-Sutcliffe efficiency coefficient of 0.927. Application of this model in RRHS design optimized the required storage volume by 50.79% compared to conventional methods, while maintaining high operational efficiency and water supply performance. This study provides a scalable, data-driven approach for determining roof runoff coefficients, offering a robust technical basis for the efficient utilization and management of urban rainwater resources.