Statistical Downscaling and Projections of Relative Humidity in the Bhima Sub-Basin, India using Change Factor Method

Abstract

This study investigates the anticipated impacts of climate change on key sectors such as agriculture, hydrology, and irrigation management through the analysis of future relative humidity scenarios. Employing the Change Factor method, we downscale daily mean relative humidity across the Bhima sub-basin using an ensemble of statistically downscaled global climate model simulations. Our focus lies in assessing changes in average daily mean relative humidity under various Representative Concentration Pathway (RCP) scenarios. The findings reveal a consistent decline in relative humidity across all RCP pathways, with higher emission scenarios like RCP 6.0 and RCP 8.5 exhibiting more pronounced reductions. Furthermore, the study highlights the presence of uncertainties in future projections, emphasizing the need for continued monitoring and adaptive measures to mitigate potential adverse impacts on ecosystems and water resources. Utilizing a box-and-whisker plot, the analysis underscores heightened uncertainty over time, with asymmetrical data distributions and skewed patterns for future periods, suggesting greater confidence in nearer-term (2021-2040) projections compared to distant future (2081-2100) estimations. The insights provided furnish crucial information for the formulation of robust policies concerning natural resource management and climate change mitigation within the Bhima sub-basin. These findings are instrumental in enlightening and directing local authorities towards the creation of a comprehensive policy framework customized to the unique characteristics of the region.