Dr. Ramamoorthy Ayyamperumal : IAGR Conference 2024
Dr. Ramamoorthy Ayyamperumal, Young Research Professor, College of Earth and Environmental Sciences, Lanzhou University-Lanzhou-P.R. China
Dr. Ramamoorthy Ayyamperumal is an accomplished geoscientist with a rich academic and research background. He completed his bachelor’s degree from VOC College under Manonmaniam Sundaranar University, followed by a Master’s degree from Annamalai University, where he graduated as the First Rank Holder and received a Gold Medal. Dr. Ayyamperumal was awarded the prestigious DST INSPIRE Award for his Ph.D. work, which he completed at the University of Madras in the field of Geosciences.
Dr. Ayyamperumal further advanced his research career with an academic visit and postdoctoral work at Lanzhou University, Gansu Province, China, specializing in Earth and Environmental Sciences and Environmental Geochemistry. His work focuses on air, soil, sediment, and water geochemistry.
Throughout his career, Dr. Ayyamperumal has been recognized with numerous awards, including the G.L. Tandon Award, Prof. T.N. Muthuswami Award, Ramasamy Padyatachi Endowment Award, Dr. Sri. A. Lakshmanaswami Mudaliar Award, as well as international honors such as the Young Scientist Award and Best Researchers Award.
He has an impressive publication record, with over 65 research articles published in high-impact journals such as *Science of the Total Environment*, *Chemosphere*, *Environmental Research*, *Marine Pollution Bulletin*, and many more. Dr. Ayyamperumal has also contributed to the academic community by conducting more than four special issues for leading journals like Elsevier, Springer, and Wiley, and by reviewing over 300 research articles in his field. Dr. Ayyamperumal’s contributions to geosciences and environmental research continue to make a significant impact in his field.
Title of Talk:
Quantifying regional rainfall dynamics, climate variation and air pollution in southern India: Unravelling monsoon characteristics and intense precipitation using satellite, observed data records and Google Earth Engine
Abstract
Climate change and regional air pollution have had significant proportional coherence and are collectively hazardous for the regional ecosystem. Rainfall patterns are unpredictable, which has implications for planning irrigation systems, constructing farm ponds and drainage systems, and conserving water and soil. We obtained high-resolution remotely sensed datasets from 2001 to 2022. The research findings have been compiled over 36 years, utilizing gridded daily precipitation data from the IMD and PWD (1984–2019). This study investigates the frequency of consecutive rainy days, the yearly rainfall pattern and the precipitation threshold value that triggers the flood. To estimate climate variation, we utilized Climate Hazard Group InfraRed Precipitation with Station Data Version 2.0 (CHIRPS) and Moderate Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST). Additionally, we used Sentinel-5P datasets to collect spatio-temporal information for regional CO (Carbon Monoxide), NO2 (Nitrogen Dioxide), SO2 (Sulfur Dioxide), and UV Aerosol index. Moreover, regional concentration of air pollutants exhibits spatio-temporal variability at annual and seasonal scales, where maximum engrossment is occupied by CO during the pre-monsoon and monsoon season. However, other pollutants are also dominant in the northern parts of the city, whereas NO2 and absorbing Aerosol during pre-monsoon season experienced significant increase throughout the years. Understanding the fluctuations in air pollution levels across different weather situations might help in developing targeted pollution reduction methods. In this study findings will show that rainfall patterns can be used to forecast floods and estimate their return and recurrence intervals. As a result, implementing this research with these precipitation threshold values could help with flood warning systems and the infrastructure required for emergency responses to flooding disasters.
Keywords: Climate change pollution; Spatial distribution; Rainfall pattern; Google earth engine.
