Formulation of Parsimonious Urban Flash Flood Predictive Model with Inferential Statistics

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%.

Automated summary

The curve number (CN) rainfall-runoff model has been found to have limitations in consistently predicting runoff results. This study presents a calibrated model that is statistically significant and does not rely on return period data. The research also highlights the importance of not solely relying on land-use and land cover when selecting CN values.