Characterization and prediction of southwest monsoon rainfall patterns in Central India as a linear state space modeling

Indian southwest monsoon rainfall has a huge socioeconomic influence, and any changes in monsoon rainfall patterns will have major consequences for agriculture, water supplies, and other connected sectors of economy. The paper explores the charactering and forecasting of the southwest monsoon rainfall (June to September) patterns over the Central region of India for the period (1901–2021: 121 years) using linear state space modeling. The study period is split into two periods, i.e., earlier period (EP: 1901–1950) and the recent period (RP: 1951–2021). The linear state space model (LSSM) with significant latent components such as trend, seasonal, periodic and random variations or noise is selected for modeling the southwest monsoon rainfall (SWMR) based on Bayesian information criteria (BIC) and statistical fit for the two periods. The result indicates that an increase in linear time trend during the EP and the RP has a decreasing local linear time trend in SWMR, and it is interesting to notice that the EP had no noteworthy periodic variations, whereas the RP exhibits significant periodic variations in SWMR. Further, the selected LSSM forecasts SWMR patterns for the period 2022–2028, and it has been observed that the Central Indian region is expected to receive substantial rainfall in the years 2026, 2027 and 2028.