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Statistical and Type II Error Assessment of a Runoff Predictive Model in Peninsula Malaysia

Author

Listed:
  • 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)

  • Zulkifli Yusop

    (Centre for Environmental Sustainability and Water Security, Universiti Teknologi Malaysia, Skudai 81310, Malaysia)

  • Joan Lucille Ling

    (Department of Liberal Arts and Languages, American Degree Programme, Taylor’s University, Jalan Taylors, Subang Jaya 47500, Malaysia)

Abstract

Flood related disasters continue to threaten mankind despite preventative efforts in technological advancement. Since 1954, the Soil Conservation Services (SCS) Curve Number (CN 0.2 ) rainfall-runoff model has been widely used but reportedly produced inconsistent results in field studies worldwide. As such, this article presents methodology to reassess the validity of the model and perform model calibration with inferential statistics. A closed form equation was solved to narrow previous research gap with a derived 3D runoff difference model for type II error assessment. Under this study, the SCS runoff model is statistically insignificant (alpha = 0.01) without calibration. Curve Number CN 0.2 = 72.58 for Peninsula Malaysia with a 99% confidence interval range of 67 to 76. Within these CN 0.2 areas, SCS model underpredicts runoff amounts when the rainfall depth of a storm is < 70 mm. Its overprediction tendency worsens in cases involving larger storm events. For areas of 1 km 2 , it underpredicted runoff amount the most (2.4 million liters) at CN 0.2 = 67 and the rainfall depth of 55 mm while it nearly overpredicted runoff amount by 25 million liters when the storm depth reached 430 mm in Peninsula Malaysia. The SCS model must be validated with rainfall-runoff datasets prior to its adoption for runoff prediction in any part of the world. SCS practitioners are encouraged to adopt the general formulae from this article to derive assessment models and equations for their studies.

Suggested Citation

  • Lloyd Ling & Zulkifli Yusop & Joan Lucille Ling, 2021. "Statistical and Type II Error Assessment of a Runoff Predictive Model in Peninsula Malaysia," Mathematics, MDPI, vol. 9(8), pages 1-24, April.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:8:p:812-:d:532307
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    References listed on IDEAS

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    3. Boyu Feng & Jinfei Wang & Ying Zhang & Brent Hall & Chuiqing Zeng, 2020. "Urban flood hazard mapping using a hydraulic–GIS combined model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 100(3), pages 1089-1104, February.
    Full references (including those not matched with items on IDEAS)

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