Determining the daily rainfall characteristics from the monthly rainfall totals in central and northeastern Thailand

Abstract

In the tropics precipitation is the greatest expression of climatic variability and the attributes of daily rainfall are crucial in the risk assessment of climate conditions that may have damaging effect on agriculture. Although daily rainfall totals data accessibility is frequently limited, monthly rainfall data are more abundant in space and time in many developing countries including Thailand. The daily rainfall totals in the vicinity of four provinces of central and northeast Thailand (Lopburi, Chachoengsao, Buriram and Sisaket) are analyzed in order to establish their relationship to monthly rainfall totals. The study area is characterized by a high diversity of crops, thus there is no single criterion that can be set for what may constitute an agro-meteorological shock. Therefore probability distributions are established to describe as fully as possible potential levels of what might be considered meteorologic droughts or excess rainfalls. Daily rainfall is modeled as a Markov process involving transitions from wet and dry days and the representation of daily rainfall totals, all of which are expected to vary seasonally and spatially. The transition probabilities for consecutive days with rain or no rain were calculated for each month. The magnitudes of daily rainfalls are represented by the gamma distribution, parameters of which can be estimated simply from the mean and variance via the method of moments. Combining derived daily rainfall parameters provides a straightforward method of predicting the risk of agro-meteorological shock. The relationship between the observed monthly rainfall and the transition probabilities, mean and standard deviation of daily rainfall is examined using a deterministic approach, finding the most likely values of parameters of interest according to 6 different intervals of monthly rainfall totals. These probability expressions provide useful information on climatic shock occurrence likelihood on the basis of widely available, monthly rainfall data in Thailand and the flexible approach can be applied by various end-users.