Claim Assessment of a Rainfall Runoff Model with Bootstrap

  • Wen Jia Tan
  • Lloyd LingEmail author
  • Zulkifli Yusop
  • Yuk Feng Huang
Conference paper


Since the inception in 1954, researchers started to scrutinise the United States Department of Agriculture (USDA) Soil Conservation Services (SCS) rainfall runoff model with different field data after the model produced inconsistent runoff prediction results throughout the world. This paper re-assessed two key hypotheses used by SCS where Ia = 0.2S and λ = 0.2 as a constant. The 112 original SCS data points were used to re-determine the correlation between Ia and S with Bootstrapping, BCa procedure. Both key hypotheses of SCS were proven to be statistical in-significant. Inferential statistics deduced that Ia ≠ 0.2S while λ is neither equal to 0.2 nor a constant at alpha = 0.01 level. Both hypotheses are not even applicable to the original dataset used by then SCS to formulate the rainfall runoff model. Ia = 0.112S fitted SCS original data points better at alpha = 0.01 level. The 1954 SCS proposal of Ia = 0.2S and λ = 0.2 committed type II error as pertain to its own dataset. Therefore, SCS rainfall runoff model cannot be blindly adopted. Practitioners of this model are encouraged to validate and derive regional specific relationship between Ia and S.


Bootsrapping Non-parametric inferential statistics Runoff prediction SCS 


  1. 1.
    Jiang, R.Y.: Investigation of runoff curve number initial abstraction ratio. Master Thesis, University of Arizona, Tucson, AZ, USA (2001)Google Scholar
  2. 2.
    Hawkins, R.H., Ward, T.J., Woodward, D.E., VanMullem, J.A.: Curve Number Hydrology: State of Practice. Reston, ASCE (2009)Google Scholar
  3. 3.
    Ling, L., Yusop, Z.: A micro focus with macro impact: exploration of initial abstraction coefficient ratio (λ) in Soil Conservation Curve Number (CN) methodology. In: IOP Conference Series: Earth Environment Science, (1) (2013)Google Scholar
  4. 4.
    Natural Resources Conservation Service (NRCS), National engineering handbook Part 630: Hydrology. U.S. Department of Agriculture, Washington, DC (1972)Google Scholar
  5. 5.
    Natural Resources Conservation Service (NRCS), National engineering handbook: Hydrology, USDA, Washington, DC (2004)Google Scholar
  6. 6.
    Rallison, R.E.: Origin and evolution of the SCS Runoff Equation. In: Symposium on Watershed Management, 21–23 July, Boise, Idaho, pp. 912–924 (1980)Google Scholar
  7. 7.
    IBM, SPSS Bootstrapping 21 guide. IBM Press (2012)Google Scholar
  8. 8.
    Hawkins, R.H., Khojeini, A.V.: Initial abstraction and loss in the curve number method. In: Arizona State Hydrological Society Proceedings, 15–17 April. Las Vegas, Nevada, pp. 115–119. Hjelmfelt, A. T. (1980)Google Scholar
  9. 9.
    Hjelmfelt, A.T.: Empirical investigation of curve number technique. J. Hydr. Eng. Div. ASCE 106(9), 1471–1476 (1980)Google Scholar
  10. 10.
    Hawkins, R.H., Ward, T.J., Woodward, D.E., Vanmullem, J.A.: Progress report: ASCE task committee on curve number hydrology. Managing Watersheds for Human and Natural Impacts, pp. 1–12. ASCE, New York (2005)Google Scholar
  11. 11.
    Soulis, K.X., Valiantzas, J.D.: Identification of the SCS-CN parameter spatial distribution using rainfall-runoff data in heterogeneous watersheds. Water Resour. Manage. 27(6), 1737–1749 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Wen Jia Tan
    • 1
  • Lloyd Ling
    • 1
    Email author
  • Zulkifli Yusop
    • 1
    • 2
  • Yuk Feng Huang
    • 1
  1. 1.Centre for Disaster Risk Reduction, Department of Civil Engineering, Lee Kong Chian Faculty of Engineering & ScienceUniversiti Tunku Abdul Rahman. Jalan Sungai Long, Bandar Sungai LongKajangMalaysia
  2. 2.Centre for Environmental Sustainability and Water Security, Research Institute for Sustainable Environment, Faculty of Civil Engineering DepartmentUniversiti Teknologi MalaysiaSkudaiMalaysia

Personalised recommendations