PERFORMANCE OF MIXED EXPONENTIAL AND EXPONENTIAL DISTRIBUTION REPRESENTING RAIN CELL INTENSITY IN NEYMANSCOTT RECTANGULAR PULSE (NSRP) MODEL
DOI:
https://doi.org/10.11113/mjce.v19.15743Keywords:
Neyman-Scott Rectangular Pulses (NSRP) Model, Shuffle Complex Evolution, Hourly Rainfall, AggregationAbstract
Sub-daily timescale data such as hourly data are needed for modeling urban systems. However such series are not readily available as compared to daily rainfall series. Stochastic rainfall models are useful in estimating input for design work. One of the models that applies the clustered point process theory is the Neyman-Scott Rectangular Pulses (NSRP) model. The model uses a flexible model fitting procedure which involves matching approximately a chosen set of historical statistics which exceeds in number of set of parameters to be estimated. An optimization technique called Shuffle Complex Evolution (SCE-UA) was used to estimate the parameters. The performance of NSRP model was evaluated using 10 years hourly data taken from a station in Wilayah Persekutuan. Two distributions, namely exponential (EXP) and mixed exponential (MEXP) were used to model the cell intensities in the model. The models were evaluated on a monthly basis regarding their ability to preserve the statistical properties as well as the physical properties of the rainfall time-series over timescales of 1 h, 6 h and 24 h. The performance of the models with the two different distributions was evaluated and compared. The model with the mixed exponential (MEXP) distribution perform better in preserving most of the statistical and physical properties of the observed dataReferences
Cowpertwait, P.S.P. (1991) Further developments of the Neyman-Scott clustered point process for modeling rainfall. Water Resources Research, 27:1431-1438.
Cowpertwait, P.S.P., O’Connel, P.E., Metcalfe, A.V. and Mawdsley, J.A. (1996) Stochastic point process modeling of rainfall: 1. Single-site fitting and validation. Journal of Hydrology,
:17-46.
Cowpertwait, P.S.P., O’Connell, P.E., Metcalfe, A.V. and Mawdsley, J.A. (1996) Stochastic point process modeling of rainfall: 1. Regionalization and disaggregation. Journal of
Hydrology, 175: 47-65.
Duan, Q., Sorooshian, S. and Gupta, V. (1992) Effective and efficient global optimization for
conceptual rainfall-runoff models. Water Resources Research, 28(4):1015-1031.
Entekhabi, D., Rodriguez-Iturbe, I., and Eagleson, P.S. (1989) Probabilistic representation of the temporal rainfall process by a modified Neyman-Scott rectangular pulses model: Parameter
estimation and validation. Water Resources Research, 25:295-302.
Fadhilah Y., Zalina MD.,Nguyen,V-T-V, Suhaila S. and Zulkifli Y. (2007) Fitting the best fit distribution for the hourly rainfall series in the Wilayah Persekutuan. Jurnal Teknologi,
(accepted)
Haan, C.T., Allen, D.M. and Street, J.O. (1976) A Markov Chain Model of daily rainfall. Water Resources Research, 12(3): 443-449.
Katz, R.W. (1977) Precipitation as a chain-dependent process. Journal of Applied Meteorology,
:671-676.
Nordila A., Zulkifli Y. and Zalina MD. (2006) Characterization of convective rain in Klang Valley . In Proceedings of National Conference on Water for Sustainable Development
towards a Developed Nation by 2020, Port Dickson, Negeri Sembilan, Malaysia, 13-14 July 2006.
Rodriguez-Iturbe, I., Cox, D.R. and Isham, V. (1987a) Some models for rainfall based on stochastic point processes. Proc. Roy. Soc. London Ser. A, 410(1839):269-288.
Rodriguez-Iturbe, I., Febres De Power, B. and Valdes, J. (1987b). Rectangular pulses point process models for rainfall: analysis of empirical data. Journal of Geophysical Resources,
(D8): 9645-9656.
Todorovic, P. and Woolhiser, D. (1975) A stochastic model of n-day precipitation. Journal of Applied Meteorology, 14(1): 17-24.
Woolhiser, D.A. and Roldan, J. (1982) Stochastic Daily Precipitation Models 2. A comparison of Distributions of Amounts. Water Resources Research, 18(5):1461-1468.