Abstract:
This dissertation will attempt to demonstrate the potential
benefits of using Stochastic Processes for modeling and interpreting
historical rainfall records by the examination of weekly rainfall
occurrence by using Markov Chains as the driving mechanism.
The weekly occurrence of rainfall was modeled by two-state
first and second order Markov chain while the amount of rainfall on
a rainy week was approximated by taking the maximum likelihood
estimation method to estimate transition probability Matrices of
rainfall sequences during rainy season.
Daily rainfall data were collected from two meteorological
stations located in Kurdufan State based on the (21) years of past
data. The result indicated that the season starts effectively from 8 th
SMW (17 – 22th June) at ElObied station and 7 th SMW (11 – 17th
June) at Kadugli station.
The transition probability matrix of Markov chain model is
homogeneous and remains constant over the years of period
considered. Accordingly the testing of ID degree that one in Elobied
higher than that of Kadugli Station the hypothesis is accepted at 5%
level of significant with P-value (0.151).
The researcher recommended that the weekly rainfall should
be generated with the first-order Markov chain model to preserve the
statistical and seasonal characteristics that exist in the historical
record exact on short season.
