Abstract
A rainfall simulation model based on a first‐order Markov chain has been developed to simulate the annual variation in rainfall amount that is observed in Bangladesh. The model has been tested in the Barind Tract of Bangladesh. Few significant differences were found between the actual and simulated seasonal, annual and average monthly. The distribution of number of success is asymptotic normal distribution. When actual and simulated daily rainfall data were used to drive a crop simulation model, there was no significant difference of rice yield response. The results suggest that the rainfall simulation model perform adequately for many applications.
Highlights
A Rainfall Simulation Model for AgriculturalA rainfall simulation model based on a first-order Markov chain has been developed to simulate the annual variation in rainfall amount that is observed in Bangladesh
The impact of extreme climate can have an importance for economically vulnerable farmer that far outweighs its apparent probability of occurrence
The exact and asymptotic mean and variance of number of success are same for each month of the Barind Tract (Table II)
Summary
A rainfall simulation model based on a first-order Markov chain has been developed to simulate the annual variation in rainfall amount that is observed in Bangladesh. The model has been tested in the Barind Tract of Bangladesh. Few significant differences were found between the actual and simulated seasonal, annual and average monthly. The distribution of number of success is asymptotic normal distribution. When actual and simulated daily rainfall data were used to drive a crop simulation model, there was no significant difference of rice yield response. The results suggest that the rainfall simulation model perform adequately for many applications. Keywords." Simulation modelling, Markov chain, Probability distribution, Latin Hypercube Sampling, Agro-technology
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