New Regional-Specific Urban Runoff Prediction Model of Sungai Kayu Ara Catchment in Malaysia

Application programs; Catchments; Floods; Rain; Sewage; Bootstrapping; Curve numbers; Drainage infrastructure; Inferential statistics; Rainfall-runoff characteristics; Rainfall-runoff events; Rainfall-runoff modeling; Runoff prediction; Runoff

Saved in:
Bibliographic Details
Main Authors: Ling L., Chow M.F., Tan W.L., Tan W.J., Tan C.Y., Yusop Z.
Other Authors: 56203785300
Format: Book Chapter
Published: Springer 2023
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uniten.dspace-25762
record_format dspace
spelling my.uniten.dspace-257622023-05-29T16:13:59Z New Regional-Specific Urban Runoff Prediction Model of Sungai Kayu Ara Catchment in Malaysia Ling L. Chow M.F. Tan W.L. Tan W.J. Tan C.Y. Yusop Z. 56203785300 57214146115 55804400500 57211825201 57217700001 6507841909 Application programs; Catchments; Floods; Rain; Sewage; Bootstrapping; Curve numbers; Drainage infrastructure; Inferential statistics; Rainfall-runoff characteristics; Rainfall-runoff events; Rainfall-runoff modeling; Runoff prediction; Runoff Malaysian government agencies adopted curve number (CN) rainfall-runoff model for design use like many other commercial software applications, while most researchers have adopted CN values from its published handbook from USA. However, there is no regional-specific curve numbers handbook in Malaysia for the rainfall-runoff predictive modelling. This study did not refer to any CN value but derived a statistically significant CN value with rainfall-runoff events directly. The derived ? = 0.0002 is statistically significant (? = 0.01), while the optimum CN value of 92.95 represents the rainfall-runoff characteristic at the Sungai Kayu Ara catchment. The runoff predictive model estimated an averaged flood depth of 7.46�cm from 100�mm rainfall event when the drainage infrastructure fails to drain away the runoff volume effectively. It is recommended to limit the upstream development, while rainwater harvesting, storm water retention, and detention facilities should be constructed to curb the urban flash flooding at the Sungai Kayu Ara catchment. � Springer Nature Singapore Pte Ltd 2020. Final 2023-05-29T08:13:58Z 2023-05-29T08:13:58Z 2020 Book Chapter 10.1007/978-981-15-1193-6_18 2-s2.0-85080854568 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080854568&doi=10.1007%2f978-981-15-1193-6_18&partnerID=40&md5=a2fdc040ae5e0e4eaf6ad4902b2561c9 https://irepository.uniten.edu.my/handle/123456789/25762 59 161 168 Springer Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Application programs; Catchments; Floods; Rain; Sewage; Bootstrapping; Curve numbers; Drainage infrastructure; Inferential statistics; Rainfall-runoff characteristics; Rainfall-runoff events; Rainfall-runoff modeling; Runoff prediction; Runoff
author2 56203785300
author_facet 56203785300
Ling L.
Chow M.F.
Tan W.L.
Tan W.J.
Tan C.Y.
Yusop Z.
format Book Chapter
author Ling L.
Chow M.F.
Tan W.L.
Tan W.J.
Tan C.Y.
Yusop Z.
spellingShingle Ling L.
Chow M.F.
Tan W.L.
Tan W.J.
Tan C.Y.
Yusop Z.
New Regional-Specific Urban Runoff Prediction Model of Sungai Kayu Ara Catchment in Malaysia
author_sort Ling L.
title New Regional-Specific Urban Runoff Prediction Model of Sungai Kayu Ara Catchment in Malaysia
title_short New Regional-Specific Urban Runoff Prediction Model of Sungai Kayu Ara Catchment in Malaysia
title_full New Regional-Specific Urban Runoff Prediction Model of Sungai Kayu Ara Catchment in Malaysia
title_fullStr New Regional-Specific Urban Runoff Prediction Model of Sungai Kayu Ara Catchment in Malaysia
title_full_unstemmed New Regional-Specific Urban Runoff Prediction Model of Sungai Kayu Ara Catchment in Malaysia
title_sort new regional-specific urban runoff prediction model of sungai kayu ara catchment in malaysia
publisher Springer
publishDate 2023
_version_ 1806424225826734080
score 13.214268