Urban flood depth estimate with a new calibrated curve number runoff prediction model

The 1954 Soil Conservation Services (SCS) runoff predictive model was adopted in engineering designs throughout the world. However, its runoff prediction reliability was under scrutiny by recent studies. The conventional curve number (CN) selection methodology is often very subjective and lacks scie...

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Main Authors: Ling, Lloyd, Yusop, Zulkifli, Chow, Ming Fai
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2020
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Online Access:http://eprints.utm.my/id/eprint/90168/1/ZulkifliYusop2020_UrbanFloodDepthEstimateWithaNewCalibrated.pdf
http://eprints.utm.my/id/eprint/90168/
http://dx.doi.org/10.1109/ACCESS.2020.2964898
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Institution: Universiti Teknologi Malaysia
Language: English
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spelling my.utm.901682021-03-31T06:21:54Z http://eprints.utm.my/id/eprint/90168/ Urban flood depth estimate with a new calibrated curve number runoff prediction model Ling, Lloyd Yusop, Zulkifli Chow, Ming Fai TA Engineering (General). Civil engineering (General) The 1954 Soil Conservation Services (SCS) runoff predictive model was adopted in engineering designs throughout the world. However, its runoff prediction reliability was under scrutiny by recent studies. The conventional curve number (CN) selection methodology is often very subjective and lacks scientific justification while nested soil group catchments complicate the issue with the risk of inappropriate curve number selection which produces unreliable runoff results. The SCS CN model was statistically invalid (α = 0.01 level) and over predicted runoff volume as much as 21% at the Sungai Kerayong catchment in Kuala Lumpur, Malaysia. Blind adoption of the model will commit a type II error. As such, this study presented a new method to calibrate and formulate an urban runoff model with inferential statistics and residual modelling technique to correct the runoff prediction results from the SCS CN model with a corrected equation. The new model out-performed the Asymptotic runoff model and SCS CN runoff model with low predictive model bias, reduced sum of squared errors by 32% and achieved high Nash-Sutcliffe efficiency value of 0.96. The derived urban curve number is 98.0 with 99% confidence interval ranging from 97.8 to 99.5 for Sungai Kerayong catchment. Twenty-five storms generated almost 29 million m3 runoff (11,548 Olympic size swimming pools) from the Sungai Kerayong catchment in this study. 75%-94% of the rain water became runoff from those storms and lost through the catchment, without efficient drainage infrastructure in place, the averaged flood depth reached 6.5 cm while the actual flood depth will be deeper at the flood ponding area near to the catchment outlet. Institute of Electrical and Electronics Engineers Inc. 2020 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/90168/1/ZulkifliYusop2020_UrbanFloodDepthEstimateWithaNewCalibrated.pdf Ling, Lloyd and Yusop, Zulkifli and Chow, Ming Fai (2020) Urban flood depth estimate with a new calibrated curve number runoff prediction model. IEEE Access, 8 . pp. 10915-10923. ISSN 2169-3536 http://dx.doi.org/10.1109/ACCESS.2020.2964898
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
Yusop, Zulkifli
Chow, Ming Fai
Urban flood depth estimate with a new calibrated curve number runoff prediction model
description The 1954 Soil Conservation Services (SCS) runoff predictive model was adopted in engineering designs throughout the world. However, its runoff prediction reliability was under scrutiny by recent studies. The conventional curve number (CN) selection methodology is often very subjective and lacks scientific justification while nested soil group catchments complicate the issue with the risk of inappropriate curve number selection which produces unreliable runoff results. The SCS CN model was statistically invalid (α = 0.01 level) and over predicted runoff volume as much as 21% at the Sungai Kerayong catchment in Kuala Lumpur, Malaysia. Blind adoption of the model will commit a type II error. As such, this study presented a new method to calibrate and formulate an urban runoff model with inferential statistics and residual modelling technique to correct the runoff prediction results from the SCS CN model with a corrected equation. The new model out-performed the Asymptotic runoff model and SCS CN runoff model with low predictive model bias, reduced sum of squared errors by 32% and achieved high Nash-Sutcliffe efficiency value of 0.96. The derived urban curve number is 98.0 with 99% confidence interval ranging from 97.8 to 99.5 for Sungai Kerayong catchment. Twenty-five storms generated almost 29 million m3 runoff (11,548 Olympic size swimming pools) from the Sungai Kerayong catchment in this study. 75%-94% of the rain water became runoff from those storms and lost through the catchment, without efficient drainage infrastructure in place, the averaged flood depth reached 6.5 cm while the actual flood depth will be deeper at the flood ponding area near to the catchment outlet.
format Article
author Ling, Lloyd
Yusop, Zulkifli
Chow, Ming Fai
author_facet Ling, Lloyd
Yusop, Zulkifli
Chow, Ming Fai
author_sort Ling, Lloyd
title Urban flood depth estimate with a new calibrated curve number runoff prediction model
title_short Urban flood depth estimate with a new calibrated curve number runoff prediction model
title_full Urban flood depth estimate with a new calibrated curve number runoff prediction model
title_fullStr Urban flood depth estimate with a new calibrated curve number runoff prediction model
title_full_unstemmed Urban flood depth estimate with a new calibrated curve number runoff prediction model
title_sort urban flood depth estimate with a new calibrated curve number runoff prediction model
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2020
url http://eprints.utm.my/id/eprint/90168/1/ZulkifliYusop2020_UrbanFloodDepthEstimateWithaNewCalibrated.pdf
http://eprints.utm.my/id/eprint/90168/
http://dx.doi.org/10.1109/ACCESS.2020.2964898
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