DEVELOPING AND CALIBRATING A STOCHASTIC RAINFALL GENERATOR MODEL FOR SIMULATING DAILY RAINFALL BY MARKOV CHAIN APPROACH
DOI:
https://doi.org/10.11113/jt.v76.5946Keywords:
Rainfall, climate change, paddy, Markov chain, weather generator (WGEN), transition probabilities, MalaysiaAbstract
Rainfall is an important parameter in tropical humid regions for which paddy production systems depend. A significant portion of paddy water requirements is supplied by natural rainfall. Several studies have predicted changes in rainfall patterns and in the amount of rain that may be obtainable in future owing to climate change. There is increased concern about future water availability for an important crop such as rice. Need to develop new water management tools for sustainable production is inevitable, but such tools require long-term climate data that is credible and consistent with the time. This study concerns itself with evaluating a stochastic weather generator (WGEN) model for simulating daily rainfall series. The model is assessed using long-term historical rainfall data obtained from a rice growing irrigation schemes in Malaysia. The model is based on a first-order two-state Markov chain approach which uses two transition probabilities and random number to generate rainfall series. Selected statistical properties were computed for each station and compared against those retrieved from the model after model training and testing. The results obtained from these comparisons are quite satisfactory giving confidence about the performance and future outputs from the model. The model has shown good skill in describing the rainfall occurrence process and rainfall amounts for the area. The model will be adapted in a subsequent study for downscaling and simulating effective daily rainfall series corresponding to future climate scenarios.
References
Intergovernmental Panel on Climate Change (IPCC). 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report Of The Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.
Lee, T. S., Haque, M. A. and Najim, M. 2005. Scheduling the Cropping Calendar in Wet-Seeded Rice Schemes in Malaysia. Agricultural water Management. 71(1): 71-84.
DID. 1996. Detailed Study of Water Resources Availability for Northwest Selangor Integrated Agricultural Development Project. Department of Irrigation and Drainage, Kuala Lumpur Office, Malaysia.
Jang, Min-Won, Choi, Jin-Yong, and Lee, Jeong-Jae. 2007. A Spatial Reasoning Approach to Estimating Paddy Rice Water Demand in Hwanghaenam-Do, North Korea. Agricultural Water Management. 89(3): 185-198.
Caron, Annie, Leconte, Robert, and Brissette, François. 2008. An Improved Stochastic Weather Generator for Hydrological Impact Studies. Canadian Water Resources Journal. 33(3): 233-256.
Furrer, Eva M, and Katz, Richard W. 2008. Improving the Simulation of Extreme Precipitation Events by Stochastic Weather Generators. Water Resources Research. 44(12).
Dian, B, Gameda, Samuel, and Hayhoe, Henry. 2008. Performance of Stochastic Weather Generators LARS-WG and AAFC-WG for Reproducing Daily Extremes of Diverse Canadian Climates. Climate Research.
Semenov, Mikhail A. 2007. Simulation of Extreme Weather Events by a Stochastic Weather Generator. Climate Research. 35(3): 203.
Wilks, Daniel S, and Wilby, Robert L. 1999. The Weather Generation Game: A Review of Stochastic Weather Models. Progress in Physical Geography. 23(3): 329-357.
Deni, Sayang Mohd, Suhaila, Jamaludin, Zin, Wan Zawiah Wan, and Jemain, Abdul Aziz. 2010. Spatial Trends of Dry Spells Over Peninsular Malaysia During Monsoon Seasons. Theoretical and Applied Climatology. 99(3-4): 357-371.
Zin, W. Z. W., et al. 2010. Recent Changes in Extreme Rainfall Events in Peninsular Malaysia: 1971–2005. Theoretical and Applied Climatology. 99(3-4): 303-314.
Yusof, Fadhilah, Hui-Mean, Foo, Suhaila, Jamaludin, Yusop, Zulkifli, and Ching-Yee, Kong. 2014. Rainfall Characterisation by Application of Standardised Precipitation Index (SPI) in Peninsular Malaysia. Theoretical and Applied Climatology. 115(3-4): 503-516.
Deni, Sayang Mohd, and Jemain, Abdul Aziz. 2009. Fitting the Distribution of Dry and Wet Spells with Alternative Probability Models. Meteorology and Atmospheric Physics. 104(1-2): 13-27.
Richardson, Clarence W. 1981. Stochastic Simulation of Daily Precipitation, Temperature, and Solar Radiation. Water Resources Research. 17(1): 182-190.
Tolika K, Maheras P. 2005. Spatial and Temporal Characteristics of Wet Spells in Greece. Theor Appl Climatol 81(1-2): 71-85.
Deni, Sayang Mohd, Jemain, Abdul Aziz, and Ibrahim, Kamarulzaman. 2010. The Best Probability Models for Dry and Wet Spells in Peninsular Malaysia During Monsoon Seasons. International Journal of Climatology. 30(8): 1194-1205.
Jamaludin, Suhaila, and Jemain, Abdul Aziz. 2007. Fitting the Statistical Distributions to the Daily Rainfall Amount in Peninsular Malaysia. Jurnal Teknologi. 46(C): 33-48.
Haan, Charles Thomas. 1977. Statistical Methods in Hydrology.
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