Advertisement

The Collective Visual Representation of Rainfall-Runoff Difference Model

  • Lloyd Ling
  • Zulkifli YusopEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9429)

Abstract

Inconsistent model prediction results were reported worldwide against SCS (now USDA) runoff model since its inception in 1954. Non parametric inferential statistics was used to reject two Null hypotheses and guided the numerical analysis optimization study to formulate a statistical significant new runoff prediction model. The technique performed regional hydrological conditions calibration to SCS base runoff model and improved runoff prediction by 27 % compared to the non-calibrated empirical model. A rainfall runoff difference model was created as a collective visual representation of runoff prediction error from the non-calibrated SCS empirical model under multiple rainfall depths and CN scenarios in Peninsula Malaysia. Statistical significant correction equations were formulated through swift data mining from the model to study the under and over-design worse case scenarios which are nearly impossible to quantify by solving the complex mathematical equation. Critical curve number concept was introduced in this study.

Keywords

Bootstrapping PASW Non-parametric inferential statistics Numerical analysis SCS 

Notes

Acknowledgments

The author would like to thank Universiti Teknologi Malaysia, Centre for Environmental Sustainability and Water Security, Research Institute for Sustainable Environment of UTM, vote no. Q.J130000.2509.07H23 and R.J130000.3009.00M41 for its financial support in this study. This study was also supported by the Asian Core Program of the Japanese Society for the Promotion of Science (JSPS) and the Ministry of Higher Education (MOHE) Malaysia. The author would also like to acknowledge the guidance provided by Prof. Richard Hawkins (University of Arizona).

References

  1. 1.
    Hawkins, R.H., Ward, T., Woodward, D.E., Van Mullem, J.: Curve Number Hydrology: State of the Practice. ASCE, Reston (2009)Google Scholar
  2. 2.
    Schneider, L., McCuen, R.H.: Statistical guidelines for curve number generation. J. Irrigation Drainage Eng. 131, 282–290 (2005)CrossRefGoogle Scholar
  3. 3.
    Hawkins, R.H.: (e-mail communication)Google Scholar
  4. 4.
    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 and Environmental Science, vol. 18, issue 1, p. 012121 (2013). doi: 10.1088/1755-1315/18/1/012121
  5. 5.
    Hydrological Procedure No. 11: Design Flood Hydrograph Estimation for Rural Catchments in Peninsula Malaysia (1994)Google Scholar
  6. 6.
    Rochoxicz, J.A. Jr.: Bootstrapping analysis, inferential statistics and EXCEL. Spreadsheets Educ. (eJSiE) 4(3), Article 4 (2011)Google Scholar
  7. 7.
    Howell, D.C.: Statistical Methods for Psychology, 6th edn. Thomson Wadsworth, Belmont (2007)Google Scholar
  8. 8.
    Wright, D.B.: Understandng Statistics: An Introduction for the Social Sciences. Sage, London (1997)Google Scholar
  9. 9.
    Ling, L., Yusop, Z.: Inferential statistics of claim assessment. In: AIP Conference Proceedings (2014). ISBN: 978-0-7354-1274-3. doi: 10.1063/1.4903675.805
  10. 10.
    Ling, L., Yusop, Z.: Inferential statistics modelling and claim re-assessment. In: ICCEMS Conference Proceedings, pp. 835–884 (2014). ISBN: 978-967-11414-7-2. http://www.iccems.com/2014/ICCEMSProcAll.pdf

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Centre for Environmental Sustainability and Water Security, Research Institute for Sustainable Environment, Faculty of Civil Engineering DepartmentUniversiti Teknologi MalaysiaSkudaiMalaysia

Personalised recommendations