Statistical Downscaling Model (SDSM) for Long Term Prediction of Rainfall and Maximum Temperature

Abstract

Climate change is a biggest threat that has impacted hundreds of millions of people. It is critical to calculate the severity of rainfall and temperature in locations prone to hydro meteorological disasters in transitional climatic patterns. As a result, the primary goal of this research is to evaluate the severity of rainfall and maximum temperature under three Representative Concentration Pathways (RCPs) using CanESM2 data from the Global Climate Model. The historic data of 40 years (1981–2021) of the Vadodara district are used which is considered as base period. Our findings were based on CanESM2 scenarios, specifically RCP8.5, which is modelled in the SDSM to determine decade wise future rainfall and maximum temperature from the years 2021–2100 under various carbon emission scenarios. The results reveal that due to the climate change, the general amount of rainfall and temperature in this region of Vadodara District shows increasing trend from 2021 to 2100 when compared to the base period. It is observed that decade wise monthly maximum temperature is increasing in every month. The maximum increasing rate of maximum temperature is 10.22% for the month of July and minimum increasing rate is 1.27% for the month October is observed from 2021 to 2100 compared to base period. It is observed that decade wise monthly rainfall is increasing in each month. For the month of July, rainfall is increasing at the average rate of 9.47% from 2021 to 2100 with respect to base period. Similarly for the month September, rainfall is increasing at the average rate of 3.28% from 2021 to 2100 with respect to base period. The maximum increasing rate of rainfall is 64.72% for the month of June and minimum increasing rate is 3.28% for the month September is observed compared to base period.