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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 accord...

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Main Authors: Ling, Lloyd, Lai, Sai Hin, Yusop, Zulkifli, Chin, Ren Jie, Ling, Joan Lucille
Format: Article
Language:English
Published: MDPI 2022
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/103104/1/ZulkifliYusop2022_FormulationofParsimoniousUrbanFlashFlood.pdf
http://eprints.utm.my/103104/
http://dx.doi.org/10.3390/math10020175
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spelling my.utm.1031042023-10-12T09:31:35Z http://eprints.utm.my/103104/ Formulation of parsimonious urban flash flood predictive model with inferential statistics Ling, Lloyd Lai, Sai Hin Yusop, Zulkifli Chin, Ren Jie Ling, Joan Lucille TA Engineering (General). Civil engineering (General) 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 m3). 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%. MDPI 2022-01-02 Article PeerReviewed application/pdf en http://eprints.utm.my/103104/1/ZulkifliYusop2022_FormulationofParsimoniousUrbanFlashFlood.pdf Ling, Lloyd and Lai, Sai Hin and Yusop, Zulkifli and Chin, Ren Jie and Ling, Joan Lucille (2022) Formulation of parsimonious urban flash flood predictive model with inferential statistics. Mathematics, 10 (2). pp. 1-18. ISSN 2227-7390 http://dx.doi.org/10.3390/math10020175 DOI:10.3390/math10020175
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Ling, Lloyd
Lai, Sai Hin
Yusop, Zulkifli
Chin, Ren Jie
Ling, Joan Lucille
Formulation of parsimonious urban flash flood predictive model with inferential statistics
description 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 m3). 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%.
format Article
author Ling, Lloyd
Lai, Sai Hin
Yusop, Zulkifli
Chin, Ren Jie
Ling, Joan Lucille
author_facet Ling, Lloyd
Lai, Sai Hin
Yusop, Zulkifli
Chin, Ren Jie
Ling, Joan Lucille
author_sort Ling, Lloyd
title Formulation of parsimonious urban flash flood predictive model with inferential statistics
title_short Formulation of parsimonious urban flash flood predictive model with inferential statistics
title_full Formulation of parsimonious urban flash flood predictive model with inferential statistics
title_fullStr Formulation of parsimonious urban flash flood predictive model with inferential statistics
title_full_unstemmed Formulation of parsimonious urban flash flood predictive model with inferential statistics
title_sort formulation of parsimonious urban flash flood predictive model with inferential statistics
publisher MDPI
publishDate 2022
url http://eprints.utm.my/103104/1/ZulkifliYusop2022_FormulationofParsimoniousUrbanFlashFlood.pdf
http://eprints.utm.my/103104/
http://dx.doi.org/10.3390/math10020175
_version_ 1781777645846921216
score 13.211869

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