AI SearchDrought prediction for irrigation scheduling in Northeast
Call Number: AIT Thesis no.WA-80-24 Material type:
TextSeries: Asian Institute of Technology. Thesis ; no. WA-80-24Publication details: Bangkok : Asian Institute of Technology, 1980Description: v, 117 pSubject(s): Online resources: Dissertation note: Thesis (M.Eng.) - Asian Institute of Technology, 1980 Summary: The lack of statistical methods to satisfactorily describe drought durations during the wet season for Northeast Thailand was the influencing factor to undertake this study. Drought measures in this study, are based on the point rainfall process of rain gauge station at Kalasin province Muang district in the Northeast of Thailand. Three different methods to describe the drought durations monthly as well as seasonal are attempted. A statistical analysis of extreme drought durations during the wet season is carried out. Probability of occurrence of wet and dry days related to various lengths of period on a monthly basis, is found to fit a simple modifications of the Negative Binomial Distribution. Being able to predict the run length of dry days at different probability: levels an attempt is made to use this knowledge in scheduling of irrigation to save irrigation water. Finally, a daily rainfall model which is a combination of First-order Markov process and Wet-Dry process, is tried out to describe the daily rainfall data of monsoonal type rainfall as in Thailand.
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22-AIT Thesis (Replacement)
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A thesis submitted in partial fulfillment of requirements for the degree of master of engineering, School of Engineering and Technology
Thesis (M.Eng.) - Asian Institute of Technology, 1980
The lack of statistical methods to satisfactorily describe drought durations during the wet season for Northeast Thailand was the influencing factor to undertake this study. Drought measures in this study, are based on the point rainfall process of rain gauge station at Kalasin province Muang district in the Northeast of Thailand. Three different methods to describe the drought durations monthly as well as seasonal are attempted. A statistical analysis of extreme drought durations during the wet season is carried out. Probability of occurrence of wet and dry days related to various lengths of period on a monthly basis, is found to fit a simple modifications of the Negative Binomial Distribution. Being able to predict the run length of dry days at different probability: levels an attempt is made to use this knowledge in scheduling of irrigation to save irrigation water. Finally, a daily rainfall model which is a combination of First-order Markov process and Wet-Dry process, is tried out to describe the daily rainfall data of monsoonal type rainfall as in Thailand.
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