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Water Resources Management

, Volume 32, Issue 2, pp 735–750 | Cite as

Influence of Rainfall, Model Parameters and Routing Methods on Stormwater Modelling

  • Yongwei Gong
  • Xiaoning Li
  • Dandan Zhai
  • Dingkun Yin
  • Ruining Song
  • Junqi Li
  • Xing Fang
  • Donghai Yuan
Article
  • 178 Downloads

Abstract

Quantification of the uncertainty associated with stormwater models should be analyzed before using modelling results to make decisions on urban stormwater control and management programs. In this study, the InfoWorks Integrated Catchment Modelling (ICM) rainfall-runoff model was used to simulate hydrographs at the outfall of a catchment (drainage area 8.3 ha, with 95% pervious areas) in Shenzhen, China. The model was calibrated and validated for two rainfall events with Nash-Sutcliffe efficiency >0.81. The influence of rainfall, model parameters and routing methods on outflow hydrograph of the catchment was systematically studied. The influence of rainfall was analyzed using generated rainfall distributions with random errors and systematic errors (± 30% offsets). Random errors had less influence than systematic errors on peak flow and runoff volume, especially for two rainfall events with larger depths and longer durations. The Monte Carlo simulations using 500 parameter sets were used to verify the equifinality of the nine model parameters and determine the prediction uncertainty. Most of the monitored flows were within the uncertainty range. The influence of two routing methods from rainfall excess to hydrograph was studied. The InfoWorks ICM model incorporating double quasilinear reservoir routing was found to have a larger effect on the simulated hydrographs for rainfall events having larger depths and longer durations than using the U.S. EPA’s Storm Water Management Model nonlinear reservoir routing method did.

Keywords

Uncertainty analysis Rainfall–runoff model InfoWorks ICM Random errors Systematic errors Routing methods uncertainty 

Notes

Acknowledgments

The study was supported by National Natural Science Foundation of China (No. 41530635 and 51109002), Beijing Higher Education Young Elite Teacher Project (YETP1645), and the General Program of Science and Technology Development Project of Beijing Municipal Education Commission of China (KM201510016005).

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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Yongwei Gong
    • 1
  • Xiaoning Li
    • 2
  • Dandan Zhai
    • 3
  • Dingkun Yin
    • 1
  • Ruining Song
    • 1
  • Junqi Li
    • 1
  • Xing Fang
    • 2
    • 4
  • Donghai Yuan
    • 1
  1. 1.Key Laboratory of Urban System and Water Environment, Ministry of EducationBeijing University of Civil Engineering and ArchitectureBeijingPeople’s Republic of China
  2. 2.Department of Civil EngineeringAuburn UniversityAuburnUSA
  3. 3.China Water Environment Group LimitedBeijingPeople’s Republic of China
  4. 4.Beijing Cooperative Innovation Research Centre on Architectural Energy Saving and Emission ReductionBeijing University of Civil Engineering and ArchitectureBeijingPeople’s Republic of China

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