4.8 Article

Extending the Rational Method for assessing and developing sustainable urban drainage systems

Journal

WATER RESEARCH
Volume 144, Issue -, Pages 112-125

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.watres.2018.07.022

Keywords

Stormwater; Storm sewer; Runoff control; Peak flow rate; Integrated design

Funding

  1. Science & Technology Commission of Shanghai Municipality [15dz1207806]
  2. Key Laboratory of Cities' Mitigation and Adaptation to Climate Change in Shanghai (CMACC)

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Onsite runoff control is considered an important part of sustainable urban drainage schemes, but estimating the maximum runoff flow rate from a catchment with onsite runoff controls remains controversial. Runoff controls complicate the issue by dividing the catchment into several subcatchments that feed into individual runoff controls, which dynamically regulate the catchment imperviousness. Rational Method (RM) is the most-employed technique to determine maximum flow rates for designing urban drainage infrastructures, but it cannot handle such conditions. Nonetheless, it has advantages over alternative methods in terms of principle from the urban drainage design perspective. This work develops Rational Method Prime (RMP) that follows the basic principle of RM but instead recalculates catchment variables by taking into account runoff control effects and evaluates runoff control efficiencies by using two indices. RMP has three merits: (1) providing an integrated response of the whole catchment with runoff controls; (2) interpreting runoff control effects by plotting runoff flow rate-rainfall duration curves; (3) connecting the design of runoff controls and storm sewers that are based on different design principles and rainfall statistics. Case study results showed that runoff controls reduced peak flow rates by 5.83-91.6%, corresponding to reduction factors for return period of maximum flow rate from 0.04 to 0.76. Indeed, the original RM is based on four assumptions, which also cause its weakness, and there have been current methods to address 3 of them. RMP contributes to addressing the last assumption (i.e. constant catchment imperviousness), which finally allowing the evolution from RM 1.0 to 2.0. (C) 2018 Elsevier Ltd. All rights reserved.

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