Research Projects

Land-atmosphere coupling (LAC) refers to the critical flow of energy, moisture, and gases between the Earth's surface and the atmosphere, which has a substantial impact on climate and weather systems. LAC has an impact on boundary layer stability, convection, and precipitation, with significant implications for the energy, water, and carbon cycles. This interaction has implications for regional climate trends and the occurrence of compound severe events, such as droughts and heatwaves, which have become increasingly important challenges in recent decades. Changes in land cover, soil moisture, and vegetation influence surface albedo and biogeochemical cycles, altering local climates and worsening weather extremes. Compound extremes, such as extended droughts and high heatwaves, endanger critical sectors like agriculture, water supplies, health, and infrastructure. The resulting disturbances highlight the critical need to better understand land-atmosphere interactions, as soil moisture shortages can trigger feedback loops that exacerbate the severity of heatwaves and regional droughts. The present study attempts to address the gaps in LAC research, focusing on the regional and seasonal diversity of LAC feedback mechanisms and their involvement in amplifying or dampening compound extreme occurrences. Our goals include investigating LAC's impact on compound extremes, analyzing LAC variability across different regions and seasons to identify hotspots, and assessing the effects of land use changes on LAC. The methodology uses observational and satellite datasets from across India (including soil moisture, temperature, precipitation, sensible heat, latent heat, and so on) to measure LAC under varied conditions. Analytical approaches such as evaporative fraction, Bowen Ratio, and soil moisture-temperature coupling will be used to estimate energy and water fluxes. To establish the linkage between LAC and compound events, we will use correlation and regression modeling, as well as machine learning for pattern detection and predictive analysis. Seasonal analysis will aid in identifying climate-specific LAC fluctuations, while machine learning methods will be used to classify regions susceptible to extreme events. Future land use projections will help assess how changes in LAC affect regional vulnerability. This complete approach attempts to strengthen forecast capacities regarding compound events induced by LAC, thereby supporting adaptive strategies in climate-sensitive regions.