Downscaling approach for analysis and forecasting of meteorological parameters and identification of different drought years

Abstract

Abstract Natural disasters related to water resources as a result of climate change are becoming a major concern for the entire world. The majority of people in India work in agriculture. Droughts, floods, cyclones, and other natural disasters are all linked to uncertainty in meteorological parameters. The primary goal of this research is to investigate and forecast various meteorological parameters in the Bhavnagar district of the Gujarat region. The statistical downscaling technique uses Global Climate Model (GCM) data for analysis, while the dynamic downscaling technique uses Regional Climate Model (RCM) data. RCM data are prepared for important places of the Earth. GCM data are freely available for all parts of the Earth. So, in this study, GCM data are used. RCM data and dynamic downscaling make the process complicated. Using GCM data and the statistical downscaling technique, meteorological parameters i.e. maximum temperature, minimum temperature, and rainfall can be accurately predicted in this study. To predict meteorological parameters, RCP 8.5 scenario and SPI are used to identify the various types of drought years of the study area. Flood hazard maps and drought contingency plans can be prepared based on predicted meteorological parameters. According to SPI analysis, Bhavnagar will experience 26 mild, 8 moderate, 2 severe, and 3 special droughts up to the year 2100.