Open AccessForecasting Meteorological Parameters to Identify Categories of Droughts in Amreli Using Downscaling Approach
Author(s) -
Gautam Zadafiya,
Chirag Ladavia,
Haresh Gandhi
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.41217
Subject(s) - downscaling , climatology , environmental science , natural disaster , meteorology , precipitation , flood myth , vulnerability (computing) , computer science , environmental resource management , geography , computer security , archaeology , geology
The rising incidence of natural disasters as a result of climate change has become a major concern for the entire world in recent years. The majority of people in India who work in agriculture or near coastal areas rely on natural resources for their sustenance and income. Droughts, floods, cyclones and other natural calamities are linked to uncertainty in meteorological parameters. Hence, this research is crucial for such analysis. As a result, the primary goal of this research is to investigate the uncertainty of numerous meteorological parameters in Amreli. In the previous decade, there has been a considerable increase in the uncertainty of meteorological data in this region. Statistical downscaling uses GCM data, while dynamic downscaling techniques use RCM data. The statistical downscaling technique was used in this work to accurately forecast meteorological characteristics. To anticipate meteorological characteristics, the RCP 8.5 (A2) scenario is used in this study. SPI values are used for the analysis and forecasting of drought categories. The predicted meteorological parameters will be extremely helpful in the future development of various tools and ways of dealing with various natural disasters. Flood vulnerability maps and famine response planning can be prepared using these data. For example, Assistance can be provided topology developers to prepare policy based on the severity of the forecasted meteorological data. Keywords: GCM, RCP, SPI, Statistical Downscaling, Temperature, Precipitation.