Single-site modeling of rainfall based on the Bartlett-Lewis rectangular pulse model
Sub-daily time-scale data such as hourly base are important for the purpose of modeling the urban system. However, as similar data may not be readily available, a stochastic rainfall model is mandatory to generate reliable rainfall series that have similar properties as those of the observed in orde...
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QA Mathematics Lau, Kim Soon Single-site modeling of rainfall based on the Bartlett-Lewis rectangular pulse model |
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Sub-daily time-scale data such as hourly base are important for the purpose of modeling the urban system. However, as similar data may not be readily available, a stochastic rainfall model is mandatory to generate reliable rainfall series that have similar properties as those of the observed in order to estimate the input for design work in the future. In this study, one of the famous models that applied the Poisson clustered point process is the Bartlett-Lewis Rectangular Pulse Model (BLRPM) will be used to access a 10-year hourly rainfall data from Station Tele Ulu Remis, Johore, Malaysia. This model applies a flexible fitting procedure to match approximately to the historical data by an optimization technique called as Shuffle Complex Evolution (SCE). SCE algorithms is chosen for the parameters estimation by minimizing an objective function with six parameters ?, ?, ?, ?x, ? and ?. The SCE algorithm performs very well in obtaining the global optimum value and the time to get the optimum value is fast. The estimated parameters for the month of November and December were compared with Powell method for validity. Subsequently, an hourly and daily rainfall simulation based on BLRPM is carried out. The performance of the BLRPM is then evaluated on a monthly basis in term of its ability to preserve statistical and physical properties. The said properties involve rainfall series over time-scales of 1-hr and 24-hr. Results from the model evaluation have indicated that BLRPM is capable to reproduce most of the statistical and physical properties of the historical data. There are also some properties that are failed to be preserved accurately. However, the BLRPM is still capable to preserve the trend of the observed properties. |
| format |
Thesis |
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Master's degree |
| author |
Lau, Kim Soon |
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Lau, Kim Soon |
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Lau, Kim Soon |
| title |
Single-site modeling of rainfall based on the Bartlett-Lewis rectangular pulse model |
| title_short |
Single-site modeling of rainfall based on the Bartlett-Lewis rectangular pulse model |
| title_full |
Single-site modeling of rainfall based on the Bartlett-Lewis rectangular pulse model |
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Single-site modeling of rainfall based on the Bartlett-Lewis rectangular pulse model |
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Single-site modeling of rainfall based on the Bartlett-Lewis rectangular pulse model |
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single-site modeling of rainfall based on the bartlett-lewis rectangular pulse model |
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Universiti Teknologi Malaysia, Faculty of Science |
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Faculty of Science |
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2010 |
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http://eprints.utm.my/id/eprint/16334/7/LauKimSoonMFSA2010.pdf |
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my-utm-ep.163342017-09-20T04:36:23Z Single-site modeling of rainfall based on the Bartlett-Lewis rectangular pulse model 2010-11 Lau, Kim Soon QA Mathematics Sub-daily time-scale data such as hourly base are important for the purpose of modeling the urban system. However, as similar data may not be readily available, a stochastic rainfall model is mandatory to generate reliable rainfall series that have similar properties as those of the observed in order to estimate the input for design work in the future. In this study, one of the famous models that applied the Poisson clustered point process is the Bartlett-Lewis Rectangular Pulse Model (BLRPM) will be used to access a 10-year hourly rainfall data from Station Tele Ulu Remis, Johore, Malaysia. This model applies a flexible fitting procedure to match approximately to the historical data by an optimization technique called as Shuffle Complex Evolution (SCE). SCE algorithms is chosen for the parameters estimation by minimizing an objective function with six parameters ?, ?, ?, ?x, ? and ?. The SCE algorithm performs very well in obtaining the global optimum value and the time to get the optimum value is fast. The estimated parameters for the month of November and December were compared with Powell method for validity. Subsequently, an hourly and daily rainfall simulation based on BLRPM is carried out. The performance of the BLRPM is then evaluated on a monthly basis in term of its ability to preserve statistical and physical properties. The said properties involve rainfall series over time-scales of 1-hr and 24-hr. Results from the model evaluation have indicated that BLRPM is capable to reproduce most of the statistical and physical properties of the historical data. There are also some properties that are failed to be preserved accurately. However, the BLRPM is still capable to preserve the trend of the observed properties. 2010-11 Thesis http://eprints.utm.my/id/eprint/16334/ http://eprints.utm.my/id/eprint/16334/7/LauKimSoonMFSA2010.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Science Faculty of Science Burlano, P. and Rosso, R. (1991). Comment on parameter estimation and sensitivity analysis for the modified Bartlett-Lewis Rectangular Pulses Model of rainfall. J. Geophys. Res. 96(D5):9391-9395. Cheong Tau Han (2006). Developing An Integrated Program For Rainfall Simulation: Neyman Scott Rectangular Pulse Model. Christian Onof and Howard S. Wheater (1993). Modelling of British Rainfall Using A Random Parameter Bartlett-Lewis Rectangular Pulse Rainfall Model. Cowpertwait, P.S.P., P.E.O’Connell, P.E., Metcalfe, A.V. and Mawdsley, J.A. (1996a). Stochastic point process modeling of rainfall. I. Single site fitting and validation., J. Hydrol., 175, 17-46 Cowpertwait, P.S.P., O’Connell, P.E., Metcalfe, A.V. and Mawdsley, J.A. (1996b), “Stochastic point process modeling of rainfall.II. Regionalisation and disaggregation”, J. Hydrol., 175, 47-65 David Cameron, Keith Beven and Jonathan Tawn (2001) Modelling extreme rainfalls using a modified random pulse Bartlett-Lewis Stochastic Rainfall Model (With Uncertainty) Fadhilah Y., Zalina MD., Nguyen V-T-V and Zulkifli Y. Performance of Mixed Exponential and Exponential Distribution Representing Rain Cell Intensity in Neyman Scott Rectangular Pulse (NSRP) Model Hahn, Brian D. “Essential matlab for engineers and scientists”, New York:Academic Press. H.J. Fowler, C.G. Kilsby, P.E.O’Connell, A. Burton, (2004) “A weather-type conditioned multi-site stochastic rainfall model for the generation of scenarios of climatic variability and change”, J. Hydrology I. Rodriguez- Iturbe, D.R. Cox, F.R.S. and Valerie Isham (1987) “ A point process model for rainfall: further developments” Islam, S., Entekhabi, D., Bras, R.L. & Rodriguez-Iturbe, I. (1990). “Parameter estimation and sensitivity analysis for the modified Bartlett-Lewis rectangular pulses model of rainfall”. J. Geophys. Res. 95:2003-2100. J.C. Smithers, G.G.S. Pegram, R.E. Schulze (2002) Design rainfall estimation in South Africa using Bartlett-Lewis Rectangular Pulse Rainfall Models Khaliq, M. and Cunnane,C.(1996).”Modelling point rainfall occurrences with the modified Bartlett-Lewis Rectangular Pulse Model”. Journal of Hydrology.180:109-138 Martin Haugh, (2004), “Generating Random Variables and Stohastic Processess”, IEOR E4703, pg 1-12 Niko Verhoest, Peter A. Troch, Francois P. De Troch, (1997) “On the applicability of Bartlett-Lewis Rectangular Pulse Models in the Modelling of design storms at a point” Paolo Burlando, Renzo Rosso (1991) Comment on “Parameter Estimation and Sensitivity Analysis for The Modified Bartlett-Lewis Rectangular Pulse Rainfall Model of Rainfall” by S. Islam et al |