Stochastics Modelling of Rainfall Process in Asia Region: A Systematics Review

Abstract

In recent years, use of the stochastic model has been growing due to the high complexity and dynamics of the atmosphere, especially the rainfall process. Various concepts have been applied to rainfall modeling, ranging from simplistic approaches to more complex models. It is important to understand different stochastic rainfall modeling approaches, as well as their advantages and limitations. This paper determines the development of the latest stochastic rainfall models in the Asia region, where different concepts of stochastic rainfall models were highlighted. It reviews the different methodologies used, including rainfall forecasting, spatio-temporal analysis, and extreme event. We select 30 articles from 1571 literature published between 2013–2022 from the Scopus database. The results show that the stochastic models often used in the literature consist of Markov chain, weather generator, probability distribution, ARIMA, and the Bayesian model. In the recent development in Asia, stochastic models in rainfall modeling research are widely used to generate the occurrence and amount of rainfall data, statistical downscaling, future rainfall trends, and estimation of extreme values. The difference in spatio-temporal, climate conditions, and the parameters model can cause the performance of each model to be different.