AI SearchCharacteristics of monthly and daily rainfall sequences in Northeast Thailand
Call Number: AIT Thesis no. WA-79-2 Material type:
TextSeries: Asian Institute of Technology. Thesis ; no. WA-79-2Publication details: Bangkok : Asian Institute of Technology, 1980Description: v, 142 pSubject(s): Online resources: Dissertation note: Thesis (M.Eng.) - Asian Institute of Technology, 1980 Summary: Characteristics of rainfall sequences in Northeast, Thailand were investigated. These include some typical descriptors of monthly rainfall, the distribution of monthly rainfall the distribution of rainfall amounts in wet season, the distribution of maximum daily rainfall and the distribution of the length of wet and dry spells. Finally a stochastic model based on a first-order Markov chain was developed to simulate daily rainfall sequences. The model used historical rainfall data to estimate the Markov transition probabilities. A separate matrix is estimated for each month of the year. In this research, 8 x 8 transitional probability matrices were used. The class widths of the states were represented by a geometric progression. The distribution of rainfall amount in each state was assumed t o be uniform except the last state, for which a shifted exponential distribution was used. Calibration and performance of the model was tested by comparing some statistical parameters of the generated data and the historical ones.
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20-AIT Publication
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Asian Institute of Technology Library AIT Publications | AIT Thesis no. WA-79-2 (Browse shelf(Opens below)) | 2 | Available | 30050003068219 | |||||||||||||
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Asian Institute of Technology Library Archives | AIT Thesis no. WA-79-2 (Browse shelf(Opens below)) | Available | 30050160040316 |
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Engineering and Technology
Thesis (M.Eng.) - Asian Institute of Technology, 1980
Characteristics of rainfall sequences in Northeast, Thailand were investigated. These include some typical descriptors of monthly rainfall, the distribution of monthly rainfall the distribution of rainfall amounts in wet season, the distribution of maximum daily rainfall and the distribution of the length of wet and dry spells. Finally a stochastic model based on a first-order Markov chain was developed to simulate daily rainfall sequences. The model used historical rainfall data to estimate the Markov transition probabilities. A separate matrix is estimated for each month of the year. In this research, 8 x 8 transitional probability matrices were used. The class widths of the states were represented by a geometric progression. The distribution of rainfall amount in each state was assumed t o be uniform except the last state, for which a shifted exponential distribution was used. Calibration and performance of the model was tested by comparing some statistical parameters of the generated data and the historical ones.
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