International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

Frequency: 12
line decor
  
line decor

 Volume 5, Issue 1 (January 2018), Pages: 101-108

----------------------------------------------

 Original Research Paper

 Title: Stochastic generation of hourly rainfall series in the Western Region of Peninsular Malaysia

 Author(s): A. H. Syafrina 1, *, A. Norzaida 2, O. Noor Shazwani 2

 Affiliation(s):

 1Department of Science in Engineering, Kuliyyah of Engineering, International Islamic University of Malaysia, 53100 Gombak, Selangor, Malaysia
 2UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia

 https://doi.org/10.21833/ijaas.2018.01.013

 Full Text - PDF          XML

 Abstract:

Comprehensive analysis and modeling of rainfall distribution is essential in capturing the characteristics of high intense rainfall. The western region of Peninsular Malaysia which is more urbanized and densely populated is prone to flash flood occurrences due to the high intense rainfall brought by a convective rainfall during the inter-monsoon season. Convective rain is usually short live and intense. Therefore, knowledge pertaining to the distribution of rainfall intensity at short time scale is crucial in planning and decision making prior to, during and after a flood event, thereby minimizing the potentially catastrophic impact of flooding. The selection of appropriate probability distribution to represent rainfall intensity is highly critical to get a better indication of seasonal contribution to the annual rainfall. This study aimed to determine the better distribution of rainfall intensity to represent extreme rainfall events in the western region using Advanced Weather Generator (AWE-GEN). Model development consists of using hourly rainfall data and other meteorological data from three stations located within the studied region. Two probability distributions incorporated in the AWE-GEN model, namely, Weibull and Gamma were fitted to the historical data. Numerical evaluation using Root Mean Square Error goodness-of-fit test was used to compare the performance of the distributions. Results showed that AWE-GEN model is capable of simulating the monthly rainfall series at the west coast region with Weibull being the better distribution representing intensity. It was found that high values in model parameters  and  contribute to the higher intense rainfall within the studied region. The AWE-GEN model also performs quite well in reproducing the hourly and 24 hour extremes rainfall as well as generating the extreme wet spell; however the model slightly underestimates the extreme dry spell. Results can be beneficial, particularly, for a better rainfall forecasting at watersheds and urban areas. 

 © 2017 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Stochastic model, Rainfall intensity, Probability distribution, Weather generator, Goodness-of-fit

 Article History: Received 17 August 2017, Received in revised form 20 October 2017, Accepted 18 November 2017

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2018.01.013

 Citation:

 Syafrina AH, Norzaida A, and Shazwani ON (2018). Stochastic generation of hourly rainfall series in the Western Region of Peninsular Malaysia. International Journal of Advanced and Applied Sciences, 5(1): 101-108

 Permanent Link:

 http://www.science-gate.com/IJAAS/2018/V5I1/Syafrina.html

----------------------------------------------

 References (37)

  1. Ababaei B, Sohrabi T, Mirzaei F, and Karimi B (2010). Evaluation of a stochastic weather generator in different climates. Computer and Information Science, 3(3): 217-229. https://doi.org/10.5539/cis.v3n3p217 
  2. Abas N, Daud ZM, and Yusof F (2014). A comparative study of mixed exponential and Weibull distributions in a stochastic model replicating a tropical rainfall process. Theoretical and Applied Climatology, 118(3): 597-607. https://doi.org/10.1007/s00704-013-1060-4 
  3. Arritt R and Daniel A (2014). Precipitation intensity changes in regional climate projections for North America. In the EGU General Assembly Conference Abstracts, Vienna, Austria. PMid:25475272     
  4. Barrow EM and Semenov MA (1995). Climate-change scenarios with high spatial and temporal resolution for agricultural applications. Forestry: An International Journal of Forest Research, 68(4): 349-360.     
  5. Brissette FP, Khalili M, and Leconte R (2007). Efficient stochastic generation of multi-site synthetic precipitation data. Journal of Hydrology, 345(3-4): 121-133. https://doi.org/10.1016/j.jhydrol.2007.06.035 
  6. Chun KP, Wheater HS, and Onof C (2013). Comparison of drought projections using two UK weather generators. Hydrological Sciences Journal, 58(2): 295-309. https://doi.org/10.1080/02626667.2012.754544 
  7. Dan'azumi S, Shamsudin S, and Aris A (2010). Modeling the distribution of rainfall intensity using hourly data. American Journal of Environmental Sciences, 6(3): 238-243. https://doi.org/10.3844/ajessp.2010.238.243 
  8. Daud ZM, Rasid SMM, and Abas N (2016). A regionalized stochastic rainfall model for the generation of high resolution data in Peninsular Malaysia. Modern Applied Science, 10(5): 77-86. https://doi.org/10.5539/mas.v10n5p77 
  9. Dubrovský M (1997). Creating daily weather series with use of the weather generator. Environmetrics, 8(5): 409-424. https://doi.org/10.1002/(SICI)1099-095X(199709/10)8:5<409::AID-ENV261>3.0.CO;2-0 
  10. Fatichi S, Ivanov VY, and Caporali E (2011). Simulation of future climate scenarios with a weather generator. Advances in Water Resources, 34(4): 448-467. https://doi.org/10.1016/j.advwatres.2010.12.013 
  11. Fowler HJ, Blenkinsop S, and Tebaldi C (2007). Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. International Journal of Climatology, 27(12): 1547-1578. https://doi.org/10.1002/joc.1556 
  12. Hashmi MZ, Shamseldin AY, and Melville BW (2009). Downscaling of future rainfall extreme events: A weather generator based approach. In the 18th World IMACS / MODSIM Congress, Cairns, Australia: 3928-3934.     
  13. Hashmi MZ, Shamseldin AY, and Melville BW (2011). Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed. Stochastic Environmental Research and Risk Assessment, 25(4): 475-484. https://doi.org/10.1007/s00477-010-0416-x 
  14. Hassan Z, Shamsudin S, and Harun S (2014). Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature. Theoretical and Applied Climatology, 116(1-2): 243-257. https://doi.org/10.1007/s00704-013-0951-8 
  15. Jeong DI, St-Hilaire A, Ouarda TBMJ, and Gachon P (2013). A multi-site statistical downscaling model for daily precipitation using global scale GCM precipitation outputs. International Journal of Climatology, 33(10): 2431-2447. https://doi.org/10.1002/joc.3598 
  16. Khazaei MR, Zahabiyoun B, and Saghafian B (2012). Assessment of climate change impact on floods using weather generator and continuous rainfall-runoff model. International Journal of Climatology, 32(13): 1997-2006. https://doi.org/10.1002/joc.2416 
  17. Kim B, Kim H, Seoh B, and Kim N (2007). Impact of climate change on water resources in Yongdam Dam Basin, Korea. Stochastic Environmental Research and Risk Assessment, 21(4): 355-373. https://doi.org/10.1007/s00477-006-0070-5 
  18. Kim BS, Kim BK, and Kwon HH (2011). Assessment of the impact of climate change on the flow regime of the Han River basin using indicators of hydrologic alteration. Hydrological Processes, 25(5): 691-704. https://doi.org/10.1002/hyp.7856 
  19. Kuchar L, Iwanski S, Jelonek L, and Szalinska W (2014). Application of spatial weather generator for the assessment of climate change impacts on a river runoff. Geografie, 119(1): 1-25.     
  20. Manning LJ, Hall JW, Fowler HJ, Kilsby CG, and Tebaldi C (2009). Using probabilistic climate change information from a multimodel ensemble for water resources assessment. Water Resources Research, 45: W11411. https://doi.org/10.1029/2007WR006674 
  21. Mareuil A, Leconte R, Brissette F, and Minville M (2007). Impacts of climate change on the frequency and severity of floods in the Châteauguay river basin, Canada. Canadian Journal of Civil Engineering, 34(9): 1048-1060. https://doi.org/10.1139/l07-022 
  22. Mehan S, Guo T, Gitau MW, and Flanagan DC (2017). Comparative study of different stochastic weather generators for long-term climate data simulation. Climate, 5(2). https://doi.org/10.3390/cli5020026 
  23. Min YM, Kryjov V, An KH, Hameed S, Sohn SJ, Lee WJ, Lee WJ, and Oh JH (2011). Evaluation of the weather generator CLIGEN with daily precipitation characteristics in Korea. Asia-Pacific Journal of Atmospheric Sciences, 47(3): 255-263. https://doi.org/10.1007/s13143-011-0014-y 
  24. Norzaida A, Zalina MD, and Fadhilah Y (2016). Application of Fourier series in managing the seasonality of convective and monsoon rainfall. Hydrological Sciences Journal, 61(10): 1967-1980. https://doi.org/10.1080/02626667.2015.1062892 
  25. Parey S, Hoang TTH, and Dacunha-Castelle D (2014). Validation of a stochastic temperature generator focusing on extremes, and an example of use for climate change. Climate Research, 59(1): 61-75. https://doi.org/10.3354/cr01201 
  26. Schnur R and Lettenmaier DP (1998). A case study of statistical downscaling in Australia using weather classification by recursive partitioning. Journal of Hydrology, 212: 362-379. https://doi.org/10.1016/S0022-1694(98)00217-0 
  27. Semenov MA (2008). Simulation of extreme weather events by a stochastic weather generator. Climate Research, 35(3): 203-212. https://doi.org/10.3354/cr00731 
  28. Semenov MA and Barrow EM (1997). Use of a stochastic weather generator in the development of climate change scenarios. Climatic Change, 35(4): 397-414. https://doi.org/10.1023/A:1005342632279 
  29. Semenov MA, Brooks RJ, Barrow EM, and Richardson CW (1998). Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Climate Research, 10(2): 95-107. https://doi.org/10.3354/cr010095 
  30. Suhaila J, Ching-Yee K, Fadhilah Y, Hui-Mean F (2011) Introducing the Mixed distribution in fitting rainfall data. Open Journal of Modern Hydrology, 1(2): 11-22. https://doi.org/10.4236/ojmh.2011.12002 
  31. Sunyer MA, Madsen H, and Ang PH (2012). A comparison of different regional climate models and statistical downscaling methods for extreme rainfall estimation under climate change. Atmospheric Research, 103: 119-128. https://doi.org/10.1016/j.atmosres.2011.06.011 
  32. Syafrina AH, Zalina MD, and Juneng L (2015). Historical trend of hourly extreme rainfall in Peninsular Malaysia. Theoretical and Applied Climatology, 120(1-2): 259–285. https://doi.org/10.1007/s00704-014-1145-8 
  33. Tseng HW, Yang TC, Kuo CM, and Yu PS (2012). Application of multi-site weather generators for investigating wet and dry spell lengths under climate change: A case study in Southern Taiwan. Water Resources Management, 26(15): 4311-4326. https://doi.org/10.1007/s11269-012-0146-6 
  34. Wilby RL, Hassan H, and Hanaki K (1998). Statistical downscaling of hydrometeorological variables using general circulation model output. Journal of Hydrology. 205(1-2): 1-19. https://doi.org/10.1016/S0022-1694(97)00130-3 
  35. Wilks DS (2010). Use of stochastic weather generators for precipitation downscaling. Wiley Interdisciplinary Reviews-Climate Change, 1(6): 898-907. https://doi.org/10.1002/wcc.85 
  36. Wilks DS and Wilby RL (1999). The weather generation game: A review of stochastic weather models. Progress in Physical Geography, 23(3): 329-357. https://doi.org/10.1191/030913399666525256 
  37. Willems P, Arnbjerg-Nielsen K, Olsson J, and Nguyen VTV (2012). Climate change impact assessment on urban rainfall extremes and urban drainage: Methods and shortcomings. Atmospheric Research, 103: 106-118. https://doi.org/10.1016/j.atmosres.2011.04.003