The Impact of ENSO on Rainfall in the Northeast of Thailand
Abstract
The purposes of this research were to find the Impact of ENSO phenomenon to construct the forecasting model on rainfall, which classified by the river basins in the in the Northeastern Thailand. The secondary data with 12 variables viz the Meteorological factors, ENSO index, Asian Monsoon Index and Indian Oceans Dipole were collected from Thai Meteorological Department and National Oceanic and Atmospheric Administration. The data were analyzed by Factor analysis and found that 4, 5 and 4 factors were effected from ENSO and IOD index with 23.228%, 23.249% and 23.184% variation by the first PC at Khong, Chi and Moon River Basins, respectively. Moreover the Principle Component Regression analysis was also constructed for forecasting the rainfall in three rivers basins with coefficients of determination as .566, .551 and .523 respectively.Keywords: ENSO, ENSO Index, Principle Component analysis, Principle Component Regression *Corresponding author. E-mail : saowanee_st@hotmail.comReferences
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Brikshavana, M. and S. Nimma. (1995), Seasonal rainfall forecast for Thailand. Proc. Second Int. Study Conf. on GEWEX in Asia and GAME, Pattaya, Thailand, 142-145.
Climatological Group. (2009). The Climate of Thailand, Metrological Development Bureau,Thai metrological.
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Kessler, W.S. 2002. Is ENSO a cycle or a series of events. Geophys.Res. Lett., 29, 2125. doi:10.1029/2002GL015924.
Kripalani, R.H. and Kulkarni, A. (1997). Rainfall variability over south-esat-Asia-Connections with Indian monsoon and ENSO extreme: new perpectives,j. Climate, 17,1155 – 1168
Kusreesakul K. (2009), Spatio-temporal Rainfall Changes in Thailand and Their Connection with Regional and Global Climate Variability, Master Thesis, Prince Songkhla University. (in Thai)
lex In Ieong et al., 2015. Using Principle Component Regression, Artificial Neural Network, and Hybrid Models for Predicting Phytoplankton Abundance in Macau Storage Reservoir. Environ Model Assess., 20,355–365.doi:10.1007/s10666-014-9433-3
McPhaden, M. J. (2004) Evolution of the 2002–2003 El Niño, Bull. Am. Meteorol. Soc., 85, 677–695.
Metrological Development Bureau. (2016), The Climate of Thailand, Retrieved October 10, 2016, from http://tmd.go.th/index.php
Sinsomboonthong, S (2016). Multivariate Analysis by SPSS and Minitab. Bangkok: Department of Statistics, Faculty of Science, King Mongkut's Institute of Technology Ladkrabang. (in Thai)
Sousa, S.I.V., Martins, F.G., Alvim-Ferraz, M.C.M., Pereira, M.C. (2007). Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations, Environmental Modelling & Software 22(1), 97- 103. 2007.
Thaweesil K. (2015).The Influence of Climatic Indices on Rainfall in the Lower Central Plain of Thailand. The 6th National Convention on Water Resources Engineering (NCWRE6). Udonthani, Thailand. (in Thai)
Vanichbuncha K. (2009). Multivariate statistical technique. Bangkok: Faculty of Commerce and Accountancy Chulalongkorn University. (in Thai)
Limsakul A.(2007), Spatio-temporal changes in total annual rainfall and the annual number of rainy days in Thailand, Journal of Environmental Research, 29(2), 1-21.
Brikshavana, M. and S. Nimma. (1995), Seasonal rainfall forecast for Thailand. Proc. Second Int. Study Conf. on GEWEX in Asia and GAME, Pattaya, Thailand, 142-145.
Climatological Group. (2009). The Climate of Thailand, Metrological Development Bureau,Thai metrological.
Dore MHI. (2005). Climate change and changes in global precipitation patterns: what do we know?. Environ Int., 31,1167–1181.
Kessler, W.S. 2002. Is ENSO a cycle or a series of events. Geophys.Res. Lett., 29, 2125. doi:10.1029/2002GL015924.
Kripalani, R.H. and Kulkarni, A. (1997). Rainfall variability over south-esat-Asia-Connections with Indian monsoon and ENSO extreme: new perpectives,j. Climate, 17,1155 – 1168
Kusreesakul K. (2009), Spatio-temporal Rainfall Changes in Thailand and Their Connection with Regional and Global Climate Variability, Master Thesis, Prince Songkhla University. (in Thai)
lex In Ieong et al., 2015. Using Principle Component Regression, Artificial Neural Network, and Hybrid Models for Predicting Phytoplankton Abundance in Macau Storage Reservoir. Environ Model Assess., 20,355–365.doi:10.1007/s10666-014-9433-3
McPhaden, M. J. (2004) Evolution of the 2002–2003 El Niño, Bull. Am. Meteorol. Soc., 85, 677–695.
Metrological Development Bureau. (2016), The Climate of Thailand, Retrieved October 10, 2016, from http://tmd.go.th/index.php
Sinsomboonthong, S (2016). Multivariate Analysis by SPSS and Minitab. Bangkok: Department of Statistics, Faculty of Science, King Mongkut's Institute of Technology Ladkrabang. (in Thai)
Sousa, S.I.V., Martins, F.G., Alvim-Ferraz, M.C.M., Pereira, M.C. (2007). Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations, Environmental Modelling & Software 22(1), 97- 103. 2007.
Thaweesil K. (2015).The Influence of Climatic Indices on Rainfall in the Lower Central Plain of Thailand. The 6th National Convention on Water Resources Engineering (NCWRE6). Udonthani, Thailand. (in Thai)
Vanichbuncha K. (2009). Multivariate statistical technique. Bangkok: Faculty of Commerce and Accountancy Chulalongkorn University. (in Thai)
Limsakul A.(2007), Spatio-temporal changes in total annual rainfall and the annual number of rainy days in Thailand, Journal of Environmental Research, 29(2), 1-21.
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Published
2016-11-03
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