The Collective Visual Representation of Rainfall-Runoff Difference Model
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.
KeywordsBootstrapping PASW Non-parametric inferential statistics Numerical analysis SCS
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).
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