Formulation of Parsimonious Urban Flash Flood Predictive Model with Inferential Statistics

Lloyd Ling, Sai Hin Lai, Zulkifli Yusop, Ren Jie Chin and Joan Lucille Ling
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Lloyd Ling: Centre of Disaster Risk Reduction (CDRR), Civil Engineering Department, Lee Kong Chian Faculty of Engineering & Science, Universiti Tunku Abdul Rahman, Jalan Sungai Long, Kajang 43000, Malaysia
Sai Hin Lai: Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
Zulkifli Yusop: Centre for Environmental Sustainability and Water Security, Universiti Teknologi Malaysia, Skudai 81310, Malaysia
Ren Jie Chin: Centre of Disaster Risk Reduction (CDRR), Civil Engineering Department, Lee Kong Chian Faculty of Engineering & Science, Universiti Tunku Abdul Rahman, Jalan Sungai Long, Kajang 43000, Malaysia
Joan Lucille Ling: American Degree Programme, Department of Liberal Arts and Sciences, Taylor’s University, No. 1, Jalan Taylors, Subang Jaya 47500, Malaysia

Mathematics, 2022, vol. 10, issue 2, 1-18

Abstract: The curve number (CN) rainfall–runoff model is widely adopted. However, it had been reported to repeatedly fail in consistently predicting runoff results worldwide. Unlike the existing antecedent moisture condition concept, this study preserved its parsimonious model structure for calibration according to different ground saturation conditions under guidance from inferential statistics. The existing CN model was not statistically significant without calibration. The calibrated model did not rely on the return period data and included rainfall depths less than 25.4 mm to formulate statistically significant urban runoff predictive models, and it derived CN directly. Contrarily, the linear regression runoff model and the asymptotic fitting method failed to model hydrological conditions when runoff coefficient was greater than 50%. Although the land-use and land cover remained the same throughout this study, the calculated CN value of this urban watershed increased from 93.35 to 96.50 as the watershed became more saturated. On average, a 3.4% increase in CN value would affect runoff by 44% (178,000 m 3 ). This proves that the CN value cannot be selected according to the land-use and land cover of the watershed only. Urban flash flood modelling should be formulated with rainfall–runoff data pairs with a runoff coefficient > 50%.

Keywords: curve number; flash flood model; inferential statistics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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