Statistical Modeling for One Hour Rainfall Data in Kuala Lumpur and Selangor

Amanda Lee Sean Peik; Choong Wee Kang; Andy Chan

doi:10.4028/www.scientific.net/AMR.1030-1032.665

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1030-1032Statistical Modeling for One Hour Rainfall Data in...

Statistical Modeling for One Hour Rainfall Data in Kuala Lumpur and Selangor

Abstract:

The purpose of this study is to assess patterns of extreme rainfall and this study focused on the changes between two phases for extreme rainfall, for the period of 1971 to 2011 and from 1995 to 2011 in Kuala Lumpur and Selangor. The generalised extreme value distribution appears to outperform other distribution functions such as two-parameter Gumbel and lognormal and the three-parameter generalized extreme value (GEV), lognormal (LN3) and log Pearson (LP3) in modeling the one-hour annual maximum rainfall series from 14 stations. The estimated return period of 20, 50, 100-year for each stations based on the best fitting model for the periods of entire record data and from 1995-2011 have been computed. More than 70% of estimated quantiles using rainfall data from 1995-2011 are higher compared to estimation using the entire recorded data.

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Periodical:

Advanced Materials Research (Volumes 1030-1032)

Pages:

665-668

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1030-1032.665

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Online since:

September 2014

Authors:

Amanda Lee Sean Peik, Choong Wee Kang*, Andy Chan

Keywords:

Extreme Rainfall, Frequency Analysis, Rainfall Patterns, Statistical Distributions

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References

[1] Kundzewicz, Z. W., Rosbjerg, D., Simonovic, S. P. & Takeuchi, K., 1993. Extreme hydrological events in perspective. Yokohama, IAHS Publication.

Google Scholar

[2] Mohamad, M. F., 2013. Key challenges in urban flood management; knowledges, experiences and policy in Malaysian perspectives. Daegu: s. n.

Google Scholar

[3] Desa, M. N. M., Noriah, A. B. & Rakhecha, P. R., 2001. Probable maximum precipitation for 24 h duration over southeast Asian monsoon region—Selangor, Malaysia. Atmospheric Research, Volume 58, pp.41-54.

DOI: 10.1016/s0169-8095(01)00070-9

Google Scholar

[4] Lee, C. Y., 2005. Application of rainfall frequency analysis on studying rainfall distribution characteristics of Chia-Nan plain area in southern Taiwan. Crop, Environment & Bioinformatics, Volume 2, pp.31-38.

Google Scholar

[5] Zalina, M., Desa, M., Nguyen, V., Kassim, A., 2002. Selecting a probability distribution for extreme rainfall series in Malaysia. Water Sci. Technol., 45(2), pp.63-68.

DOI: 10.2166/wst.2002.0028

Google Scholar

[6] Vogel, R. M., Thomas Jr., W. O. & McMahon, T. A., 1993. Flood flow frequency model selection in Southwestern United States. Journal of Water Resources Planning and Management, 119(3), pp.353-366.

DOI: 10.1061/(asce)0733-9496(1993)119:3(353)

Google Scholar

[7] Laio, F., 2004. Cramer-von Mises and Anderson-Darling goodness of fit tests for extreme value distributions with unknown parameters. Water Resources Research, Volume 40, pp. W09308-W09318.

DOI: 10.1029/2004wr003204

Google Scholar