Stochastic modeling of daily rainfall at Naogaon district in Bangladesh: a comparative study

Abstract

The weather plays a significant role in the agriculture of a country. Rainfall is the most important weather parameter affecting non-irrigated crop areas. The region lies in the driest part of the country in terms of rainfall and is semi-arid in character. Markov chain model has been used to evaluate probabilities of getting a sequence of wet –dry weeks over this region. A non-linear proceed, the model is tested using multivariate logistic regression technique. The results showed the relative frequency of rain against the estimated value of P using the logistic regression at 7 days interval. The results of the relative frequencies of 0.29 and 0.71 are similar in the study area. The logistic regression technique allows flexibility in deciding between areas of indicated rain or no rain. The results indicate that no rain is predicted when the estimated value of P is less than 0.5 and rain, when it is greater than 0.5. Therefore when the estimated P is less than 0.1, it is almost not raining. Basically this type of information gives the advantages of logistic regression over a simple threshold technique to agricultural planners and irrigation engineers in identifying the areas where agricultural development should be focused as a long-term drought mitigation strategy.